Alveolar-arterial (A-a) Oxygen Gradient

Description

Calculates the A-a O2 Gradient, a measure of the difference between the alveolar concentration of oxygen (PAO2) and the arterial concentration of oxygen (PaO2). This gradient helps differentiate between extrapulmonary causes of hypoxemia (e.g., hypoventilation, high altitude) and intrapulmonary causes (e.g., V/Q mismatch, shunt, diffusion defects).

The function utilizes the Alveolar Gas Equation: PAO2 = (FiO2 * (Patm - PH2O)) - (PaCO2 / R) Where PH2O is assumed to be 47 mmHg and R (Respiratory Quotient) is assumed to be 0.8.

Usage

a_a_gradient(pao2, paco2, fio2, age = NULL, patm = 760, elevation_m = NULL)

Arguments

Value

A list containing:

References

Mellemgaard K. The alveolar-arterial oxygen difference: its size and components in normal man. Acta Physiol Scand. 1966;67(1):10-20. doi:10.1111/j.1748-1716.1966.tb03281.x

Kanber GJ, King FW, Eshchar YR, et al. The alveolar-arterial oxygen gradient in young and elderly men during air and oxygen breathing. Am Rev Respir Dis. 1968;97(3):376-381.

Examples

# Example 1: Normal Gradient (Hypoventilation)
# 30yo on Room Air (21%), PaO2 60, PaCO2 80
a_a_gradient(pao2 = 60, paco2 = 80, fio2 = 21, age = 30)

# Example 2: Elevated Gradient (V/Q Mismatch/Shunt)
# 60yo on 40% FiO2, PaO2 80, PaCO2 40
a_a_gradient(pao2 = 80, paco2 = 40, fio2 = 0.40, age = 60)

# Example 3: High Altitude
# 25yo at 3000m elevation
a_a_gradient(pao2 = 50, paco2 = 30, fio2 = 21, age = 25, elevation_m = 3000)

AAP Pediatric Hypertension Guidelines (2017)

Description

Classifies blood pressure in children and adolescents according to the 2017 American Academy of Pediatrics (AAP) Clinical Practice Guideline.

The function handles two age groups: 1. Adolescents (>= 13 years): Uses static absolute cutoffs matching adult guidelines (Normal, Elevated, Stage 1, Stage 2). 2. Children (1 to <13 years): Uses percentile-based logic. Note: Users must provide the child-specific 90th and 95th percentile BP thresholds (based on age, sex, and height) as arguments.

Usage

aap_pediatric_htn_guidelines(age, systolic, diastolic,
                             sbp_90th = NULL, sbp_95th = NULL,
                             dbp_90th = NULL, dbp_95th = NULL)

Arguments

Value

A list containing:

References

Flynn JT, Kaelber DC, Baker-Smith CM, et al. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017;140(3):e20171904. doi:10.1542/peds.2017-1904

Examples

# Example 1: Adolescent (14 years old) - Static Cutoffs
# BP 132/82 -> Stage 1 Hypertension
aap_pediatric_htn_guidelines(age = 14, systolic = 132, diastolic = 82)

# Example 2: Child (8 years old) - Percentile Based
# Measured: 112/60 -> Stage 1 (SBP > 95th)
aap_pediatric_htn_guidelines(
  age = 8, systolic = 112, diastolic = 60,
  sbp_90th = 105, sbp_95th = 109,
  dbp_90th = 65, dbp_95th = 69
)

Abbey Pain Scale for Dementia Patients

Description

Calculates the Abbey Pain Scale score, a tool designed to assess pain in patients with advanced dementia who cannot verbalize their needs. The scale evaluates six domains: Vocalization, Facial Expression, Body Language, Behavioral Change, Physiological Change, and Physical Changes.

Usage

abbey_pain_scale(vocalization, facial_expression, body_language,
                 behavioral_change, physiological_change, physical_changes)

Arguments

Value

A list containing:

References

Abbey J, Piller N, De Bellis A, et al. The Abbey pain scale: a 1-minute numerical indicator for people with end-stage dementia. Int J Palliat Nurs. 2004;10(1):6-13. doi:10.12968/ijpn.2004.10.1.12013

Examples

# Example 1: Moderate Pain
# Mild vocalization (1), Moderate facial expression (2), Mild body language (1),
# No behavioral change (0), Mild physiological change (1), Severe physical change (3)
# Total = 8
abbey_pain_scale(1, 2, 1, 0, 1, 3)

# Example 2: No Pain
# All absent
abbey_pain_scale(0, 0, 0, 0, 0, 0)

Abbreviated Mental Test (AMT-10)

Description

Calculates the AMT-10 score, a screening tool for cognitive impairment in elderly patients. It consists of 10 questions, each scoring 1 point for a correct answer. A score of less than 8 suggests possible cognitive impairment.

Usage

abbreviated_mental_test_10(age_correct, time_correct, address_recall_correct,
                           year_correct, place_correct, recognition_correct,
                           dob_correct, ww1_date_correct, monarch_correct,
                           count_backwards_correct)

Arguments

Value

A list containing:

References

Hodkinson HM. Evaluation of a mental test score for assessment of mental impairment in the elderly. Age Ageing. 1972;1(4):233-238. doi:10.1093/ageing/1.4.233

Examples

# Example 1: Normal Cognition
# All correct
abbreviated_mental_test_10(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)

# Example 2: Possible Impairment
# Missed address recall, year, and counting backwards
abbreviated_mental_test_10(1, 1, 0, 0, 1, 1, 1, 1, 1, 0)

Abbreviated Mental Test 4 (AMT-4)

Description

Calculates the AMT-4 score, a rapid screening tool for cognitive impairment derived from the longer AMT-10. It assesses four specific domains: Age, Date of Birth, Place, and Year. A score of less than 4 indicates possible cognitive impairment.

Usage

abbreviated_mental_test_4(age_correct, dob_correct, place_correct, year_correct)

Arguments

Value

A list containing:

References

Swain DG, Nightingale PG. Evaluation of a shortened version of the Abbreviated Mental Test in a series of elderly patients. Clin Rehabil. 1997;11(3):243-248. doi:10.1177/026921559701100311

Examples

# Example 1: Normal Cognition
# All answers correct
abbreviated_mental_test_4(1, 1, 1, 1)

# Example 2: Possible Impairment
# Incorrect year
abbreviated_mental_test_4(1, 1, 1, 0)

ABC/2 Formula for Intracerebral Hemorrhage Volume

Description

Calculates the estimated volume of an intracerebral hemorrhage (ICH) using the ABC/2 formula. This simple geometric approximation correlates well with planimetric measurements and is widely used for prognosis and treatment stratification.

The formula assumes an ellipsoid shape: Volume = (A * B * C) / 2.

Usage

abc2_ich_volume(hemorrhage_length_cm, hemorrhage_width_cm, num_slices, slice_thickness_mm)

Arguments

Value

A list containing:

References

Kothari RU, Brott T, Broderick JP, et al. The ABCs of measuring intracerebral hemorrhage volumes. Stroke. 1996;27(8):1304-1305. doi:10.1161/01.str.27.8.1304

Examples

# Example 1: Large Hemorrhage
# 5cm x 4cm on largest slice, 12 slices total (all full size), 5mm thickness
# C = 12 * 0.5cm = 6cm. Volume = (5 * 4 * 6) / 2 = 60 mL
abc2_ich_volume(5, 4, 12, 5)

# Example 2: Small Hemorrhage
# 3cm x 2cm, 3 slices (5mm thick)
# C = 3 * 0.5cm = 1.5cm. Volume = (3 * 2 * 1.5) / 2 = 4.5 mL
abc2_ich_volume(3, 2, 3, 5)

Assessment of Blood Consumption (ABC) Score for Massive Transfusion

Description

Calculates the ABC Score to predict the need for massive transfusion in adult trauma patients. The score uses four non-laboratory variables available early in the trauma resuscitation to identify patients who may benefit from activation of a Massive Transfusion Protocol (MTP).

Usage

abc_massive_transfusion(penetrating_mechanism, positive_fast, sbp_mmHg, hr_bpm)

Arguments

Value

A list containing:

References

Nunez TC, Voskresensky IV, Dossett LA, et al. Early prediction of massive transfusion in trauma: simple as ABC (assessment of blood consumption)? J Trauma. 2009;66(2):346-352. doi:10.1097/TA.0b013e3181961c35

Examples

# Example 1: MTP Likely
# Penetrating trauma, Positive FAST, Hypotensive, Tachycardic
abc_massive_transfusion(1, 1, 85, 130)

# Example 2: MTP Unlikely
# Blunt trauma, Negative FAST, Stable vitals
abc_massive_transfusion(0, 0, 110, 88)

ABCD2 Score for TIA

Description

Calculates the ABCD2 Score to estimate the risk of stroke within 2, 7, and 90 days after a Transient Ischemic Attack (TIA). This score is widely used to determine the urgency of evaluation and the need for hospital admission.

Usage

abcd2_tia(age, bp_systolic, bp_diastolic, clinical_features, duration_min, diabetes)

Arguments

Value

A list containing:

References

Johnston SC, Rothwell PM, Nguyen-Huynh MN, et al. Validation and refinement of scores to predict very early stroke risk after transient ischaemic attack. Lancet. 2007;369(9558):283-92. doi:10.1016/S0140-6736(07)60150-0

Examples

# Example 1: High Risk
# 65yo, BP 150/90, Unilateral weakness, Duration 45 mins, Diabetes
# Age(1) + BP(1) + Weakness(2) + Duration(1) + Diabetes(1) = 6
abcd2_tia(65, 150, 90, 2, 45, 1)

# Example 2: Low Risk
# 45yo, BP 120/80, Speech disturbance only, Duration 5 mins, No Diabetes
# Age(0) + BP(0) + Speech(1) + Duration(0) + Diabetes(0) = 1
abcd2_tia(45, 120, 80, 1, 5, 0)

Arterial Blood Gas (ABG) Analyzer

Description

Interprets arterial blood gas values to identify acid-base disturbances. It determines the primary disorder (Respiratory vs. Metabolic, Acidosis vs. Alkalosis), assesses the degree of compensation (Acute vs. Chronic, or appropriate secondary response), and calculates the Anion Gap and Delta Ratio if electrolytes are provided.

Usage

abg_analyzer(ph, paco2, hco3, na = NULL, cl = NULL, albumin = 4.0)

Arguments

Value

A list containing:

References

Adrogué HJ, Madias NE. Management of life-threatening acid-base disorders. N Engl J Med. 1998;338(1):26-34.

Examples

# Example 1: Diabetic Ketoacidosis (High AG Met Acidosis)
# pH 7.2, pCO2 25 (Compensated), HCO3 10, Na 140, Cl 100
abg_analyzer(7.2, 25, 10, 140, 100)

# Example 2: Acute Respiratory Acidosis (Opioid Overdose)
# pH 7.25, pCO2 60, HCO3 26 (Acute/Uncompensated)
abg_analyzer(7.25, 60, 26)

# Example 3: Mixed Disorder (Septic Shock + Vomiting)
# High AG Acidosis + Met Alkalosis
# pH 7.4, pCO2 40, HCO3 24 (Normal pH/Nos), but High Gap
# Na 140, Cl 90, Alb 4.0
# AG = 140 - (90 + 24) = 26. Delta Gap = 14. Delta HCO3 = 0. Ratio > 2.0
abg_analyzer(7.4, 40, 24, 140, 90)

ABIC Score for Alcoholic Hepatitis

Description

Calculates the ABIC (Age, Bilirubin, INR, Creatinine) Score to stratify the risk of death at 90 days in patients with alcoholic hepatitis. It categorizes patients into Low, Intermediate, and High risk groups, helping to guide treatment decisions such as the use of corticosteroids.

Usage

abic_alcoholic_hepatitis(age, bilirubin, inr, creatinine,
                         bilirubin_units = "mg/dL", creatinine_units = "mg/dL")

Arguments

Value

A list containing:

References

Dominguez M, Rincon D, Abraldes JG, et al. A new scoring system for prognostic stratification of patients with alcoholic hepatitis. Am J Gastroenterol. 2008;103(11):2747-2756. doi:10.1111/j.1572-0241.2008.02104.x

Examples

# Example 1: High Risk
# 50yo, Bili 15 mg/dL, INR 2.0, Cr 1.5 mg/dL
# Score = (50*0.1) + (15*0.08) + (1.5*0.3) + (2.0*0.8) = 5 + 1.2 + 0.45 + 1.6 = 8.25 (Intermediate)
abic_alcoholic_hepatitis(50, 15, 2.0, 1.5)

# Example 2: Low Risk
# 40yo, Bili 3 mg/dL, INR 1.1, Cr 0.8 mg/dL
abic_alcoholic_hepatitis(40, 3, 1.1, 0.8)

Absolute Lymphocyte Count (ALC)

Description

Calculates the Absolute Lymphocyte Count (ALC) based on the total White Blood Cell (WBC) count and the percentage of lymphocytes from a differential blood count. ALC is a useful marker for immune function and is used in the monitoring of HIV/AIDS, post-transplant patients, and chemotherapy (e.g., assessing susceptibility to opportunistic infections).

Usage

absolute_lymphocyte_count(wbc, lymphs_percent)

Arguments

Value

A list containing:

References

Al-Gwaiz LA, Babay HH. The diagnostic value of absolute lymphocyte count in typhoid fever. J Infect Dev Ctries. 2007;1(3):260-265.

Examples

# Example 1: Normal
# WBC 6000 cells/uL, 30% Lymphs
absolute_lymphocyte_count(6000, 30)

# Example 2: Lymphopenia (using k/uL input)
# WBC 3.5 k/uL, 15% Lymphs
# Result will be 0.525 k/uL (which is 525 cells/uL -> Lymphopenia)
absolute_lymphocyte_count(3.5, 15)

Absolute Neutrophil Count (ANC)

Description

Calculates the Absolute Neutrophil Count (ANC), a measure of the number of neutrophil granulocytes (including segmented neutrophils and bands) present in the blood. ANC is critical for assessing the risk of infection in patients undergoing chemotherapy or those with other immune system suppressions.

Usage

absolute_neutrophil_count(wbc, neutrophils_percent, bands_percent = 0)

Arguments

Value

A list containing:

References

Al-Gwaiz LA, Babay HH. The diagnostic value of absolute lymphocyte count in typhoid fever. J Infect Dev Ctries. 2007;1(3):260-265. (General hematology reference).

Examples

# Example 1: Severe Neutropenia (Chemotherapy patient)
# WBC 1.2 k/uL, 20% Segs, 5% Bands
# ANC = 1.2 * (0.20 + 0.05) = 0.3 k/uL (300 cells/uL)
absolute_neutrophil_count(1.2, 20, 5)

# Example 2: Normal
# WBC 6000 cells/uL, 50% Segs, 0% Bands
absolute_neutrophil_count(6000, 50)

Abuse Assessment Screen (AAS)

Description

Implements the Abuse Assessment Screen (AAS), a 5-item screening tool designed to detect domestic abuse (intimate partner violence) in pregnant and non-pregnant women in healthcare settings. It assesses emotional, physical, and sexual abuse, as well as fear of a partner.

Usage

abuse_assessment_screen(ever_abused, past_year_physically_hurt,
                        forced_sexual_activities, afraid_of_partner,
                        pregnant_and_hurt = 0)

Arguments

Value

A list containing:

References

McFarlane J, Parker B, Soeken K, Bullock L. Assessing for abuse during pregnancy. Severity and frequency of injuries and associated entry into prenatal care. JAMA. 1992;267(23):3176-3178.

Examples

# Example 1: Positive Screen
# Patient reports being afraid of partner
abuse_assessment_screen(0, 0, 0, 1, 0)

# Example 2: Negative Screen
# No affirmative answers
abuse_assessment_screen(0, 0, 0, 0, 0)

ACC/AHA Heart Failure Staging

Description

Determines the stage of Heart Failure (A, B, C, or D) based on the American College of Cardiology (ACC) and American Heart Association (AHA) guidelines. This classification emphasizes the development and progression of the disease, from patients at risk to those with refractory symptoms.

Usage

acc_aha_hf_staging(risk_factors, structural_disease, hf_symptoms, refractory_hf)

Arguments

Value

A list containing:

References

Yancy CW, Jessup M, Bozkurt B, et al. 2013 ACCF/AHA Guideline for the Management of Heart Failure. Circulation. 2013;128(16):e240-e327. doi:10.1161/CIR.0b013e31829e8776

Examples

# Example 1: Stage C (Symptomatic)
# Patient with structural disease and dyspnea
acc_aha_hf_staging(1, 1, 1, 0)

# Example 2: Stage A (At Risk)
# Hypertension only, no structural disease or symptoms
acc_aha_hf_staging(1, 0, 0, 0)

Adverse Childhood Experiences (ACE) Score

Description

Calculates the Adverse Childhood Experiences (ACE) Score based on the 10-item survey from the CDC-Kaiser Permanente ACE Study. The score sums the number of categories of adverse experiences reported during the first 18 years of life. Higher scores are strongly correlated with negative health and behavioral outcomes later in life.

Usage

ace_score(emotional_abuse, physical_abuse, sexual_abuse, emotional_neglect,
          physical_neglect, divorce_separation, domestic_violence,
          substance_abuse, mental_illness, incarcerated_relative)

Arguments

Value

A list containing:

References

Felitti VJ, Anda RF, Nordenberg D, et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study. Am J Prev Med. 1998;14(4):245-258. doi:10.1016/s0749-3797(98)00017-8

Examples

# Example 1: High Risk
# Divorce, Substance Abuse, Mental Illness, Incarceration
ace_score(0, 0, 0, 0, 0, 1, 0, 1, 1, 1)

# Example 2: Low Risk
# No adverse experiences reported
ace_score(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)

ACEF II Risk Score for Cardiac Surgery

Description

Calculates the ACEF II (Age, Creatinine, Ejection Fraction II) Risk Score to predict 30-day operative mortality in patients undergoing elective or emergency cardiac surgery. This updated version of the original ACEF score incorporates emergency status and preoperative anemia (hematocrit).

Usage

acef_ii_cardiac_surgery(age, ejection_fraction, creatinine_mg_dl,
                        hematocrit, emergency_surgery)

Arguments

Value

A list containing:

References

Ranucci M, Castelvecchio S, Menicanti L, et al. The ACEF II Risk Score for cardiac surgery: updated but still parsimonious. Eur Heart J. 2018;39(23):2183-2189. doi:10.1093/eurheartj/ehx039

Examples

# Example 1: Low Risk
# 65yo, EF 55%, Cr 1.0, Hct 40%, Elective
# Score = 65/55 = 1.18
acef_ii_cardiac_surgery(65, 55, 1.0, 40, 0)

# Example 2: High Risk
# 75yo, EF 30%, Cr 2.5, Hct 30%, Emergency
# Score = (75/30) + 2.0 + 3.0 + (0.2 * (36-30))
#       = 2.5 + 2.0 + 3.0 + 1.2 = 8.7
acef_ii_cardiac_surgery(75, 30, 2.5, 30, 1)

ACEP ED COVID-19 Management Tool (Severity Classification)

Description

Classifies the severity of COVID-19 infection in adult patients presenting to the Emergency Department based on the American College of Emergency Physicians (ACEP) COVID-19 Management Tool guidelines. The classification (Mild, Moderate, Severe, Critical) is used to guide diagnostic testing, disposition, and treatment decisions.

Usage

acep_covid19_management(spo2, resp_rate, signs_of_shock_mods = FALSE,
                        resp_failure = FALSE, lower_resp_disease = FALSE,
                        infiltrates_gt_50 = FALSE, pao2_fio2 = NULL,
                        symptoms_present = TRUE)

Arguments

Value

A list containing:

References

American College of Emergency Physicians (ACEP). ACEP COVID-19 Field Guide: Emergency Department COVID-19 Management Tool. 2023 Update.

Examples

# Example 1: Severe Illness
# SpO2 92%, RR 22, Positive Infiltrates (but not >50%)
# Severity driven by Hypoxia (SpO2 < 94%)
acep_covid19_management(spo2 = 92, resp_rate = 22, lower_resp_disease = TRUE)

# Example 2: Mild Illness
# SpO2 98%, RR 16, Symptoms present, No lower respiratory disease
acep_covid19_management(spo2 = 98, resp_rate = 16, lower_resp_disease = FALSE)

# Example 3: Critical Illness
# Shock present
acep_covid19_management(spo2 = 88, resp_rate = 35, signs_of_shock_mods = TRUE)

Acetaminophen Overdose NAC Dosing (Rumack-Matthew Nomogram)

Description

Determines the need for N-acetylcysteine (NAC) treatment following a single acute acetaminophen ingestion. The function utilizes the Rumack-Matthew Nomogram logic, specifically the "Treatment Line" (starting at 150 mcg/mL at 4 hours), to interpret serum acetaminophen levels obtained between 4 and 24 hours post-ingestion.

Usage

acetaminophen_nomogram(time_ingestion_hrs, acetaminophen_level, units = "mcg/mL")

Arguments

Value

A list containing:

References

Rumack BH, Matthew H. Acetaminophen poisoning and toxicity. Pediatrics. 1975;55(6):871-876.

Examples

# Example 1: Treatment Needed
# 8 hours post-ingestion, Level 100 mcg/mL
# Threshold at 8 hours is 75 mcg/mL -> Above line
acetaminophen_nomogram(8, 100, "mcg/mL")

# Example 2: No Treatment
# 5 hours post-ingestion, Level 100 mcg/mL
# Threshold at 5 hours is ~126 mcg/mL -> Below line
acetaminophen_nomogram(5, 100, "mcg/mL")

# Example 3: Different Units
# 4 hours post, Level 20 mg/dL -> Converts to 200 mcg/mL (Above 150 threshold)
acetaminophen_nomogram(4, 20, "mg/dL")

2015 ACR/EULAR Gout Classification Criteria

Description

Calculates the score for the 2015 ACR/EULAR Gout Classification Criteria. These criteria apply to patients with at least one episode of peripheral joint/bursa swelling, pain, or tenderness (Entry Criterion). The presence of MSU crystals is sufficient for diagnosis. If MSU crystals are not confirmed, a scoring system based on clinical, laboratory, and imaging features is used.

Usage

acr_eular_gout_2015(entry_criteria, sufficient_criteria, joint_pattern,
                    symptom_characteristics, episode_time_course, clinical_tophus,
                    serum_urate, synovial_fluid_negative, imaging_urate,
                    imaging_erosion)

Arguments

Value

A list containing:

References

Neogi T, Jansen TL, Dalbeth N, et al. 2015 Gout classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis. 2015;74(10):1789-1798. doi:10.1136/annrheumdis-2015-208237

Examples

# Example 1: Classic Gout Presentation
# 1st MTP (2), 3 Characteristics (3), Recurrent episodes (2), Urate 9.0 (3)
# Score = 2 + 3 + 2 + 3 = 10 -> Classified as Gout
acr_eular_gout_2015(1, 0, 2, 3, 2, 0, 9.0, 0, 0, 0)

# Example 2: Negative Workup
# Ankle (1), 1 Characteristic (1), One episode (1), Urate 5.0 (0)
# Score = 1 + 1 + 1 + 0 = 3 -> Not Classified
acr_eular_gout_2015(1, 0, 1, 1, 1, 0, 5.0, 0, 0, 0)

ACR/EULAR 2010 Rheumatoid Arthritis Classification Criteria

Description

Calculates the score for the 2010 American College of Rheumatology (ACR) / European League Against Rheumatism (EULAR) Classification Criteria for Rheumatoid Arthritis (RA). A score of 6 or greater (out of 10) classifies a patient as having definite RA.

This criteria set is intended for use in patients who have at least 1 joint with definite clinical synovitis (swelling) and with the synovitis not better explained by another disease.

Usage

acr_eular_ra_2010(joint_involvement, serology, acute_phase_reactants, duration_symptoms)

Arguments

Value

A list containing:

References

Aletaha D, Neogi T, Silman AJ, et al. 2010 Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis Rheum. 2010;62(9):2569-2581. doi:10.1002/art.27584

Examples

# Example 1: Definite RA
# 4-10 small joints (3 pts), High-positive RF (3 pts), Normal CRP (0 pts), <6 weeks (0 pts)
# Total = 6
acr_eular_ra_2010(3, 2, 0, 0)

# Example 2: Not Classified
# 1-3 small joints (2 pts), Negative Serology (0 pts), Abnormal CRP (1 pt), >=6 weeks (1 pt)
# Total = 4
acr_eular_ra_2010(2, 0, 1, 1)

ACTION ICU Score for NSTEMI

Description

Calculates the ACTION ICU Score to predict the risk of in-hospital complications (cardiac arrest, shock, high-grade AV block, respiratory failure, stroke, or death) in patients with Non-ST-Elevation Myocardial Infarction (NSTEMI). This tool helps triage patients who may benefit from Intensive Care Unit (ICU) admission versus those who can be safely managed in a non-ICU setting (e.g., step-down or telemetry unit).

Usage

action_icu_nstemi(age, creatinine, heart_rate, sbp, troponin_x_uln,
                  heart_failure, st_depression, prior_revasc, chronic_lung_disease)

Arguments

Value

A list containing:

References

Fanari Z, et al. Risk Score to Predict Need for Intensive Care in Initially Hemodynamically Stable Adults With Non-ST-Segment-Elevation Myocardial Infarction. J Am Heart Assoc. 2018;7(11):e008894. doi:10.1161/JAHA.118.008894

Examples

# Example 1: High Risk Candidate for ICU
# 72yo (+1), Cr 1.5 (+1), HR 110 (+3), SBP 110 (+3), HF present (+5), No prior revasc (+1)
# Score = 14 -> High Risk
action_icu_nstemi(72, 1.5, 110, 110, 5, 1, 0, 0, 0)

# Example 2: Low Risk (Floor Candidate)
# 60yo (0), Cr 0.9 (0), HR 70 (0), SBP 150 (0), Trop 2x (0), No HF (0), Prior Revasc (0)
# Score = 0
action_icu_nstemi(60, 0.9, 70, 150, 2, 0, 0, 1, 0)

Acute Gout Diagnosis Rule (Janssens Rule)

Description

Calculates the Acute Gout Diagnosis Rule score to estimate the probability of gout in primary care patients presenting with acute arthritis. This rule is particularly useful when synovial fluid analysis is not immediately available. It categorizes patients into Low, Intermediate, or High probability groups based on clinical and laboratory features.

Usage

acute_gout_diagnosis(sex, prev_attack, onset_1day, joint_redness,
                     mtp1_involvement, htn_cvd, uric_acid_elevated)

Arguments

Value

A list containing:

References

Janssens HJ, Fransen J, van de Lisdonk EH, et al. A diagnostic rule for acute gouty arthritis in primary care without joint fluid analysis. Arch Intern Med. 2010;170(13):1120-1126. doi:10.1001/archinternmed.2010.196

Examples

# Example 1: High Probability
# Male, Previous Attack, Rapid Onset, MTP1, HTN, High Uric Acid
# Score = 2 + 2 + 0.5 + 2.5 + 1.5 + 3.5 = 12
acute_gout_diagnosis("male", 1, 1, 0, 1, 1, 1)

# Example 2: Low Probability
# Female, Rapid Onset, Redness, No other features
# Score = 0.5 + 1 = 1.5
acute_gout_diagnosis("female", 0, 1, 1, 0, 0, 0)

Acute Pancreatitis Prediction Model

Description

Calculates a prediction score for the likelihood of acute pancreatitis in patients presenting with elevated lipase. A score of 6 or greater suggests a high probability of acute pancreatitis, helping to differentiate it from other causes of hyperlipasemia.

Usage

acute_pancreatitis_prediction_model(prior_episodes, cholelithiasis, recent_surgery,
                                    epigastric_pain, worsening_severity,
                                    pain_duration_days, pain_severity_score,
                                    lipase_uln_multiplier)

Arguments

Value

A list containing:

References

Jang DK, et al. Development and Validation of a Prediction Model for Acute Pancreatitis in Patients with Elevated Serum Lipase. Sci Rep. 2019.

Examples

# Example 1: High Probability
# No prior eps, Stones present (+2), No surgery, Epigastric pain (+2),
# Worsening (+1), 2 days duration (+1), Severe pain (+3), Lipase 25x (+2)
# Score = 2 + 2 + 1 + 1 + 3 + 2 = 11
acute_pancreatitis_prediction_model(0, 1, 0, 1, 1, 2, 8, 25)

# Example 2: Low Risk
# 1 prior ep (0), No stones, Recent surgery (-2), No epigastric pain,
# Not worsening, 10 days duration (0), Mild pain (0), Lipase 5x (0)
# Score = -2
acute_pancreatitis_prediction_model(1, 0, 1, 0, 0, 10, 2, 5)

American Diabetes Association (ADA) Risk Calculator

Description

Calculates the risk score for Type 2 Diabetes using the American Diabetes Association (ADA) Risk Test. This tool evaluates risk factors including age, sex, family history, hypertension, physical activity, and BMI to determine if a patient is at high risk and should be screened for diabetes.

Usage

ada_diabetes_risk(age, sex, family_history, high_bp, physically_active,
                  height_in, weight_lbs, history_gestational_dm = 0)

Arguments

Value

A list containing:

References

Bang H, Edwards AM, Bomback AS, et al. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med. 2009;151(11):775-783. doi:10.7326/0003-4819-151-11-200912010-00005

Examples

# Example 1: High Risk
# 55yo Male, Family Hx, High BP, Inactive, Overweight (BMI ~27)
# Age(2) + Sex(1) + Fam(1) + BP(1) + Inactive(1) + BMI(1) = 7
ada_diabetes_risk(55, "male", 1, 1, 0, 70, 190, 0)

# Example 2: Low Risk
# 35yo Female, Active, Normal Weight, No other factors
# Age(0) + Sex(0) + Fam(0) + BP(0) + Active(0) + BMI(0) = 0
ada_diabetes_risk(35, "female", 0, 0, 1, 65, 130, 0)

ADAPT Protocol for Cardiac Event Risk

Description

Implements the 2-Hour Accelerated Diagnostic Protocol to Assess Patients with Chest Pain Symptoms (ADAPT). This protocol identifies low-risk patients who can be safely discharged from the Emergency Department within 2 hours.

To be classified as "Low Risk" (safe for discharge), a patient must meet ALL of the following criteria: 1. TIMI Score = 0. 2. No new ischemic changes on ECG. 3. Negative Troponin at 0 hours. 4. Negative Troponin at 2 hours.

Usage

adapt_cardiac_risk(timi_score, ekg_new_ischemia,
troponin_0h_positive, troponin_2h_positive)

Arguments

Value

A list containing:

References

Than M, Cullen L, Aldous S, et al. 2-Hour Accelerated Diagnostic Protocol to Assess Patients With Chest Pain Symptoms Using Contemporary Troponins as the Only Biomarker: The ADAPT Trial. J Am Coll Cardiol. 2012;59(23):2091-2098. doi:10.1016/j.jacc.2012.02.035

Examples

# Example 1: Low Risk (Safe for discharge)
# TIMI 0, No EKG changes, Negative Troponins x2
adapt_cardiac_risk(0, 0, 0, 0)

# Example 2: Not Low Risk
# TIMI 1, No EKG changes, Negative Troponins x2
adapt_cardiac_risk(1, 0, 0, 0)

# Example 3: Not Low Risk
# TIMI 0, No EKG changes, Positive 2h Troponin
adapt_cardiac_risk(0, 0, 0, 1)

ADHERE Algorithm for Acute Decompensated Heart Failure

Description

Implements the classification regression tree (CART) analysis from the Acute Decompensated Heart Failure National Registry (ADHERE). This algorithm uses admission Blood Urea Nitrogen (BUN), Systolic Blood Pressure (SBP), and Serum Creatinine to stratify patients into four risk groups for in-hospital mortality.

Usage

adhere_algorithm(bun, sbp, creatinine)

Arguments

Value

A list containing:

References

Fonarow GC, Adams KF Jr, Abraham WT, et al. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis. JAMA. 2005;293(5):572-580. doi:10.1001/jama.293.5.572

Examples

# Example 1: Low Risk (BUN < 43)
adhere_algorithm(bun = 30, sbp = 120, creatinine = 1.0)

# Example 2: Very High Risk (BUN >= 43, SBP < 115, Cr >= 2.75)
adhere_algorithm(bun = 50, sbp = 100, creatinine = 3.0)

Adult Self-Report Scale (ASRS-v1.1) for ADHD

Description

Calculates the score for Part A of the World Health Organization (WHO) Adult Self-Report Scale (ASRS-v1.1) Screener for ADHD. This 6-item tool screens for Attention-Deficit/Hyperactivity Disorder in adults.

Usage

adult_adhd_self_report_scale(q1_trouble_wrapping_up,
q2_difficulty_getting_organized,
q3_problems_remembering_appointments,
q4_procrastinate_difficult_tasks,
q5_fidgety, q6_overly_active)

Arguments

Details

Scoring Key: 0 = Never 1 = Rarely 2 = Sometimes 3 = Often 4 = Very Often

Thresholds: Questions 1-3 are considered positive if the response is "Sometimes", "Often", or "Very Often" (Score >= 2). Questions 4-6 are considered positive if the response is "Often" or "Very Often" (Score >= 3).

Value

A list containing:

References

Kessler RC, Adler L, Ames M, et al. The World Health Organization Adult ADHD Self-Report Scale (ASRS): a short screening scale for use in the general population. Psychol Med. 2005;35(2):245-56.

Examples

# Example 1: Positive Screen
# Q1-Q3: Often (3), Q4-Q6: Often (3) -> All positive
adult_adhd_self_report_scale(3, 3, 3, 3, 3, 3)

# Example 2: Negative Screen
# All responses "Rarely" (1)
adult_adhd_self_report_scale(1, 1, 1, 1, 1, 1)

Age-Adjusted D-dimer for VTE

Description

Applies an age-adjusted cutoff to D-dimer interpretation for the exclusion of Venous Thromboembolism (VTE), including Pulmonary Embolism (PE) and Deep Vein Thrombosis (DVT). In patients > 50 years old, the cutoff is adjusted to (Age * 10) ng/mL, increasing specificity without compromising sensitivity.

Usage

age_adjusted_d_dimer(age, d_dimer, d_dimer_units = "ng/mL")

Arguments

Value

A list containing:

References

Righini M, Van Es J, Den Exter PL, et al. Age-adjusted D-dimer cutoff levels to rule out pulmonary embolism: the ADJUST-PE study. JAMA. 2014;311(11):1117-1124. doi:10.1001/jama.2014.2135

Examples

# Example 1: 65-year-old with D-dimer 600 ng/mL
# Standard cutoff (500) would be positive.
# Age-adjusted cutoff (65 * 10 = 650) is higher.
# Result: Negative (600 < 650) -> PE Ruled Out.
age_adjusted_d_dimer(65, 600)

# Example 2: 40-year-old with D-dimer 600 ng/mL
# Age <= 50, so cutoff remains 500.
# Result: Positive (600 > 500).
age_adjusted_d_dimer(40, 600)

# Example 3: 70-year-old with D-dimer 0.8 ug/mL
# Converts to 800 ng/mL. Cutoff is 700.
# Result: Positive (800 > 700).
age_adjusted_d_dimer(70, 0.8, "ug/mL")

Age-Adjusted ESR and CRP

Description

Calculates the upper limit of normal for Erythrocyte Sedimentation Rate (ESR) and C-Reactive Protein (CRP) based on patient age and sex. Standard reference ranges often misclassify elderly patients as having elevated inflammatory markers due to the natural physiological rise of these markers with age. These formulas provide a more accurate cutoff for inflammation.

Usage

age_adjusted_esr_crp(age, sex, esr = NULL, crp = NULL)

Arguments

Value

A list containing:

References

Miller A, Green M, Robinson D. Simple rule for calculating normal erythrocyte sedimentation rate. Br Med J (Clin Res Ed). 1983;286(6361):266.

Wener MH, Daum PR, McQuillan GM. The influence of age, sex, and race on the upper reference limit of serum C-reactive protein concentration. J Rheumatol. 2000;27(10):2351-2359.

Examples

# Example 1: 70-year-old Female
# ESR Limit = (70 + 10) / 2 = 40
# CRP Limit = (70 / 50) + 0.6 = 1.4 + 0.6 = 2.0
age_adjusted_esr_crp(70, "female")

# Example 2: Evaluation of specific labs
# 60-year-old Male with ESR 25 and CRP 1.5
# ESR Limit = 30 (Normal); CRP Limit = 1.2 (Elevated)
age_adjusted_esr_crp(60, "male", esr = 25, crp = 1.5)

Calculate Stroke Risk for General Population based off AHA Patient Stroke risk calculator

Description

Using common patient history of comorbidities and conditions, in addition to traditional lifestyle factors, assess the total risk of suffering a stroke. Developed by the AHA and revised in 2023 for patient use, a positive score is associated with a high likelihood of suffering a stroke while a negative score has a low likelihood. Has not been validated for clinical use.

Usage

aha_stroke(bp, afib, sugar, bmi, diet, chol, dm, physical, hx, smoke)

Arguments

Value

A numeric value/vector with predicted stroke risk score.

References

“Stroke Risk Assessment.” www.stroke.org, December 14, 2023. https://www.stroke.org/en/about-stroke/stroke-risk-factors/stroke-risk-assessment.

Examples

# The function is defined as
aha_stroke(1, 0, 1, 1, 0, 0, 0, 0, 1, 0)

AIMS65 Score for Upper GI Bleeding Mortality

Description

Calculates the AIMS65 score to predict in-hospital mortality in patients with acute upper gastrointestinal bleeding (UGIB). The acronym stands for Albumin, INR, Mental status, Systolic blood pressure, and age 65.

Usage

aims65_upper_gi_bleed(albumin_g_dl, inr, altered_mental_status, sbp_mmhg, age_years)

Arguments

Value

A list containing:

References

Saltzman JR, Tabak YP, Hyett BH, Sun X, Travis AC, Johannes RS. A simple risk score accurately predicts in-hospital mortality, length of stay, and cost in acute upper GI bleeding. Gastrointest Endosc. 2011;74(6):1215-1224. doi:10.1016/j.gie.2011.06.024

Examples

# Example 1: High Risk
# Albumin 2.5 (+1), INR 2.0 (+1), Alert (0), SBP 85 (+1), Age 70 (+1)
# Score = 4
aims65_upper_gi_bleed(2.5, 2.0, 0, 85, 70)

# Example 2: Low Risk
# Albumin 4.0 (0), INR 1.0 (0), Alert (0), SBP 120 (0), Age 40 (0)
# Score = 0
aims65_upper_gi_bleed(4.0, 1.0, 0, 120, 40)

Abnormal Involuntary Movement Scale (AIMS)

Description

Calculates the total score for the first 7 items of the AIMS to screen for Tardive Dyskinesia (TD) in patients receiving neuroleptic therapy. It also provides a diagnostic interpretation based on the Schooler-Kane criteria.

Usage

aims_tardive_dyskinesia(facial, lips, jaw, tongue, upper_ext, lower_ext, trunk)

Arguments

Details

Scoring Key: 0 = None 1 = Minimal (may be extreme normal) 2 = Mild 3 = Moderate 4 = Severe

Value

A list containing:

References

Guy W. ECDEU Assessment Manual for Psychopharmacology. Rockville, MD: US Department of Health, Education, and Welfare Public Health Service Alcohol, Drug Abuse, and Mental Health Administration. 1976.

Examples

# Example 1: Positive for TD
# Moderate Tongue (3) movement, others minimal
aims_tardive_dyskinesia(1, 1, 1, 3, 1, 0, 0)

# Example 2: Negative for TD
# Mild Facial (2) movement only
aims_tardive_dyskinesia(2, 0, 0, 0, 0, 0, 0)

Acute Interstitial Nephritis (AIN) Risk Calculator

Description

Calculates the probability of biopsy-proven Acute Interstitial Nephritis (AIN) in patients with acute kidney injury. This model uses variables from the electronic health record (Serum Creatinine, BUN, Urine Specific Gravity, and Urine Protein) to differentiate AIN from other causes of AKI (e.g., Acute Tubular Necrosis). The formula is derived from Moledina et al. and includes an adjustment for local disease prevalence.

Usage

ain_risk_calculator(creatinine, bun, urine_specific_gravity,
                    urine_protein_high, local_ain_prevalence = 0.23)

Arguments

Value

A list containing:

References

Moledina DG, Wilson FP, Puthumana J, et al. Development and External Validation of a Diagnostic Model for Biopsy-Proven Acute Interstitial Nephritis Using Electronic Health Record Data. J Clin Invest. 2019;133(13):e168950.

Examples

# Example 1: High Risk AIN
# High Cr, Low BUN/Cr ratio, Low SG, Low Protein
ain_risk_calculator(4.0, 40, 1.010, 0)

# Example 2: Low Risk AIN (Likely ATN/Glomerular)
# Lower Cr, High Ratio, High SG, High Protein
ain_risk_calculator(1.5, 30, 1.020, 1)

Appendicitis Inflammatory Response (AIR) Score

Description

Calculates the AIR Score to predict the likelihood of acute appendicitis. Designed to outperform the Alvarado score, the AIR score incorporates objective inflammatory markers (CRP and PMN proportion) alongside clinical signs to stratify patients into low, intermediate, or high probability groups.

Usage

air_appendicitis(vomiting, rlq_pain, rebound_tenderness, temp_c,
                 wbc_count, pmn_percent, crp_mg_l)

Arguments

Value

A list containing:

References

Andersson M, Andersson RE. The appendicitis inflammatory response score: a tool for the diagnosis of acute appendicitis that outperforms the Alvarado score. World J Surg. 2008;32(8):1843-1849. doi:10.1007/s00268-008-9649-y

Examples

# Example 1: High Probability
# Vomiting, RLQ pain, Strong Rebound (3), Temp 39 (1), WBC 16 (2), PMN 90% (2), CRP 60 (2)
# Score = 1 + 1 + 3 + 1 + 2 + 2 + 2 = 12
air_appendicitis(1, 1, 3, 39, 16, 90, 60)

# Example 2: Low Probability
# RLQ pain only, Normal labs
# Score = 1
air_appendicitis(0, 1, 0, 37, 8, 60, 5)

Asthma Impairment and Risk Questionnaire (AIRQ)

Description

Calculates the AIRQ score, a 10-item, yes/no questionnaire designed to assess asthma control in patients aged 12 years and older. It evaluates both current impairment (past 2 weeks) and future risk (past 12 months). The total score stratifies patients into Well-controlled, Not well-controlled, or Very poorly controlled categories.

Usage

airq_asthma_score(symptoms_gt_4days, awakening_gt_1time, activity_limited_daily,
                  rescue_inhaler_daily, social_limited, exercise_limited,
                  difficult_control, oral_steroids_12mo, er_visit_12mo,
                  hospital_overnight_12mo)

Arguments

Value

A list containing:

References

Murphy KR, et al. Development of the Asthma Impairment and Risk Questionnaire (AIRQ): A Composite Control Measure. J Allergy Clin Immunol Pract. 2020;8(7):2263-2274.e5. doi:10.1016/j.jaip.2020.02.042

Examples

# Example 1: Very Poorly Controlled
# Symptoms >4 days, Waking >1, Exercise limited, Felt difficult to control, Steroids used
# Score = 5
airq_asthma_score(1, 1, 0, 0, 0, 1, 1, 1, 0, 0)

# Example 2: Well-Controlled
# No symptoms or history of exacerbations
# Score = 0
airq_asthma_score(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)

Abbreviated Injury Score (AIS) for Inhalation Injury

Description

Retrieves the classification and estimated mortality risk for the Abbreviated Injury Score (AIS) for Inhalation Injury. This scoring system grades the severity of inhalation injury based on bronchoscopic findings, ranging from Grade 0 (No injury) to Grade 4 (Massive injury).

Usage

ais_inhalation(grade)

Arguments

Value

A list containing:

References

Endorf FW, Gamelli RL. Inhalation injury, pulmonary complications, and mortality. J Burn Care Res. 2007;28(1):80-83. doi:10.1097/BCR.0B013E31802C889F

Examples

# Example 1: Moderate Injury
# Patient with moderate erythema and carbonaceous deposits
ais_inhalation(2)

# Example 2: Massive Injury
# Patient with mucosal sloughing and necrosis
ais_inhalation(4)

AKIN Classification for Acute Kidney Injury (AKI)

Description

Classifies the severity of Acute Kidney Injury (AKI) based on the Acute Kidney Injury Network (AKIN) criteria. The staging is determined by the worst of two criteria: serum creatinine changes (relative to baseline) or urine output duration.

Usage

akin_aki_staging(baseline_creatinine, max_creatinine,
                 urine_output_stage = 0, rrt_initiated = 0)

Arguments

Value

A list containing:

References

Mehta RL, Kellum JA, Shah SV, et al. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007;11(2):R31. doi:10.1186/cc5713

Examples

# Example 1: Stage 1 (Creatinine rise >= 0.3 mg/dL)
# Base 1.0, Max 1.3, Normal UO
akin_aki_staging(1.0, 1.3, 0, 0)

# Example 2: Stage 3 (Urine Output criteria)
# Base 1.0, Max 1.2 (No AKI by Cr), UO < 0.3 for 24h (Stage 3)
akin_aki_staging(1.0, 1.2, 3, 0)

Alberta Stroke Program Early CT Score (ASPECTS)

Description

Calculates the ASPECTS score to quantitate the severity of early ischemic changes on non-contrast CT scans in patients with acute ischemic stroke of the anterior circulation. The score starts at 10 and 1 point is subtracted for every region showing early ischemic changes. Lower scores indicate larger infarct volumes and poorer outcomes.

Usage

alberta_stroke_aspects(caudate, lentiform, internal_capsule, insular_cortex,
                       m1, m2, m3, m4, m5, m6)

Arguments

Value

A list containing:

References

Barber PA, Demchuk AM, Zhang J, Buchan AM. Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. ASPECTS Study Group. Alberta Stroke Programme Early CT Score. Lancet. 2000;355(9216):1670-1674. doi:10.1016/s0140-6736(00)02237-6

Examples

# Example 1: Large Infarct (Score 6)
# Caudate, Lentiform, IC, and Insula involved
alberta_stroke_aspects(1, 1, 1, 1, 0, 0, 0, 0, 0, 0)

# Example 2: Normal CT (Score 10)
# No ischemic changes
alberta_stroke_aspects(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)

Calculate ALBI (Albumin-Bilirubin) Grade for HCC (Hepatocellular Carcinoma)

Description

Using non-SI/SI measurements of serum bilirubin and serum albumin, calculate the ALBI grade for patients with HCC. Developed by Johnson et al. 2015, an ALBI score of =<-2.60 is grade 1 with a median survival of 18.5-85.6 months, an ALBI score of -2.60 to (and including) -1.39 is grade 2 with a median survival of 5.3-46.5 months, and an ALBI score of >-1.39 is grade 3 with a median survival of 2.3-15.5 months. Validated by Hiroaka et al. 2017 and Chen et al. 2017.

Usage

albi(sbill, salb, units = "")

Arguments

Value

A numeric value/vector with predicted ALBI score.

References

Johnson PJ, Berhane S, Kagebayashi C, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol. 2015;33(6):550-558. doi:10.1200/JCO.2014.57.9151 Hiraoka A, Michitaka K, Kumada T, et al. Validation and Potential of Albumin-Bilirubin Grade and Prognostication in a Nationwide Survey of 46,681 Hepatocellular Carcinoma Patients in Japan: The Need for a More Detailed Evaluation of Hepatic Function. Liver Cancer. 2017;6(4):325-336. doi:10.1159/000479984 Chen B, Lin S. Albumin-bilirubin (ALBI) score at admission predicts possible outcomes in patients with acute-on-chronic liver failure. Medicine (Baltimore). 2017;96(24):e7142. doi:10.1097/MD.0000000000007142

Examples

# For non-SI measurements, the function is defined as
albi(1,3.7)

# For SI measurements, the function is defined as
albi(31,37, units = "SI")

Modified Aldrete Score for PACU Discharge

Description

Calculates the Modified Aldrete Score, a tool used to assess a patient's recovery from anesthesia and determine readiness for discharge from the Post-Anesthesia Care Unit (PACU). The modified version replaces the original "Color" component with "Oxygen Saturation".

Usage

aldrete_score(activity, respiration, circulation, consciousness, oxygenation)

Arguments

Value

A list containing:

References

Aldrete JA. The post-anesthesia recovery score revisited. J Clin Anesth. 1995;7(1):89-91. doi:10.1016/0952-8180(94)00001-s

Examples

# Example 1: Ready for Discharge
# Moves 4 limbs (2), Deep breathing (2), BP within 20 (2), Awake (2), SpO2 >92% RA (2)
aldrete_score(2, 2, 2, 2, 2)

# Example 2: Not Ready
# Moves 2 limbs (1), Dyspneic (1), BP within 20 (2), Arousable (1), SpO2 >90% on O2 (1)
# Total = 6
aldrete_score(1, 1, 2, 1, 1)

Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R)

Description

Calculates the ALSFRS-R score, a validated instrument for monitoring the progression of disability in patients with Amyotrophic Lateral Sclerosis (ALS). It evaluates four domains: Bulbar, Fine Motor, Gross Motor, and Respiratory function. Each of the 12 items is scored from 0 (Complete Loss of Function) to 4 (Normal Function), yielding a total score between 0 and 48.

Usage

alsfrs_r_score(speech, salivation, swallowing, handwriting, cutting_food,
               dressing_hygiene, turning_in_bed, walking, climbing_stairs,
               dyspnea, orthopnea, respiratory_insufficiency)

Arguments

Value

A list containing:

References

Cedarbaum JM, Stambler N, Malta E, et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J Neurol Sci. 1999;169(1-2):13-21. doi:10.1016/s0022-510x(99)00210-5

Examples

# Example 1: Mild Disease
# All normal except slight speech (3) and handwriting (3)
alsfrs_r_score(3, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 4)

# Example 2: Advanced Disease
# Bulbar and Respiratory impairment
alsfrs_r_score(1, 2, 1, 0, 0, 0, 1, 0, 0, 2, 1, 0)

ALT-70 Score for Cellulitis

Description

Calculates the ALT-70 score to predict the likelihood of lower extremity cellulitis. This score helps differentiate true cellulitis from cellulitis mimics (pseudocellulitis) such as stasis dermatitis, which are often misdiagnosed and mistreated with antibiotics.

Usage

alt_70_cellulitis(asymmetry, leukocytosis, tachycardia, age_ge_70)

Arguments

Value

A list containing:

References

Raff AB, Weng QY, Cohen JM, et al. A predictive model for diagnosis of lower extremity cellulitis: A cross-sectional study. J Am Acad Dermatol. 2017;76(4):618-625.e2. doi:10.1016/j.jaad.2016.12.044

Examples

# Example 1: High Likelihood
# Unilateral leg redness (+3), HR 95 (+1), Age 75 (+2), Normal WBC (0)
# Score = 6
alt_70_cellulitis(1, 0, 1, 1)

# Example 2: Low Likelihood (Likely Stasis Dermatitis)
# Bilateral redness (0), Age 65 (0), No systemic signs
# Score = 0
alt_70_cellulitis(0, 0, 0, 0)

Altitude-Adjusted PERC Rule for Pulmonary Embolism

Description

Applies the Pulmonary Embolism Rule-out Criteria (PERC) with an adjustment for high altitude. Standard PERC requires an oxygen saturation (SaO2) >= 95%. The Altitude-Adjusted PERC lowers this threshold to >= 90% to account for the physiological hypoxia associated with higher elevations, maintaining specificity while preventing unnecessary imaging.

Usage

altitude_adjusted_perc(age, heart_rate, o2_sat, hemoptysis, estrogen_use,
                       surgery_trauma_4wks, prior_vte, unilateral_leg_swelling)

Arguments

Value

A list containing:

References

Madsen T, et al. Prospective validation of the pulmonary embolism rule-out criteria at high altitude. Am J Emerg Med. 2020.

Examples

# Example 1: Negative Rule Out (High Altitude Context)
# 45yo, HR 90, O2 91% (Passes altitude cutoff), No other risks
altitude_adjusted_perc(45, 90, 91, 0, 0, 0, 0, 0)

# Example 2: Positive Rule Out (Fails O2)
# 45yo, HR 90, O2 88% (Fails even adjusted cutoff)
altitude_adjusted_perc(45, 90, 88, 0, 0, 0, 0, 0)

Alvarado Score for Acute Appendicitis

Description

Calculates the Alvarado Score (also known as the MANTRELS score) to stratify the risk of acute appendicitis in patients presenting with abdominal pain. The score uses clinical symptoms, physical exam findings, and laboratory values to categorize patients into Low, Moderate, or High risk groups, helping to guide decisions regarding imaging (CT) and surgical consultation.

Usage

alvarado_appendicitis(migratory_pain, anorexia, nausea_vomiting, rlq_tenderness,
                      rebound_tenderness, elevated_temp, leukocytosis, shift_left)

Arguments

Value

A list containing:

References

Alvarado A. A practical score for the early diagnosis of acute appendicitis. Ann Emerg Med. 1986;15(5):557-564. doi:10.1016/s0196-0644(86)80993-3

Examples

# Example 1: High Risk (Surgical Candidate)
# Migratory pain, Nausea, RLQ Tenderness (2), Rebound, Fever, Leukocytosis (2)
# Score = 1 + 1 + 2 + 1 + 1 + 2 = 8
alvarado_appendicitis(1, 0, 1, 1, 1, 1, 1, 0)

# Example 2: Low Risk
# Nausea only
# Score = 1
alvarado_appendicitis(0, 0, 1, 0, 0, 0, 0, 0)

Antivenom Dosing Algorithm (CroFab)

Description

Determines the recommended dosing for CroFab antivenom in patients with North American Crotalid (pit viper) envenomation. The algorithm guides the initial dose to achieve control of symptoms (arrest of local progression, normalization of coagulopathy, and resolution of systemic signs) and subsequent maintenance therapy.

Usage

antivenom_dosing_algorithm(signs_of_envenomation, initial_control_achieved = FALSE)

Arguments

Value

A list containing:

References

Lavonas EJ, Ruha AM, Banner W, et al. Unified treatment algorithm for the management of crotaline snakebite in the United States: results of an evidence-informed consensus workshop. BMC Emerg Med. 2011;11:2. doi:10.1186/1471-227X-11-2

Examples

# Example 1: Initial Presentation
# Patient with swelling and pain
antivenom_dosing_algorithm(1, 0)

# Example 2: Control Achieved
# Patient received initial dose, swelling stopped, labs stable
antivenom_dosing_algorithm(1, 1)

# Example 3: Dry Bite
# No signs of envenomation
antivenom_dosing_algorithm(0, 0)

Aortic Dissection Detection Risk Score (ADD-RS)

Description

Calculates the ADD-RS to stratify the pre-test probability of acute aortic dissection. The score is based on the presence of features in three categories: Predisposing Conditions, Pain Features, and Physical Exam Findings. The presence of any feature in a category awards 1 point, for a maximum score of 3.

Usage

aortic_dissection_detection_risk_score(high_risk_conditions,
high_risk_pain, high_risk_exam)

Arguments

Value

A list containing:

References

Rogers AM, Hermann LK, Booher AM, et al. Sensitivity of the aortic dissection detection risk score, a novel guideline-based tool for identification of acute aortic dissection at initial presentation: results from the international registry of acute aortic dissection. Circulation. 2011;123(20):2213-2218. doi:10.1161/CIRCULATIONAHA.110.988568

Examples

# Example 1: High Risk
# Patient with Marfan syndrome (Conditions=1) and Ripping chest pain (Pain=1)
aortic_dissection_detection_risk_score(1, 1, 0)

# Example 2: Low Risk
# No high risk features present
aortic_dissection_detection_risk_score(0, 0, 0)

APACHE II Score

Description

Calculates the Acute Physiology and Chronic Health Evaluation II (APACHE II) Score. This severity-of-disease classification system is used to estimate in-hospital mortality for adult patients admitted to the Intensive Care Unit (ICU). The score is calculated based on 12 physiological measurements, age, and chronic health status measured within the first 24 hours of ICU admission.

Usage

apache_ii_score(age, temperature_c, mean_arterial_pressure, heart_rate,
                respiratory_rate, fio2, pao2, paco2 = NULL, arterial_ph,
                sodium, potassium, creatinine, acute_renal_failure,
                hematocrit, wbc_count, gcs_total, chronic_organ_insufficiency,
                admission_type)

Arguments

Value

A list containing:

References

Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818-829.

Examples

# Example 1: Critical Patient (High Score)
# 75yo, Temp 39, MAP 50, HR 140, RR 35, PaO2 50 (FiO2 0.4), pH 7.2, Na 130, K 6.0,
# Cr 2.0 (ARF), Hct 25, WBC 25, GCS 10, Chronic organ failure (Non-op)
apache_ii_score(75, 39, 50, 140, 35, 0.4, 50, NULL, 7.2,
130, 6.0, 2.0, 1, 25, 25, 10, 1, "non_operative")

# Example 2: Stable Post-Op
# 50yo, Normal vitals/labs, GCS 15, No chronic health issues, Elective surgery
apache_ii_score(50, 37, 90, 80, 16, 0.21, 90, NULL,
7.4, 140, 4.0, 1.0, 0, 40, 8, 15, 0, "elective_postop")

Apfel Score for Postoperative Nausea and Vomiting

Description

Calculates the Apfel Score to estimate the risk of Postoperative Nausea and Vomiting (PONV) in adult patients undergoing inhalational anesthesia. The score ranges from 0 to 4, with each risk factor adding approximately 20% to the baseline risk.

Usage

apfel_ponv(female_sex, history_ponv_motion_sickness, non_smoker, postop_opioids)

Arguments

Value

A list containing:

References

Apfel CC, Laara E, Koivuranta M, Greim CA, Roewer N. A simplified risk score for predicting postoperative nausea and vomiting: conclusions from cross-validations between two centers. Anesthesiology. 1999;91(3):693-700. doi:10.1097/00000542-199909000-00022

Examples

# Example 1: High Risk
# Female, History of Motion Sickness, Non-Smoker, Getting Opioids
# Score = 4 (79% Risk)
apfel_ponv(1, 1, 1, 1)

# Example 2: Low Risk
# Male, Smoker, No Hx, No Opioids
# Score = 0 (10% Risk)
apfel_ponv(0, 0, 0, 0)

APGAR Score for Newborns

Description

Calculates the APGAR score, a rapid method for assessing the clinical status of a newborn at 1 and 5 minutes after birth. The score evaluates five criteria: Appearance, Pulse, Grimace, Activity, and Respiration.

Usage

apgar_score(appearance, pulse, grimace, activity, respiration)

Arguments

Value

A list containing:

References

Apgar V. A proposal for a new method of evaluation of the newborn infant. Curr Res Anesth Analg. 1953;32(4):260-267.

Examples

# Example 1: Healthy Newborn
# Pink body/blue limbs (1), HR >100 (2), Cry (2), Active (2), Good cry (2)
# Score = 9
apgar_score(1, 2, 2, 2, 2)

# Example 2: Distressed Newborn
# Blue (0), HR <100 (1), Grimace (1), Flaccid (0), Irregular resp (1)
# Score = 3
apgar_score(0, 1, 1, 0, 1)

Calculate APRI (AST to Platelet Ratio)

Description

Using aspartate aminotransferase and platelet values, calculate the APRI index, devised by Wait et al 2003, estimates hepatic fibrosis and cirrhosis in patients with hepatitis C. APRI value <0.7 is indicative of little to no evidence of fibrosis, 0.7-1.0 is moderate fibrosis, and 1.0+ is evidence of cirrhosis and significant fibrosis, as validated by Khan et al. 2008 and Lin et al 2011.

Usage

apri(ast, plt)

Arguments

Value

A numeric value/vector with predicted APRI index score.

References

Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518-526. doi:10.1053/jhep.2003.50346 Khan DA, Fatima-Tuz-Zuhra, Khan FA, Mubarak A. Evaluation of diagnostic accuracy of APRI for prediction of fibrosis in hepatitis C patients. J Ayub Med Coll Abbottabad. 2008;20(4):122-126. Lin, Z.-H., Xin, Y.-N., Dong, Q.-J., Wang, Q., Jiang, X.-J., Zhan, S.-H., Sun, Y. and Xuan, S.-Y. (2011), Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: An updated meta-analysis. Hepatology, 53: 726-736. https://doi.org/10.1002/hep.24105

Examples

# The function is defined as
apri(16,150)

ARISCAT Score for Postoperative Pulmonary Complications

Description

Calculates the ARISCAT (Assess Respiratory Risk in Surgical Patients in Catalonia) score to predict the risk of developing postoperative pulmonary complications (PPC) such as respiratory failure, infection, pleural effusion, atelectasis, pneumothorax, bronchospasm, or aspiration pneumonitis.

Usage

ariscat_score(age, spo2, resp_infection_last_month, anemia_hb_le_10,
              incision_site, duration_surgery_hours, emergency_surgery)

Arguments

Value

A list containing:

References

Canet J, Gallart L, Gomar C, et al. Prediction of postoperative pulmonary complications in a population-based surgical cohort. Anesthesiology. 2010;113(6):1338-1350. doi:10.1097/ALN.0b013e3181fc6e0a

Examples

# Example 1: High Risk
# 82yo, SpO2 90%, Intrathoracic, >3h surgery
# Score = 16 + 24 + 0 + 0 + 24 + 23 + 0 = 87
ariscat_score(82, 90, 0, 0, "intrathoracic", 3.5, 0)

# Example 2: Low Risk
# 45yo, SpO2 98%, Peripheral, 1h surgery
# Score = 0
ariscat_score(45, 98, 0, 0, "peripheral", 1, 0)

ASA Physical Status Classification System

Description

Retrieves the definition and examples for the American Society of Anesthesiologists (ASA) Physical Status Classification System. This system is used to assess the fitness of patients before surgery.

Usage

asa_physical_status(asa_class, emergency = 0)

Arguments

Value

A list containing:

References

ASA House of Delegates/Executive Committee. ASA Physical Status Classification System. American Society of Anesthesiologists. Last amended 2020.

Examples

# Example 1: Healthy Patient
asa_physical_status(1, 0)

# Example 2: Emergency Surgery for High Risk Patient
# ASA IV (Severe systemic disease, threat to life)
asa_physical_status(4, 1)

ASAS Criteria for Axial Spondyloarthritis (SpA)

Description

Applies the Assessment of SpondyloArthritis international Society (ASAS) classification criteria for axial spondyloarthritis. These criteria are applicable to patients with back pain for >= 3 months and age of onset < 45 years. Classification is achieved through either the "Imaging Arm" (Sacroiliitis + >=1 SpA feature) or the "Clinical Arm" (HLA-B27 + >=2 other SpA features).

Usage

asas_axial_spa(back_pain_gt_3mo, age_onset_lt_45, sacroiliitis_imaging, hla_b27,
               inflammatory_bp, arthritis, enthesitis_heel, uveitis, dactylitis,
               psoriasis, crohns_colitis, good_nsaid_response, family_history_spa,
               elevated_crp_esr)

Arguments

Value

A list containing:

References

Rudwaleit M, van der Heijde D, Landewé R, et al. The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann Rheum Dis. 2009;68(6):777-783. doi:10.1136/ard.2009.108233

Examples

# Example 1: Imaging Arm
# Entry criteria met, Sacroiliitis present, Inflammatory BP present
asas_axial_spa(1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0)

# Example 2: Clinical Arm
# Entry criteria met, No imaging findings, HLA-B27 Positive,
# Uveitis present, Family History present (2 clinical features)
asas_axial_spa(1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0)

ASAS Criteria for Peripheral Spondyloarthritis (SpA)

Description

Applies the Assessment of SpondyloArthritis international Society (ASAS) classification criteria for peripheral spondyloarthritis. These criteria are applicable to patients with peripheral arthritis, enthesitis, or dactylitis. Classification is achieved if the patient has at least one specific SpA feature (Group 1) OR at least two other SpA features (Group 2).

Usage

asas_peripheral_spa(arthritis, enthesitis, dactylitis, uveitis, psoriasis,
                    crohns_colitis, preceding_infection, hla_b27,
                    sacroiliitis_imaging, inflammatory_bp, family_history_spa)

Arguments

Value

A list containing:

References

Rudwaleit M, van der Heijde D, Landewé R, et al. The Assessment of SpondyloArthritis international Society classification criteria for peripheral spondyloarthritis and for spondyloarthritis in general. Ann Rheum Dis. 2011;70(1):25-31. doi:10.1136/ard.2010.133645

Examples

# Example 1: Group 1 Met
# Arthritis (Entry), Psoriasis (Group 1)
asas_peripheral_spa(1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0)

# Example 2: Group 2 Met
# Enthesitis (Entry + Group 2), Family History (Group 2)
# Total Group 2 count = 2
asas_peripheral_spa(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1)

ASCOD Phenotyping for Ischemic Stroke

Description

Generates the ASCOD phenotype string for ischemic stroke characterization. The ASCOD classification system characterizes stroke based on the presence and likelihood of five underlying pathologies: Atherosclerosis (A), Small vessel disease (S), Cardiac pathology (C), Other causes (O), and Dissection (D). Each category is graded based on the strength of the causal link.

Usage

ascod_stroke_phenotype(a_grade, s_grade, c_grade, o_grade, d_grade)

Arguments

Details

Grading Key: 1: Potential Cause (Disease present and potentially causal). 2: Uncertain Cause (Disease present but causal link uncertain). 3: Unlikely Cause (Disease present but unlikely to be the cause). 0: Absent (No disease found). 9: Insufficient Workup (Grading not possible).

Value

A list containing:

References

Amarenco P, Bogousslavsky J, Caplan LR, Donnan GA, Hennerici MG. The ASCOD phenotyping of ischemic stroke (Updated ASCOD I). Cerebrovasc Dis. 2013;36(1):1-5. doi:10.1159/000352050

Examples

# Example 1: Cardioembolic Stroke
# Atherosclerosis absent (0), Small vessel unlikely (3),
# Cardiac potential (1), Other insuff (9), Dissection absent (0)
ascod_stroke_phenotype(0, 3, 1, 9, 0)

# Example 2: Mixed Etiology
# Atherosclerosis potential (1), Small vessel potential (1), others absent
ascod_stroke_phenotype(1, 1, 0, 0, 0)

ASCVD Risk Estimator (2013 ACC/AHA Guidelines)

Description

Calculates the 10-year estimated risk of atherosclerotic cardiovascular disease (ASCVD) events (fatal/non-fatal MI or Stroke) using the 2013 ACC/AHA Pooled Cohort Equations. The calculator is valid for adults aged 40-79 without existing cardiovascular disease.

Usage

ascvd_2013_risk_calculator(age, sex, race, total_cholesterol, hdl_cholesterol,
                           systolic_bp, bp_medication, diabetes, smoker)

Arguments

Value

A list containing:

References

Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49-73. doi:10.1161/01.cir.0000437741.48606.98

Examples

# Example 1: High Risk
# 65yo Male, AfrAm, TotChol 200, HDL 40, SBP 150 (Treated), Smoker, Diabetes
ascvd_2013_risk_calculator(65, "male", "aa", 200, 40, 150, 1, 1, 1)

# Example 2: Low Risk
# 45yo Female, White, TotChol 170, HDL 60, SBP 110 (Untreated), Non-Smoker, No DM
ascvd_2013_risk_calculator(45, "female", "white", 170, 60, 110, 0, 0, 0)

Ankylosing Spondylitis Disease Activity Score with CRP (ASDAS-CRP)

Description

Calculates the ASDAS-CRP score, a composite index to assess disease activity in patients with Ankylosing Spondylitis (AS). It combines patient-reported outcomes (Back Pain, Morning Stiffness, Global Assessment, Peripheral Pain) with an objective inflammatory marker (CRP).

Usage

asdas_crp(back_pain, duration_morning_stiffness, patient_global,
          peripheral_pain_swelling, crp_mg_l)

Arguments

Value

A list containing:

References

Lukas C, Landewe R, Sieper J, et al. Development of an ASAS-endorsed disease activity score for ankylosing spondylitis (ASDAS). Ann Rheum Dis. 2009;68(1):18-24. doi:10.1136/ard.2008.094870

Examples

# Example 1: High Disease Activity
# Back Pain 6, Stiffness 5, Global 6, Peripheral 4, CRP 10
asdas_crp(6, 5, 6, 4, 10)

# Example 2: Inactive Disease
# All scores 1, CRP 1.0
asdas_crp(1, 1, 1, 1, 1)

Ankylosing Spondylitis Disease Activity Score with ESR (ASDAS-ESR)

Description

Calculates the ASDAS-ESR score, a composite index to assess disease activity in patients with Ankylosing Spondylitis (AS). It combines patient-reported outcomes (Back Pain, Morning Stiffness, Global Assessment, Peripheral Pain) with the Erythrocyte Sedimentation Rate (ESR).

Usage

asdas_esr(back_pain, duration_morning_stiffness, patient_global,
          peripheral_pain_swelling, esr_mm_hr)

Arguments

Value

A list containing:

References

Lukas C, Landewe R, Sieper J, et al. Development of an ASAS-endorsed disease activity score for ankylosing spondylitis (ASDAS). Ann Rheum Dis. 2009;68(1):18-24. doi:10.1136/ard.2008.094870

Examples

# Example 1: High Disease Activity
# Back Pain 6, Stiffness 5, Global 6, Peripheral 4, ESR 25
asdas_esr(6, 5, 6, 4, 25)

# Example 2: Inactive Disease
# All scores 1, ESR 5
asdas_esr(1, 1, 1, 1, 5)

ASSURE Renal Cell Carcinoma Prognosis

Description

Calculates the prognostic risk group (Low, Intermediate, High) for Disease-Free Survival (DFS) and Overall Survival (OS) in patients with resected intermediate- or high-risk localized Renal Cell Carcinoma (RCC). This model is based on data from the ASSURE (E2805) clinical trial and incorporates histology, grade, necrosis, and vascular invasion.

Usage

assure_rcc_prognosis(histology, fuhrman_grade, tumor_size_cm, necrosis,
                     vascular_invasion, nodal_involvement, sarcomatoid)

Arguments

Value

A list containing:

References

Correa AF, et al. Predicting Disease Recurrence, Early Progression, and Overall Survival Following Surgical Resection for High-risk Localized and Locally Advanced Renal Cell Carcinoma. Eur Urol. 2021;80(2):20-31.

Examples

# Example 1: High Risk
# Clear Cell, Grade 4, 8cm, Necrosis, Renal Vein Inv, N1
# Points: 35 (Hist) + 78 (Gr4) + 32 (Size) + 23 (Necr) + 30 (Vasc) + 65 (Node) = 263 -> High
assure_rcc_prognosis("clear cell", 4, 8.0, 1, "renal vein", 1, 0)

# Example 2: Low Risk
# Chromophobe, Grade 2, 4cm, No Necrosis, No Vasc, N0
# Points: 0 (Hist) + 13 (Gr2) + 16 (Size) = 29 -> Low
assure_rcc_prognosis("chromophobe", 2, 4.0, 0, "none", 0, 0)

De Ritis Ratio (Ast/Alt Ratio)

Description

The De Ritis Ratio has been used clinically and in research for almost 50 years and is simply the ratio of aspartate transferase to alanine transferase. Validated in many cohorts, interpretations vary, and as such, I simply define a ratio of above 0.8 as indicative of liver injury, commonly agreed upon in the literature.

Usage

ast_alt_ratio_interpret(ast, alt)

Arguments

Value

A numeric value/vector with whether by De Ritis ratio, there is liver injury.

References

Botros M, Sikaris KA. The de ritis ratio: the test of time. Clin Biochem Rev. 2013 Nov;34(3):117-30. PMID: 24353357; PMCID: PMC3866949.

Examples

# The function is defined as
ast_alt_ratio_interpret(30, 25)

Asthma Predictive Index (API)

Description

Calculates the Stringent Asthma Predictive Index (API) to predict the likelihood of developing active asthma at school age in children younger than 3 years who present with frequent wheezing. This tool is based on the original Castro-Rodriguez criteria derived from the Tucson Children's Respiratory Study.

Usage

asthma_predictive_index(wheezing_freq_ge_4, parent_asthma, eczema,
                        allergic_rhinitis, wheezing_apart_colds,
                        eosinophilia_ge_4)

Arguments

Value

A list containing:

References

Castro-Rodriguez JA, Holberg CJ, Wright AL, Martinez FD. A clinical index to define risk of asthma in young children with recurrent wheeze. Am J Respir Crit Care Med. 2000;162(4 Pt 1):1403-1406. doi:10.1164/ajrccm.162.4.9912111

Examples

# Example 1: Positive API
# Freq wheezing, Parent Asthma (Major), No others
# Result: Positive (1 Major met)
asthma_predictive_index(1, 1, 0, 0, 0, 0)

# Example 2: Positive API via Minor Criteria
# Freq wheezing, No Major, Allergic Rhinitis + Eosinophilia (2 Minor)
asthma_predictive_index(1, 0, 0, 1, 0, 1)

# Example 3: Negative API
# Freq wheezing, only Eosinophilia (1 Minor)
asthma_predictive_index(1, 0, 0, 0, 0, 1)

ASTRAL Score for Ischemic Stroke

Description

Calculates the ASTRAL (Acute Stroke Registry and Analysis of Lausanne) Score to predict the probability of an unfavorable functional outcome (modified Rankin Scale score > 2) at 3 months following an acute ischemic stroke.

Usage

astral_stroke_score(age, nihss, time_gt_3h, visual_field_defect, glucose,
                    loc_reduced, glucose_units = "mg/dL")

Arguments

Value

A list containing:

References

Ntaios G, Faouzi M, Ferrari J, et al. An integer-based score to predict functional outcome in acute ischemic stroke: the ASTRAL score. Neurology. 2012;78(24):1916-1922. doi:10.1212/WNL.0b013e318259e221

Examples

# Example 1: High Risk
# 80yo (16pts), NIHSS 20, Time >3h (2), Visual Defect (2), Glucose 150 (1), Reduced LOC (2)
# Score = 16 + 20 + 2 + 2 + 1 + 2 = 43
astral_stroke_score(80, 20, 1, 1, 150, 1)

# Example 2: Moderate Risk
# 65yo (13pts), NIHSS 8, Time <3h (0), No Visual (0), Glucose 100 (0), Alert LOC (0)
# Score = 13 + 8 = 21
astral_stroke_score(65, 8, 0, 0, 100, 0)

Mayo Clinic Risk Stratification for Smoldering Multiple Myeloma (20-2-20 Model)

Description

Calculates the risk stratification for patients with Smoldering Multiple Myeloma (SMM) using the "20-2-20" criteria developed by the Mayo Clinic. This model predicts the risk of progression to active Multiple Myeloma (MM) based on three risk factors: Bone Marrow Plasma Cells (BMPC) > 20%, Serum M-protein > 2 g/dL, and Serum Free Light Chain (FLC) ratio > 20.

Usage

asymptomatic_myeloma_prognosis(bmpc_percent, m_protein_g_dl, flc_ratio)

Arguments

Value

A list containing:

References

Lakshman A, Rajkumar SV, Buadi FK, et al. Risk stratification of smoldering multiple myeloma incorporating revised IMWG diagnostic criteria. Blood Cancer J. 2018;8(6):59. doi:10.1038/s41408-018-0077-4

Examples

# Example 1: High Risk (2 factors)
# BMPC 30%, M-protein 1.5, FLC Ratio 25
asymptomatic_myeloma_prognosis(30, 1.5, 25)

# Example 2: Low Risk (0 factors)
# BMPC 10%, M-protein 1.0, FLC Ratio 8
asymptomatic_myeloma_prognosis(10, 1.0, 8)

ATLAS Score for Clostridium Difficile Infection

Description

Calculates the ATLAS Score to predict cure rates and 30-day mortality in patients with Clostridium difficile infection (CDI). The score evaluates Age, Treatment with systemic antibiotics, Leukocyte count, Albumin, and Serum creatinine.

Usage

atlas_cdi_score(age, systemic_abx, wbc_count, albumin, creatinine,
                albumin_units = "g/dL", creatinine_units = "mg/dL")

Arguments

Value

A list containing:

References

Miller MA, et al. Derivation and validation of a simple clinical bedside score (ATLAS) for Clostridium difficile infection which predicts response to therapy. BMC Infect Dis. 2013;13:148.

Examples

# Example 1: Low Risk
# 45yo, No abx, WBC 10k, Alb 4.0, Cr 0.9
atlas_cdi_score(45, 0, 10000, 4.0, 0.9)

# Example 2: High Risk
# 85yo (+2), On Abx (+2), WBC 26k (+2), Alb 2.0 (+2), Cr 2.5 (+2)
# Score = 10
atlas_cdi_score(85, 1, 26000, 2.0, 2.5)

ATRIA Bleeding Risk Score

Description

Calculates the ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Bleeding Risk Score. This tool stratifies the risk of major hemorrhage in patients with atrial fibrillation who are taking warfarin. It outperforms the HAS-BLED score in predicting major bleeding events.

Usage

atria_bleeding_risk(age, sex, hemoglobin_g_dl, egfr_ml_min, prior_bleeding, hypertension)

Arguments

Value

A list containing:

References

Fang MC, Go AS, Chang Y, et al. A new risk scheme to predict warfarin-associated hemorrhage: The ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study. J Am Coll Cardiol. 2011;58(4):395-401. doi:10.1016/j.jacc.2011.03.031

Examples

# Example 1: High Risk
# 80yo Male (+2), Hgb 11 (+3), eGFR 25 (+3), No Bleed, HTN (+1)
# Score = 9
atria_bleeding_risk(80, "male", 11, 25, 0, 1)

# Example 2: Low Risk
# 65yo Female (0), Hgb 13 (0), eGFR 60 (0), No Bleed, No HTN
# Score = 0
atria_bleeding_risk(65, "female", 13, 60, 0, 0)

ATRIA Stroke Risk Score

Description

Calculates the ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Stroke Risk Score. This tool estimates the risk of ischemic stroke in patients with atrial fibrillation. It was developed to improve upon the CHADS2 score by incorporating differential weighting for age, prior stroke, and including renal dysfunction variables.

Usage

atria_stroke_risk(age, prior_stroke, female_sex, diabetes, chf,
                  hypertension, proteinuria, egfr_lt_45_or_esrd)

Arguments

Value

A list containing:

References

Singer DE, Chang Y, Borowsky LH, et al. A new risk scheme to predict ischemic stroke and other thromboembolism in atrial fibrillation: the ATRIA (Anticoagulation and Risk Factors in Atrial Fibrillation) Study. J Am Heart Assoc. 2013;2(3):e000250. doi:10.1161/JAHA.113.000250

Examples

# Example 1: High Risk
# 85yo (+6), Female (+1), Prior Stroke (+4)
# Score = 11
atria_stroke_risk(85, 1, 1, 0, 0, 0, 0, 0)

# Example 2: Low Risk
# 70yo (+3), HTN (+1), No other factors
# Score = 4
atria_stroke_risk(70, 0, 0, 0, 0, 1, 0, 0)

Atropine Dosing for Cholinesterase Inhibitor Toxicity

Description

Calculates the recommended atropine dosing for patients with organophosphate or carbamate toxicity (cholinesterase inhibitor poisoning). The protocol utilizes a "doubling dose" strategy: if bronchial secretions remain copious after the initial dose, the subsequent dose is doubled every 5-10 minutes until secretions are dried (atropinization). Once controlled, a maintenance infusion is calculated.

Usage

atropine_toxicity_dosing(last_dose_mg, secretions_controlled,
                         total_cumulative_dose_mg = NULL)

Arguments

Value

A list containing:

References

Eddleston M, Buckley NA, Eyer P, Dawson AH. Management of acute organophosphorus pesticide poisoning. Lancet. 2008;371(9612):597-607. doi:10.1016/S0140-6736(07)61202-1

Examples

# Example 1: Uncontrolled after 2mg
# Secretions still present (0)
atropine_toxicity_dosing(2, 0)

# Example 2: Uncontrolled after 4mg
# Secretions still present (0) -> Recommends 8mg
atropine_toxicity_dosing(4, 0)

# Example 3: Controlled
# Secretions dry (1). Total dose given was 20mg.
# Recommends infusion at 2-4 mg/hr
atropine_toxicity_dosing(8, 1, total_cumulative_dose_mg = 20)

American Urological Association Symptom Index (AUA-SI)

Description

Calculates the AUA Symptom Index (also known as the International Prostate Symptom Score, IPSS) for Benign Prostatic Hyperplasia (BPH). The index consists of 7 questions regarding urinary symptoms, each scored from 0 to 5. An optional 8th question assesses Quality of Life (QoL) regarding urinary symptoms.

Usage

aua_symptom_index(incomplete_emptying, frequency, intermittency, urgency,
                  weak_stream, straining, nocturia, quality_of_life = NULL)

Arguments

Details

Scoring Key (Questions 1-6): 0 = Not at all 1 = Less than 1 time in 5 2 = Less than half the time 3 = About half the time 4 = More than half the time 5 = Almost always

Value

A list containing:

References

Barry MJ, Fowler FJ Jr, O'Leary MP, et al. The American Urological Association symptom index for benign prostatic hyperplasia. The Measurement Committee of the American Urological Association. J Urol. 1992;148(5):1549-1557. doi:10.1016/s0022-5347(17)36966-5

Examples

# Example 1: Moderate Symptoms
# Scores: 2, 2, 3, 1, 3, 1, 2 = 14
aua_symptom_index(2, 2, 3, 1, 3, 1, 2, quality_of_life = 3)

# Example 2: Mild Symptoms
# All 1s = 7
aua_symptom_index(1, 1, 1, 1, 1, 1, 1)

American University of Beirut (AUB) HAS2 Cardiovascular Risk Index

Description

Calculates the AUB-HAS2 score to stratify cardiovascular risk. This simple additive risk index was derived and validated to predict major adverse cardiovascular events (death, myocardial infarction, or coronary revascularization) using four easily obtainable clinical variables: Hypertension, Age, Smoking, and Hyperlipidemia (HAS2).

Usage

aub_has2_cardiovascular_risk(hypertension, age, smoking, hyperlipidemia)

Arguments

Value

A list containing:

References

Dakik HA, Chehab O, Eldanawi H, et al. The AUB-HAS2 Cardiovascular Risk Index: A Simple Outcome Score for Clinical Practice. PLoS One. 2016;11(1):e0146534. doi:10.1371/journal.pone.0146534

Examples

# Example 1: High Risk
# Hypertension (+1), Age 65 (+1), Hyperlipidemia (+2) -> Score 4
aub_has2_cardiovascular_risk(1, 65, 0, 1)

# Example 2: Low Risk
# Smoking only (+1), Age 40, No HTN/Lipids
# Score = 1
aub_has2_cardiovascular_risk(0, 40, 1, 0)

AUDIT-C Alcohol Use Screen

Description

Calculates the AUDIT-C (Alcohol Use Disorders Identification Test - Consumption) score. This is a 3-item screening tool derived from the full AUDIT to identify patients with hazardous alcohol consumption or active alcohol use disorders.

Usage

audit_c_alcohol_screen(sex, frequency_score, amount_score, binge_score)

Arguments

Value

A list containing:

References

Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med. 1998;158(16):1789-1795. doi:10.1001/archinte.158.16.1789

Examples

# Example 1: Positive Screen (Male)
# Frequent drinking (3), Moderate amount (1), Occasional binge (2)
# Score = 6
audit_c_alcohol_screen("male", 3, 1, 2)

# Example 2: Negative Screen (Female)
# Monthly drinking (1), Low amount (0), No binge (0)
# Score = 1
audit_c_alcohol_screen("female", 1, 0, 0)

Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK)

Description

Calculates the AUSDRISK score to estimate the 5-year risk of developing Type 2 Diabetes. This tool is designed for use in the general Australian adult population. It evaluates demographic, lifestyle, and anthropometric risk factors.

Usage

ausdrisk_score(age, sex, ethnicity_high_risk, family_history,
               high_glucose_history, bp_meds, smoker, fruit_veg_daily,
               physical_activity_sufficient, waist_cm)

Arguments

Value

A list containing:

References

Department of Health and Aged Care. The Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK). Australian Government. 2008.

Examples

# Example 1: High Risk
# 50yo Male (+7), Asian (+2), FamHx (+3), No Activity (+2), Waist 95 (+4)
# Score = 4 (Age) + 3 (Sex) + 2 (Eth) + 3 (Fam) + 2 (Act) + 4 (Waist) = 18
ausdrisk_score(50, "male", 1, 1, 0, 0, 0, 1, 0, 95)

# Example 2: Low Risk
# 30yo Female, No risk factors
# Score = 0
ausdrisk_score(30, "female", 0, 0, 0, 0, 0, 1, 1, 70)

AWOL Score for Delirium

Description

Calculates the AWOL score to predict the risk of developing delirium during hospitalization. The score assesses four risk factors: Age, ability to spell "World" backwards, Orientation, and iLlnes severity.

Usage

awol_delirium_risk(age, spell_world_failed, orientation_failed,
                   illness_severity_moderate_severe)

Arguments

Value

A list containing:

References

Douglas VC, Hessler CS, Dhaliwal G, et al. The AWOL tool: derivation and validation of a delirium prediction rule. J Hosp Med. 2013;8(9):493-499. doi:10.1002/jhm.2062

Examples

# Example 1: High Risk
# 82yo (+1), Failed spelling (+1), Oriented (0), Moderate Severity (+1)
# Score = 3 (~37% risk)
awol_delirium_risk(82, 1, 0, 1)

# Example 2: Low Risk
# 65yo (0), Spelled correctly (0), Oriented (0), Mild Severity (0)
# Score = 0 (~2% risk)
awol_delirium_risk(65, 0, 0, 0)

Bacterial Meningitis Score for Children

Description

Calculates the Bacterial Meningitis Score to stratify the risk of bacterial meningitis in children (aged 29 days to 19 years) presenting with CSF pleocytosis (>= 10 WBCs/uL). A score of 0 identifies patients at very low risk who may be candidates for outpatient management.

Usage

bacterial_meningitis_score(gram_stain_positive, csf_protein_mg_dl,
                           peripheral_anc, seizure_history, csf_neutrophils)

Arguments

Value

A list containing:

References

Nigrovic LE, Kuppermann N, Malley R. Development and validation of a multivariable predictive model to distinguish bacterial from aseptic meningitis in children in the post-Haemophilus influenzae era. Pediatrics. 2002;110(4):712-719. doi:10.1542/peds.110.4.712

Examples

# Example 1: High Risk
# Positive Gram stain (+2), Protein 90 (+1), ANC 12000 (+1)
# Score = 4
bacterial_meningitis_score(1, 90, 12000, 0, 500)

# Example 2: Very Low Risk
# Negative Gram stain, Protein 40, ANC 5000, No seizure, CSF Neuts 100
# Score = 0
bacterial_meningitis_score(0, 40, 5000, 0, 100)

BALL Score for Relapsed/Refractory CLL

Description

Calculates the BALL (Beta-2 microglobulin, Anemia, LDH, Last therapy) score to prognosticate Overall Survival in patients with relapsed or refractory Chronic Lymphocytic Leukemia (CLL). This score uses four readily available clinical variables to stratify patients into Low, Intermediate, or High risk categories.

Usage

ball_score_cll(beta2_microglobulin_mg_l, hemoglobin_g_dl, sex, ldh_elevated,
               last_therapy_pfs_lt_24mo)

Arguments

Value

A list containing:

References

Grosicki S, et al. The BALL score: a new prognostic model for relapsed/refractory chronic lymphocytic leukemia patients. Leuk Lymphoma. 2020.

Examples

# Example 1: High Risk
# B2M 5.0 (+1), Hb 10 (Male, +1), High LDH (+1), Short prior remission (+1)
# Score = 4
ball_score_cll(5.0, 10.0, "male", 1, 1)

# Example 2: Low Risk
# B2M 2.0 (0), Hb 13 (Female, 0), Normal LDH (0), Long prior remission (0)
# Score = 0
ball_score_cll(2.0, 13.0, "female", 0, 0)

BAP-65 Score for Acute Exacerbation of COPD

Description

Calculates the BAP-65 score to stratify risk in patients presenting with an acute exacerbation of Chronic Obstructive Pulmonary Disease (COPD). The score correlates with in-hospital mortality and need for mechanical ventilation.

Usage

bap65_copd(bun, altered_mental_status, pulse, age)

Arguments

Value

A list containing:

References

Shorr AF, Sun X, Johannes RS, Yaitanes A, Tabak YP. Validation of a novel risk score for severity of illness in acute exacerbations of COPD. Chest. 2011;140(5):1177-1183. doi:10.1378/chest.10-2851

Examples

# Example 1: High Risk (Class IV)
# BUN 30 (+1), Alert (0), Pulse 115 (+1), Age 70 (+1)
# Score = 3
bap65_copd(30, 0, 115, 70)

# Example 2: Low Risk (Class I)
# BUN 15 (0), Alert (0), Pulse 80 (0), Age 60 (0)
# Score = 0
bap65_copd(15, 0, 80, 60)

BARD Score for assessing Fibrosis in NAFLD

Description

By using a patient's bmi, lab values of aspartate transferae, alanine transferase, and whether they have type 2 diabetes, calculate the patient's risk for fibrosis, especially if they have diagnosed or suspected NAFLD. BARD score from 0-1 is low risk of fibrosis and a score from 2-4 is high risk of fibrosis. Created by Harrison et al. 2008. Validated by Ruffillo et al. (2011) and Raszeja-wyszomirska et al. (2010).

Usage

bard_score(bmi, ast, alt, diabetes)

Arguments

Value

A numeric value/vector with predicted BARD score.

References

Harrison SA, Oliver D, Arnold HL, Gogia S, Neuschwander-tetri BA. Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease. Gut. 2008;57(10):1441-7. Raszeja-wyszomirska J, Szymanik B, Ławniczak M, et al. Validation of the BARD scoring system in Polish patients with nonalcoholic fatty liver disease (NAFLD). BMC Gastroenterol. 2010;10:67. Ruffillo G, Fassio E, Alvarez E, et al. Comparison of NAFLD fibrosis score and BARD score in predicting fibrosis in nonalcoholic fatty liver disease. J Hepatol. 2011;54(1):160-3. Cichoż-lach H, Celiński K, Prozorow-król B, Swatek J, Słomka M, Lach T. The BARD score and the NAFLD fibrosis score in the assessment of advanced liver fibrosis in nonalcoholic fatty liver disease. Med Sci Monit. 2012;18(12):CR735-40.

Examples

# The function is defined as
bard_score(25, 40, 32, 1)

BARD Score for assessing Fibrosis in NAFLD Interpretation

Description

By using a patient's bmi, lab values of aspartate transferae, alanine transferase, and whether they have type 2 diabetes, calculate the patient's risk for fibrosis, especially if they have diagnosed or suspected NAFLD. Created by Harrison et al. 2008. Validated by Ruffillo et al. (2011) and Raszeja-wyszomirska et al. (2010).

Usage

bard_score_interpret(bmi, ast, alt, diabetes)

Arguments

Value

A string with interpreted BARD score as "high" or "low" fibrosis.

References

Harrison SA, Oliver D, Arnold HL, Gogia S, Neuschwander-tetri BA. Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease. Gut. 2008;57(10):1441-7. Raszeja-wyszomirska J, Szymanik B, Ławniczak M, et al. Validation of the BARD scoring system in Polish patients with nonalcoholic fatty liver disease (NAFLD). BMC Gastroenterol. 2010;10:67. Ruffillo G, Fassio E, Alvarez E, et al. Comparison of NAFLD fibrosis score and BARD score in predicting fibrosis in nonalcoholic fatty liver disease. J Hepatol. 2011;54(1):160-3. Cichoż-lach H, Celiński K, Prozorow-król B, Swatek J, Słomka M, Lach T. The BARD score and the NAFLD fibrosis score in the assessment of advanced liver fibrosis in nonalcoholic fatty liver disease. Med Sci Monit. 2012;18(12):CR735-40.

Examples

# The function is defined as
bard_score_interpret(25, 40, 32, 1)

Barnes-Jewish Hospital Stroke Dysphagia Screen

Description

Implements the Barnes-Jewish Hospital Stroke Dysphagia Screen, a validated bedside tool to assess swallowing function in acute stroke patients. The screen consists of two stages: an initial assessment of consciousness and oral motor function, followed by a 3-oz water swallow test if the first stage is passed.

Usage

barnes_jewish_dysphagia_screen(gcs, facial_asymmetry, tongue_asymmetry,
                               palatal_asymmetry, water_swallow_signs = NULL)

Arguments

Value

A list containing:

References

Edmiaston J, Connor LT, Loehr L, Nassief A. Validation of a dysphagia screening tool in acute stroke patients. Am J Crit Care. 2010;19(4):357-364. doi:10.4037/ajcc2009961

Examples

# Example 1: Failed Initial Assessment
# GCS 15, but Facial Asymmetry present
barnes_jewish_dysphagia_screen(15, 1, 0, 0)

# Example 2: Passed Initial, Failed Water Test
# Normal anatomy, but coughed during water test
barnes_jewish_dysphagia_screen(15, 0, 0, 0, 1)

# Example 3: Passed Full Screen
# Normal anatomy, no signs on water swallow
barnes_jewish_dysphagia_screen(15, 0, 0, 0, 0)

Behavioral Activity Rating Scale (BARS)

Description

Retrieves the description and clinical status for a given Behavioral Activity Rating Scale (BARS) score. BARS is a validated tool used to assess the severity of agitation in patients, particularly in acute psychiatric or emergency settings. It utilizes a 7-point scale ranging from 1 (Difficult to rouse) to 7 (Violent).

Usage

bars_agitation_score(patient_state_score)

Arguments

Value

A list containing:

References

Swift RH, Harrigan EP, Cappelleri JC, et al. Validation of the Behavioural Activity Rating Scale (BARS): a novel measure of activity in agitated patients. J Psychiatr Res. 2002;36(2):87-95. doi:10.1016/s0022-3956(01)00053-6

Examples

# Example 1: Normal Activity
bars_agitation_score(4)

# Example 2: Severe Agitation
bars_agitation_score(7)

Barthel Index for Activities of Daily Living (ADL)

Description

Calculates the Barthel Index to assess a patient's performance in activities of daily living (ADL). The index measures the degree of assistance required by an individual on 10 items of mobility and self-care. A higher score indicates greater independence.

Usage

barthel_index_adl(feeding, bathing, grooming, dressing, bowels, bladder,
                  toilet_use, transfers, mobility, stairs)

Arguments

Value

A list containing:

References

Mahoney FI, Barthel DW. Functional evaluation: the Barthel Index. Md State Med J. 1965;14:61-65. Shah S, Vanclay F, Cooper B. Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol. 1989;42(8):703-709. doi:10.1016/0895-4356(89)90065-6

Examples

# Example 1: Total Dependence
# All 0s
barthel_index_adl(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)

# Example 2: Moderate Dependence
# Feeding 10, Bathing 0, Grooming 5, Dressing 5, Bowels 10, Bladder 10,
# Toilet 5, Transfers 10, Mobility 10, Stairs 5
# Score = 70
barthel_index_adl(10, 0, 5, 5, 10, 10, 5, 10, 10, 5)

Basal Energy Expenditure (Harris-Benedict Equation)

Description

Calculates the Basal Energy Expenditure (BEE), which estimates the number of calories a person burns at rest over 24 hours. This function uses the original Harris-Benedict Equation (1919), which is widely used for nutritional assessments.

Usage

basal_energy_expenditure(sex, height_cm, weight_kg, age)

Arguments

Value

A list containing:

References

Harris JA, Benedict FG. A Biometric Study of Basal Metabolism in Man. Washington DC: Carnegie Institute of Washington. 1919.

Examples

# Example 1: Male
# 180cm, 80kg, 30 years old
# BEE = 66.5 + (13.75*80) + (5.003*180) - (6.755*30) = 66.5 + 1100 + 900.5 - 202.6 = ~1864
basal_energy_expenditure("male", 180, 80, 30)

# Example 2: Female
# 160cm, 60kg, 40 years old
# BEE = 655.1 + (9.56*60) + (1.85*160) - (4.68*40) = 655.1 + 573.6 + 296 - 187.2 = ~1338
basal_energy_expenditure("female", 160, 60, 40)

Basic Statistics Calculator (Diagnostic Test Evaluation)

Description

Calculates standard diagnostic statistics from a 2x2 contingency table (True Positives, False Positives, False Negatives, True Negatives). Metrics include Sensitivity, Specificity, Positive/Negative Predictive Values (PPV/NPV), Likelihood Ratios (LR+/LR-), Accuracy, and Disease Prevalence.

Usage

basic_stats_calculator(tp, fp, fn, tn)

Arguments

Value

A list containing:

References

Altman DG, Bland JM. Diagnostic tests 1: Sensitivity and specificity. BMJ. 1994;308(6943):1552. Altman DG, Bland JM. Diagnostic tests 2: Predictive values. BMJ. 1994;309(6947):102.

Examples

# Example 1: Good Test
# TP=80, FP=10, FN=20, TN=90
basic_stats_calculator(80, 10, 20, 90)

# Example 2: Perfect Specificity
# TP=50, FP=0, FN=50, TN=100
# LR+ will be Inf
basic_stats_calculator(50, 0, 50, 100)

Bastion Classification of Lower Limb Blast Injuries

Description

Classifies lower limb injuries caused by blasts (e.g., Improvised Explosive Devices/IEDs) based on the most proximal level of injury. This classification system aids in communicating injury severity, anticipating resource needs (surgical consultations), and guiding hemorrhage control (tourniquet viability).

Usage

bastion_classification_lower_limb(injury_level, segmental_injury = 0,
                                  abdominal_injury = 0, genital_perineal_injury = 0,
                                  pelvic_ring_injury = 0, upper_limb_injury = 0)

Arguments

Value

A list containing:

References

Jacobs N, Rourke K, Keene D, et al. Lower limb injuries caused by improvised explosive devices: proposed 'Bastion classification' and prospective validation. Injury. 2012;43(9):1442-1446. doi:10.1016/j.injury.2012.05.001

Examples

# Example 1: Class 3 Injury
# Proximal lower leg injury (Level 3), with upper limb injury
bastion_classification_lower_limb(3, 0, 0, 0, 0, 1)

# Example 2: Class 5 Injury
# Buttock involvement (Level 5), with abdominal and pelvic injury
bastion_classification_lower_limb(5, 0, 1, 0, 1, 0)

Barcelona Clinic Liver Cancer (BCLC) Staging

Description

Determines the BCLC Stage, prognosis, and treatment recommendations for Hepatocellular Carcinoma (HCC). The classification uses Performance Status (ECOG), Tumor Burden, and Liver Function (Child-Pugh) to stratify patients into five stages (0, A, B, C, D).

Usage

bclc_staging(ecog_ps, tumor_characteristics, child_pugh_class)

Arguments

Value

A list containing:

References

Llovet JM, Bru C, Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis. 1999;19(3):329-38. doi:10.1055/s-2007-1007122 Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis and treatment of hepatocellular carcinoma: The 2022 update. J Hepatol. 2022;76(3):681-693.

Examples

# Example 1: Intermediate Stage
# PS 0, Multinodular, Child-Pugh A
bclc_staging(0, "multinodular", "A")

# Example 2: Advanced Stage
# PS 1, Single >2cm (Tumor burden is early, but PS 1 upgrades stage), Child-Pugh A
bclc_staging(1, "single_gt_2cm_or_3_lt_3cm", "A")

# Example 3: Terminal Stage
# PS 3
bclc_staging(3, "single_lt_2cm", "B")

BeAM Value for Type 2 Diabetes

Description

Calculates the BeAM Value (Bedtime minus AM glucose differential). This simple metric helps determine if a patient with Type 2 Diabetes, currently treated with basal insulin, requires the addition of prandial (mealtime) insulin. A high BeAM value indicates that blood glucose is rising significantly throughout the day (post-prandial spikes), despite adequate overnight control (good AM fasting glucose).

Usage

beam_value_diabetes(bedtime_glucose, am_glucose, units = "mg/dL")

Arguments

Value

A list containing:

References

Zisman A, Morales F, Stewart J, et al. BeAM value: an indicator of the need to initiate and intensify prandial therapy in patients with type 2 diabetes mellitus receiving basal insulin. BMJ Open Diab Res Care. 2016;4:e000268. doi:10.1136/bmjdrc-2016-000268

Examples

# Example 1: High BeAM Value (Prandial Insulin Needed)
# Bedtime 200 mg/dL, AM Fasting 110 mg/dL
# BeAM = 90 (>50)
beam_value_diabetes(200, 110)

# Example 2: Low BeAM Value (Basal Issue or Controlled)
# Bedtime 130 mg/dL, AM Fasting 120 mg/dL
# BeAM = 10
beam_value_diabetes(130, 120)

Benzodiazepine Conversion Calculator

Description

Calculates the estimated equivalent dose between different benzodiazepines. The calculation typically uses 10 mg of oral Diazepam as the standard reference unit to derive equivalencies for other agents (e.g., based on the Ashton Manual and other clinical pharmacological data).

Usage

benzodiazepine_conversion(source_drug, source_dose, target_drug)

Arguments

Value

A list containing:

References

Ashton H. Protocol for the treatment of benzodiazepine withdrawal. University of Newcastle upon Tyne. 2002.

Examples

# Example 1: Convert 1mg Alprazolam to Diazepam
# 0.5mg Alprazolam ~= 10mg Diazepam, so 1mg ~= 20mg
benzodiazepine_conversion("alprazolam", 1, "diazepam")

# Example 2: Convert 2mg Lorazepam to Clonazepam
# 1mg Lorazepam ~= 0.5mg Clonazepam, so 2mg ~= 1mg
benzodiazepine_conversion("lorazepam", 2, "clonazepam")

Berg Balance Scale (BBS)

Description

Calculates the Berg Balance Scale score, a widely used clinical test of a person's static and dynamic balance abilities. The scale consists of 14 functional tasks, each scored from 0 to 4. The total score (0-56) indicates the patient's risk of falling.

Usage

berg_balance_scale(sit_to_stand, standing_unsupported, sitting_unsupported,
                   stand_to_sit, transfers, standing_eyes_closed,
                   standing_feet_together, reaching_forward, retrieving_object,
                   turning_to_look_behind, turning_360, stool_stepping,
                   tandem_standing, one_leg_stand)

Arguments

Value

A list containing:

References

Berg KO, Wood-Dauphinee SL, Williams JI, Maki B. Measuring balance in the elderly: validation of an instrument. Can J Public Health. 1992;83 Suppl 2:S7-11.

Examples

# Example 1: Low Fall Risk
# Most items scored 4 or 3
berg_balance_scale(4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 4, 2, 2)

# Example 2: High Fall Risk
# Most items scored 0 or 1
berg_balance_scale(1, 1, 2, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0)

Berlin Criteria for Acute Respiratory Distress Syndrome (ARDS)

Description

Applies the Berlin Definition to diagnostic criteria for ARDS. It evaluates timing, chest imaging, origin of edema, and oxygenation status to classify patients into Mild, Moderate, or Severe ARDS.

Usage

berlin_ards_criteria(timing_within_1_week, bilateral_opacities,
                     edema_not_cardiac, pao2_fio2_ratio, peep_or_cpap)

Arguments

Value

A list containing:

References

The ARDS Definition Task Force. Acute Respiratory Distress Syndrome: The Berlin Definition. JAMA. 2012;307(23):2526-2533. doi:10.1001/jama.2012.5669

Examples

# Example 1: Severe ARDS
# Acute onset, Bilateral infiltrates, Non-cardiac, PF Ratio 80, PEEP 10
berlin_ards_criteria(1, 1, 1, 80, 10)

# Example 2: Not ARDS (PF Ratio too high)
# PF Ratio 350
berlin_ards_criteria(1, 1, 1, 350, 5)

# Example 3: Not ARDS (PEEP too low)
# PEEP 2
berlin_ards_criteria(1, 1, 1, 150, 2)

Hour-Specific Risk for Neonatal Hyperbilirubinemia (Bhutani Nomogram)

Description

Calculates the risk zone for neonatal hyperbilirubinemia based on the Bhutani Nomogram. This tool assesses the risk of developing severe hyperbilirubinemia in infants >= 35 weeks gestational age and birth weight >= 2000g or >= 2500g depending on specific guideline versions. It uses postnatal age in hours and Total Serum Bilirubin (TSB) to stratify infants into Low, Low-Intermediate, High-Intermediate, or High Risk zones.

Usage

bhutani_nomogram_risk(age_hours, total_bilirubin, units = "mg/dL")

Arguments

Value

A list containing:

References

Bhutani VK, Johnson L, Sivieri EM. Predictive ability of a predischarge hour-specific serum bilirubin for subsequent significant hyperbilirubinemia in healthy term and near-term newborns. Pediatrics. 1999;103(1):6-14. doi:10.1542/peds.103.1.6

Examples

# Example 1: High Risk
# 24 hours old, Bilirubin 9.0 mg/dL (High Risk starts > ~8.0)
bhutani_nomogram_risk(24, 9.0)

# Example 2: Low Risk
# 48 hours old, Bilirubin 6.0 mg/dL (< 40th percentile of ~9.3)
bhutani_nomogram_risk(48, 6.0)

Bicarbonate Deficit Calculator

Description

Calculates the total body bicarbonate deficit in patients with metabolic acidosis. This estimate is used to guide replacement therapy with Sodium Bicarbonate. The formula relies on the apparent volume of distribution of bicarbonate (typically estimated as 40-50% of body weight).

Usage

bicarbonate_deficit(weight_kg, current_hco3, desired_hco3 = 24,
                    volume_distribution_fraction = 0.4)

Arguments

Value

A list containing:

References

Adrogué HJ, Madias NE. Management of life-threatening acid-base disorders. N Engl J Med. 1998;338(1):26-34. doi:10.1056/NEJM199801013380106

Examples

# Example 1: Severe Acidosis
# 70kg patient, current HCO3 8, target 15 (partial correction)
# Deficit = 0.4 * 70 * (15 - 8) = 196 mEq
bicarbonate_deficit(70, 8, 15)

# Example 2: Full Correction Calculation
# 80kg patient, current 12, target 24
# Deficit = 0.4 * 80 * (24 - 12) = 384 mEq
bicarbonate_deficit(80, 12, 24)

Binet Staging System for Chronic Lymphocytic Leukemia (CLL)

Description

Classifies Chronic Lymphocytic Leukemia (CLL) into three prognostic stages (A, B, or C) based on the number of involved lymphoid areas and the presence of anemia or thrombocytopenia.

Usage

binet_staging_cll(hemoglobin_g_dl, platelets_per_ul, cervical_nodes,
                  axillary_nodes, inguinal_nodes, liver_spleen_palpable)

Arguments

Value

A list containing:

References

Binet JL, Auquier A, Dighiero G, et al. A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis. Cancer. 1981;48(1):198-206. doi:10.1002/1097-0142(19810701)48:1<198::aid-cncr2820480131>3.0.co;2-v

Examples

# Example 1: Stage C
# Hb 9.0 (Anemia), Plt 150k, 1 area involved
binet_staging_cll(9.0, 150000, 1, 0, 0, 0)

# Example 2: Stage A
# Hb 14, Plt 200k, 1 area involved (Cervical)
binet_staging_cll(14, 200000, 1, 0, 0, 0)

# Example 3: Stage B
# Hb 13, Plt 180k, 3 areas involved (Cervical, Axillary, Spleen)
binet_staging_cll(13, 180000, 1, 1, 0, 1)

BISAP Score for Pancreatitis Mortality

Description

Calculates the BISAP (Bedside Index for Severity in Acute Pancreatitis) score to estimate in-hospital mortality risk. This simple 5-point scoring system uses data available within the first 24 hours of admission.

Usage

bisap_score(bun, impaired_mental_status, sirs_criteria_met, age,
            pleural_effusion, bun_units = "mg/dL")

Arguments

Value

A list containing:

References

Wu BU, Johannes RS, Sun X, Tabak Y, Conwell DL, Banks PA. The early prediction of mortality in acute pancreatitis: a large population-based study. Gut. 2008;57(12):1698-1703. doi:10.1136/gut.2008.152702

Examples

# Example 1: High Risk
# BUN 30 (+1), Altered mental status (+1), SIRS (+1), Age 65 (+1), No effusion
# Score = 4
bisap_score(30, 1, 1, 65, 0)

# Example 2: Low Risk
# BUN 15 (0), Normal mental status (0), No SIRS (0), Age 40 (0), No effusion (0)
# Score = 0
bisap_score(15, 0, 0, 40, 0)

Bishop Score for Vaginal Delivery / Induction of Labor

Description

Calculates the Bishop Score to assess cervical readiness for induction of labor. The score evaluates five parameters: Dilation, Effacement, Station, Consistency, and Position of the cervix. A higher score indicates a more favorable cervix and a higher likelihood of successful vaginal delivery.

Usage

bishop_score_induction(dilation_cm, effacement_percent, station,
                       consistency, position)

Arguments

Value

A list containing:

References

Bishop EH. Pelvic scoring for elective induction. Obstet Gynecol. 1964;24:266-268.

Examples

# Example 1: Unfavorable Cervix
# Closed, 30% effaced, -3 station, Firm, Posterior
# Score = 0
bishop_score_induction(0, 30, -3, "firm", "posterior")

# Example 2: Favorable Cervix
# 4cm dilated (2), 60% effaced (2), -1 station (2), Soft (2), Anterior (2)
# Score = 10
bishop_score_induction(4, 60, -1, "soft", "anterior")

Blast Lung Injury Severity Score (BLISS)

Description

Calculates the Blast Lung Injury Severity Score to stratify patients with primary blast lung injury (PBLI). The score guides ventilator management strategies based on the severity of lung injury, assessing oxygenation (PaO2/FiO2), chest x-ray findings, and the presence of a bronchopleural fistula.

Usage

blast_lung_injury_severity(pao2_fio2_ratio, chest_xray_findings,
                           bronchopleural_fistula)

Arguments

Value

A list containing:

References

Pizov R, Oppenheim-Eden A, Matot I, et al. Blast lung injury from explosion of a bomb in a civilian bus. Chest. 1999;115(1):165-172. doi:10.1378/chest.115.1.165

Examples

# Example 1: Moderate Injury
# PF 150 (2 pts), Bilateral infiltrates (2 pts), No BPF (0 pts)
# Score = 4
blast_lung_injury_severity(150, 1, 0)

# Example 2: Severe Injury
# PF 50 (4 pts), Massive bilateral (3 pts), BPF present (1 pt)
# Score = 8
blast_lung_injury_severity(50, 2, 1)

Estimated Blood Volume (Nadler Equation)

Description

Calculates the estimated total blood volume in Liters using the Nadler equation. This method is considered more accurate than simple weight-based estimates (e.g., 70 mL/kg) because it accounts for the patient's height and sex. Accurate blood volume estimation is critical for procedures like plasmapheresis or assessing volume status in shock.

Usage

blood_volume_calculation(sex, height_cm, weight_kg)

Arguments

Value

A list containing:

References

Nadler SB, Hidalgo JH, Bloch T. Prediction of blood volume in normal human adults. Surgery. 1962;51(2):224-232.

Examples

# Example 1: Male
# 180cm, 80kg
# BV = (0.3669 * 1.8^3) + (0.03219 * 80) + 0.6041 = ~5.3 L
blood_volume_calculation("male", 180, 80)

# Example 2: Female
# 160cm, 60kg
# BV = (0.3561 * 1.6^3) + (0.03308 * 60) + 0.1833 = ~3.6 L
blood_volume_calculation("female", 160, 60)

BODE Index for COPD Survival

Description

Calculates the BODE Index, a multidimensional scoring system to predict mortality in patients with Chronic Obstructive Pulmonary Disease (COPD). The score evaluates Body mass index, Obstruction (FEV1), Dyspnea (mMRC), and Exercise capacity (6MWD).

Usage

bode_index_copd(bmi, fev1_percent_predicted, mmrc_scale,
                six_minute_walk_distance_m)

Arguments

Value

A list containing:

References

Celli BR, Cote CG, Marin JM, et al. The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med. 2004;350(10):1005-1012. doi:10.1056/NEJMoa021322

Examples

# Example 1: Severe COPD (High Mortality Risk)
# BMI 20 (+1), FEV1 30% (+3), mMRC 3 (+2), 6MWD 140m (+3)
# Score = 9
bode_index_copd(20, 30, 3, 140)

# Example 2: Moderate COPD
# BMI 25 (0), FEV1 55% (+1), mMRC 1 (0), 6MWD 300m (+1)
# Score = 2
bode_index_copd(25, 55, 1, 300)

Body Fluid Balance Calculator

Description

Calculates the net fluid balance over a specified period (typically 24 hours) by summing all sources of fluid intake (Input) and subtracting all sources of fluid loss (Output). This calculation is critical for monitoring hydration status in hospitalized patients, particularly those with heart failure, kidney disease, or shock.

Usage

body_fluid_balance(oral_input_ml = 0, iv_input_ml = 0, enteral_input_ml = 0,
                   other_input_ml = 0, urine_output_ml = 0, stool_output_ml = 0,
                   emesis_ng_output_ml = 0, drain_output_ml = 0,
                   insensible_loss_ml = 0)

Arguments

Value

A list containing:

References

Mazzoni D, et al. Fluid Balance. In: StatPearls. Treasure Island (FL): StatPearls Publishing. 2023.

Examples

# Example 1: Positive Balance (Fluid Overload concern)
# 2L IV, 500mL Oral, 1L Urine, no other losses
body_fluid_balance(oral_input_ml = 500, iv_input_ml = 2000, urine_output_ml = 1000)

# Example 2: Negative Balance (Dehydration/Diuresis)
# 1L Oral, 2L Urine, 500mL Insensible
body_fluid_balance(oral_input_ml = 1000, urine_output_ml = 2000, insensible_loss_ml = 500)

Body Mass Index (BMI) and Body Surface Area (BSA)

Description

Calculates the Body Mass Index (BMI) and estimates Body Surface Area (BSA) using five common formulas (Mosteller, Du Bois, Haycock, Gehan-George, and Boyd). These metrics are fundamental for dosing medications (e.g., chemotherapy), assessing nutritional status, and stratifying obesity risks.

Usage

body_mass_index_bsa(weight_kg, height_cm)

Arguments

Value

A list containing:

References

Mosteller RD. Simplified calculation of body-surface area. N Engl J Med. 1987;317(17):1098. doi:10.1056/NEJM198710223171717

Du Bois D, Du Bois EF. A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med. 1916;17(6):863-871.

Examples

# Example 1: Average Adult
# 75 kg, 175 cm
body_mass_index_bsa(75, 175)

# Example 2: Child
# 30 kg, 130 cm
body_mass_index_bsa(30, 130)

Body Roundness Index (BRI)

Description

Calculates the Body Roundness Index (BRI), a geometric measure of body composition that models the human body as an ellipse. BRI improves upon Body Mass Index (BMI) by quantifying the degree of central adiposity (visceral fat), which is a stronger predictor of metabolic syndrome, cardiovascular disease, and all-cause mortality.

Usage

body_roundness_index(waist_cm, height_cm)

Arguments

Value

A list containing:

References

Thomas DM, Bredlau C, Bosy-Westphal A, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometric model. Obesity (Silver Spring). 2013;21(11):2264-2271. doi:10.1002/oby.20408

Examples

# Example 1: Average measurements
# Waist 85 cm, Height 175 cm
body_roundness_index(85, 175)

# Example 2: High central adiposity
# Waist 110 cm, Height 170 cm
body_roundness_index(110, 170)

Ideal Body Weight (IBW) and Adjusted Body Weight (ABW)

Description

Calculates Ideal Body Weight (using the Devine Formula) and Adjusted Body Weight. These metrics are essential for calculating nutritional needs and dosing medications (e.g., aminoglycosides, acyclovir) in patients who are obese or have abnormal body composition.

Usage

body_weight_calculations(sex, height, actual_weight,
                         height_units = "in", weight_units = "kg")

Arguments

Value

A list containing:

References

Devine BJ. Gentamicin therapy. Drug Intell Clin Pharm. 1974;8:650–655.

Examples

# Example 1: Male, Normal Weight (70 in, 75 kg)
body_weight_calculations("male", 70, 75)

# Example 2: Female, Obese (160 cm, 90 kg)
# Height ~63 in. IBW = 45.5 + 2.3(3) = 52.4 kg.
# Actual 90 kg. ABW = 52.4 + 0.4(90-52.4) = 67.4 kg.
body_weight_calculations("female", 160, 90, "cm", "kg")

Behavioral Observational Pain Scale (BOPS)

Description

Calculates the Behavioral Observational Pain Scale (BOPS) score for assessing postoperative pain in children aged 1-7 years. The scale evaluates three behavioral domains: Facial Expression, Verbalization, and Body Position. A score of 3 or greater typically indicates the need for analgesic intervention.

Usage

bops_pediatric_pain(facial_expression, verbalization, body_position)

Arguments

Value

A list containing:

References

Hesselgard K, Larsson S, Romner B, Strömblad LG, Reinstrup P. Validity and reliability of the Behavioural Observational Pain Scale for postoperative pain measurement in children 1-7 years of age. Pediatr Crit Care Med. 2007;8(2):102-108. doi:10.1097/01.PCC.0000257098.32286.2D

Examples

# Example 1: Significant Pain
# Grimace (2), Crying (2), Restless (1)
# Score = 5
bops_pediatric_pain(2, 2, 1)

# Example 2: No Pain
# Smiling (0), Normal talk (0), Moving easily (0)
# Score = 0
bops_pediatric_pain(0, 0, 0)

Bova Score for Pulmonary Embolism Complications

Description

Calculates the Bova Score to stratify the risk of PE-related complications (death, hemodynamic collapse, or recurrent PE) within 30 days in normotensive patients with acute pulmonary embolism. This tool helps identify patients who, despite being hemodynamically stable at presentation, are at intermediate-to-high risk for deterioration.

Usage

bova_pe_score(sbp, heart_rate, troponin_elevated, rv_dysfunction)

Arguments

Value

A list containing:

References

Bova C, Sanchez O, Prandoni P, et al. Identification of intermediate-risk patients with acute symptomatic pulmonary embolism. Eur Heart J. 2014;35(20):1348-1356. doi:10.1093/eurheartj/ehu007

Examples

# Example 1: High Risk (Stage III)
# SBP 95 (+2), HR 115 (+1), Troponin Neg (0), RV Dysfx (+2)
# Score = 5
bova_pe_score(95, 115, 0, 1)

# Example 2: Low Risk (Stage I)
# SBP 120 (0), HR 90 (0), Troponin Pos (+2), RV Normal (0)
# Score = 2
bova_pe_score(120, 90, 1, 0)

Behavioral Pain Scale (BPS) for Intubated Patients

Description

Calculates the Behavioral Pain Scale (BPS) score to assess pain in unconscious, sedated, and intubated patients. The scale evaluates three behavioral domains: Facial Expression, Upper Limb Movements, and Compliance with Mechanical Ventilation.

Usage

bps_intubated_pain(facial_expression, upper_limb_movements, compliance_ventilation)

Arguments

Value

A list containing:

References

Payen JF, Bru O, Bosson JL, et al. Assessing pain in critically ill sedated patients by using a behavioral pain scale. Crit Care Med. 2001;29(12):2258-2263. doi:10.1097/00003246-200112000-00004

Examples

# Example 1: Significant Pain
# Grimacing (4), Fully bent arms (3), Fighting vent (3)
# Score = 10
bps_intubated_pain(4, 3, 3)

# Example 2: No Pain
# Relaxed (1), No movement (1), Tolerating vent (1)
# Score = 3
bps_intubated_pain(1, 1, 1)

Braden Score for Pressure Ulcer Risk

Description

Calculates the Braden Score, a widely used tool for assessing the risk of developing pressure ulcers (bedsores) in adult patients. The scale evaluates six subscales: sensory perception, moisture, activity, mobility, nutrition, and friction/shear. Lower scores indicate higher risk.

Usage

braden_score(sensory_perception, moisture, activity, mobility,
             nutrition, friction_shear)

Arguments

Value

A list containing:

References

Bergstrom N, Braden BJ, Laguzza A, Holman V. The Braden Scale for Predicting Pressure Sore Risk. Nurs Res. 1987;36(4):205-210.

Examples

# Example 1: High Risk Patient
# Unresponsive (1), Constantly moist (1), Bedfast (1), Immobile (1), Poor nutrition (1), Sliding (1)
# Score = 6
braden_score(1, 1, 1, 1, 1, 1)

# Example 2: Mild Risk
# Slightly limited sensory (3), Occasional moisture (3), Walks occasionally (3),
# Slightly limited mobility (3), Adequate nutrition (3), No friction (3)
# Score = 18
braden_score(3, 3, 3, 3, 3, 3)

Brain Metastasis Velocity (BMV) Model

Description

Calculates the Brain Metastasis Velocity (BMV) score, a prognostic metric used to predict overall survival in patients who develop new brain metastases (distant brain failure) after initial stereotactic radiosurgery (SRS). The score is defined as the cumulative number of new brain metastases developed since initial SRS divided by the time interval (in years) since that initial treatment.

Usage

brain_metastasis_velocity(new_mets_count, time_interval_years)

Arguments

Value

A list containing:

References

Farris M, McTyre ER, Chuong M, et al. Brain Metastasis Velocity: A Novel Prognostic Metric Predictive of Overall Survival and Freedom From Neurologic Death After Distant Brain Failure Following Upfront Radiosurgery Alone. Int J Radiat Oncol Biol Phys. 2017;98(1):131-141. doi:10.1016/j.ijrobp.2017.01.229

Examples

# Example 1: Low Risk
# 2 new mets appearing 1 year after initial SRS
# BMV = 2 / 1 = 2
brain_metastasis_velocity(2, 1.0)

# Example 2: High Risk
# 8 new mets appearing 0.5 years (6 months) after initial SRS
# BMV = 8 / 0.5 = 16
brain_metastasis_velocity(8, 0.5)

Breast-GPA for Brain Metastasis Survival

Description

Calculates the Diagnosis-Specific Graded Prognostic Assessment (Breast-GPA) score. This tool estimates the median survival of patients with breast cancer who have developed brain metastases. The score is based on tumor subtype (ER/PR/HER2), Karnofsky Performance Status (KPS), age, and the presence of extracranial metastases.

Usage

breast_cancer_brain_metastasis_gpa(subtype_er_pr_status, subtype_her2_status,
                                   kps, age, extracranial_metastases,
                                   number_of_brain_metastases)

Arguments

Value

A list containing:

References

Sperduto PW, Kased N, Roberge D, et al. Summary report on the graded prognostic assessment: an accurate and facile diagnosis-specific tool to estimate survival for patients with brain metastases. J Clin Oncol. 2012;30(4):419-425. doi:10.1200/JCO.2011.36.6972

Examples

# Example 1: Poor Prognosis (Basal Type)
# Triple Negative (0), KPS 60 (0), Age 65 (0), ECM Present (0), Multiple Mets (0)
# Score = 0
breast_cancer_brain_metastasis_gpa("negative", "negative", 60, 65, 1, 3)

# Example 2: Good Prognosis (Luminal B)
# ER+ HER2+ (2.0), KPS 90 (1.0), Age 50 (0.5), No ECM (0.5), Single Met (0.5)
# Score = 4.0
breast_cancer_brain_metastasis_gpa("positive", "positive", 90, 50, 0, 1)

Brescia-COVID Respiratory Severity Scale (BCRSS)

Description

Implements the Brescia-COVID Respiratory Severity Scale Algorithm. This stepwise approach helps triage COVID-19 patients based on respiratory symptoms, oxygenation status, and chest imaging to determine the appropriate level of respiratory support (Observation, O2, or NIV/Intubation).

Usage

brescia_covid_severity(wheezing_present, rr_gt_22_or_desat,
                       worsening_cxr_or_unstable)

Arguments

Value

A list containing:

References

Piva S, Filippini M, Turla F, et al. Clinical presentation and initial management of COVID-19 patients in Brescia, Italy. Anaesthesia. 2020.

Examples

# Example 1: High Severity (Need NIV/ICU)
# No wheeze, Tachypneic, Worsening despite O2
brescia_covid_severity(0, 1, 1)

# Example 2: Mild/Observation
# No wheeze, Normal RR/SpO2
brescia_covid_severity(0, 0, 0)

Brief Addiction Monitor (BAM)

Description

Calculates domain scores for the Brief Addiction Monitor (BAM), a 17-item instrument used to monitor progress in recovery from substance use disorders. It evaluates three key domains over the past 30 days: Substance Use, Risk Factors, and Protective Factors.

Usage

brief_addiction_monitor(alcohol_days, drug_days, marijuana_days,
                        risk_health, risk_sleep, risk_anxiety, risk_depression,
                        risk_cravings, risk_situations,
                        prot_confidence, prot_support, prot_spiritual,
                        prot_work, prot_income, prot_family,
                        prot_self_help_days, prot_commitment)

Arguments

Value

A list containing:

References

Cacciola JS, Alterman AI, Dephilippis D, et al. Development and initial evaluation of the Brief Addiction Monitor (BAM). J Subst Abuse Treat. 2013;44(3):256-263. doi:10.1016/j.jsat.2012.07.013

Examples

# Example 1: High Risk Profile
# 15 days alcohol, High cravings (4), Low confidence (1)
brief_addiction_monitor(15, 0, 0, 2, 2, 3, 3, 4, 3, 1, 1, 0, 2, 1, 1, 0, 1)

# Example 2: Strong Recovery Profile
# 0 days use, Low risk factors, High protection, 12 days self-help
brief_addiction_monitor(0, 0, 0, 0, 0, 1, 0, 1, 0, 4, 4, 3, 4, 3, 4, 12, 4)

Brief Alcohol Withdrawal Scale (BAWS)

Description

Calculates the Brief Alcohol Withdrawal Scale (BAWS) score, a streamlined tool for assessing the severity of alcohol withdrawal syndrome (AWS) and guiding symptom-triggered benzodiazepine therapy. The scale evaluates 5 clinical items, each scored from 0 to 3.

Usage

brief_alcohol_withdrawal_scale(tremor, sweating, agitation, orientation,
                               hallucinations)

Arguments

Value

A list containing:

References

Maldonado JR, Sher Y, Das S, et al. A prospective validation study of the prediction of alcohol withdrawal severity scale (PAWSS) in medically ill inpatients: a new scale for the prediction of complicated alcohol withdrawal syndrome. Alcohol Alcohol. 2015;50(5):509-518. (Contextual reference for withdrawal scales). MDCalc. Brief Alcohol Withdrawal Scale (BAWS).

Examples

# Example 1: Moderate Withdrawal
# Tremor (1), Sweating (1), Agitated (1), Oriented (0), No Hallucinations (0)
# Score = 3
brief_alcohol_withdrawal_scale(1, 1, 1, 0, 0)

# Example 2: Severe Withdrawal
# Tremor (2), Drenching Sweat (3), Agitated (2), Disoriented time/place (2), Mild Hallucinations (1)
# Score = 10
brief_alcohol_withdrawal_scale(2, 3, 2, 2, 1)

Bristol Stool Form Scale

Description

Retrieves the description and clinical interpretation for the Bristol Stool Form Scale (BSFS). This diagnostic medical tool classifies the form of human faeces into seven categories, which correlate with colonic transit time.

Usage

bristol_stool_scale(stool_type)

Arguments

Value

A list containing:

References

Lewis SJ, Heaton KW. Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol. 1997;32(9):920-924. doi:10.3109/00365529709011203

Examples

# Example 1: Normal Stool
bristol_stool_scale(4)

# Example 2: Severe Constipation
bristol_stool_scale(1)

# Example 3: Diarrhea
bristol_stool_scale(7)

Brief Resolved Unexplained Events 2.0 (BRUE 2.0) Criteria

Description

Applies the BRUE 2.0 criteria (based on the 2016 AAP Guidelines and subsequent validation) to risk-stratify infants presenting with a Brief Resolved Unexplained Event. The tool identifies "Lower-Risk" infants who require minimal workup versus "Higher-Risk" infants who may benefit from further observation or diagnostic testing. A BRUE is defined as a sudden, brief, resolved episode of cyanosis/pallor, absent/irregular breathing, marked tone change, or altered responsiveness in an infant < 1 year, with no explanation after history and physical exam.

Usage

brue_2_0_criteria(age_days, gestational_age_weeks, duration_less_than_1_min,
                  cpr_performed, recurring_event, concerning_history,
                  concerning_exam)

Arguments

Value

A list containing:

References

Tieder JS, Bonkowsky JL, Etzel RA, et al. Clinical Practice Guideline: Brief Resolved Unexplained Events (Formerly Apparent Life-Threatening Events) and Evaluation of Lower-Risk Infants. Pediatrics. 2016;138(1):e20160590. doi:10.1542/peds.2016-0590

Examples

# Example 1: Lower-Risk BRUE
# 4-month-old (120d), term (40w), brief event, no CPR, first time, normal H&P
brue_2_0_criteria(120, 40, 1, 0, 0, 0, 0)

# Example 2: Higher-Risk BRUE (Age)
# 45-day-old, otherwise low risk
brue_2_0_criteria(45, 40, 1, 0, 0, 0, 0)

# Example 3: Higher-Risk BRUE (Duration & Recurrence)
# 3-month-old, event > 1 min, recurring
brue_2_0_criteria(90, 39, 0, 0, 1, 0, 0)

Brief Resolved Unexplained Event (BRUE) Risk Stratification

Description

Stratifies infants presenting with a Brief Resolved Unexplained Event (BRUE) into "Low Risk" or "Not Low Risk" categories based on the 2016 American Academy of Pediatrics (AAP) guidelines. Low-risk classification requires meeting rigorous age, history, and event characteristics criteria, allowing for less invasive management.

Usage

brue_risk_stratification(age_days, gestational_age_weeks,
                         duration_less_than_1_min, cpr_performed,
                         recurring_event, concerning_history_or_exam)

Arguments

Value

A list containing:

References

Tieder JS, Bonkowsky JL, Etzel RA, et al. Clinical Practice Guideline: Brief Resolved Unexplained Events (Formerly Apparent Life-Threatening Events) and Evaluation of Lower-Risk Infants. Pediatrics. 2016;138(1):e20160590. doi:10.1542/peds.2016-0590

Examples

# Example 1: Low Risk
# 90 days old, born at 40 weeks, short event, no CPR, first time, normal exam
brue_risk_stratification(90, 40, 1, 0, 0, 0)

# Example 2: Not Low Risk (Age)
# 45 days old (Too young), otherwise benign
brue_risk_stratification(45, 40, 1, 0, 0, 0)

# Example 3: Not Low Risk (Prematurity)
# 70 days old (Chronological OK), born at 30 weeks (GA < 32)
brue_risk_stratification(70, 30, 1, 0, 0, 0)

Brugada Criteria for Ventricular Tachycardia

Description

Implements the Brugada Algorithm to differentiate Ventricular Tachycardia (VT) from Supraventricular Tachycardia (SVT) with aberrancy in patients with wide complex tachycardia. The algorithm follows a four-step sequential evaluation. If any step is positive, the diagnosis is VT. If all steps are negative, the diagnosis defaults to SVT with aberrancy.

Usage

brugada_criteria_vt(absent_rs_precordial, rs_interval_gt_100,
                    av_dissociation, morphology_criteria_vt)

Arguments

Value

A list containing:

References

Brugada P, Brugada J, Mont L, Smeets J, Andries EW. A new approach to the differential diagnosis of a regular tachycardia with a wide QRS complex. Circulation. 1991;83(5):1649-1659. doi:10.1161/01.cir.83.5.1649

Examples

# Example 1: VT diagnosed at Step 2
# RS complex present (0), but RS interval > 100ms (1)
brugada_criteria_vt(0, 1, 0, 0)

# Example 2: SVT with Aberrancy
# All criteria negative
brugada_criteria_vt(0, 0, 0, 0)

BUN/Creatinine Ratio

Description

Calculates the Blood Urea Nitrogen (BUN) to Creatinine ratio. This ratio helps differentiate between prerenal, intrinsic renal, and postrenal causes of acute kidney injury (AKI). The calculation standardizes inputs to mg/dL before computing the ratio to ensure consistent application of the 10:1 and 20:1 clinical thresholds.

Usage

bun_creatinine_ratio(bun, creatinine, bun_units = "mg/dL", creatinine_units = "mg/dL")

Arguments

Value

A list containing:

References

Dossetor JB. Creatininemia versus uremia. The relative significance of blood urea nitrogen and serum creatinine concentrations in azotemia. Ann Intern Med. 1966;65(6):1287-1299. doi:10.7326/0003-4819-65-6-1287

Examples

# Example 1: Prerenal Azotemia (Dehydration)
# BUN 40 mg/dL, Cr 1.0 mg/dL
# Ratio = 40 (>20)
bun_creatinine_ratio(40, 1.0)

# Example 2: Intrinsic Renal Disease (ATN)
# Urea 10 mmol/L, Cr 300 umol/L
# BUN = 10 * 2.8 = 28 mg/dL
# Cr = 300 / 88.4 = 3.39 mg/dL
# Ratio = 28 / 3.39 = 8.2 (<10)
bun_creatinine_ratio(10, 300, "mmol/L", "umol/L")

Burch-Wartofsky Point Scale (BWPS) for Thyrotoxicosis

Description

Calculates the Burch-Wartofsky Point Scale score to estimate the likelihood of Thyroid Storm in patients presenting with thyrotoxicosis. The scale aggregates scores from thermoregulatory dysfunction, central nervous system effects, gastrointestinal-hepatic dysfunction, cardiovascular tachycardia, heart failure, atrial fibrillation, and the presence of a precipitating history.

Usage

burch_wartofsky_score(temp_c, cns_scale, gi_scale, heart_rate, chf_scale,
                      afib_present, precipitating_event)

Arguments

Value

A list containing:

References

Burch HB, Wartofsky L. Life-threatening thyrotoxicosis. Thyroid storm. Endocrinol Metab Clin North Am. 1993;22(2):263-277.

Examples

# Example 1: Highly Suggestive of Storm
# Temp 39.5 (+25), Mod CNS (+20), HR 145 (+25), No GI, No CHF, No Afib, Event (+10)
# Score = 80
burch_wartofsky_score(39.5, 2, 0, 145, 0, 0, 1)

# Example 2: Impending Storm
# Temp 38.0 (+10), Mild CNS (+10), HR 115 (+10), No other signs
# Score = 30
burch_wartofsky_score(38.0, 1, 0, 115, 0, 0, 0)

Bush-Francis Catatonia Rating Scale (BFCRS)

Description

Calculates the total score for the Bush-Francis Catatonia Rating Scale, the standard instrument for diagnosing and assessing the severity of catatonia. It evaluates 23 items on a scale of 0 to 3. The first 14 items constitute the screening instrument; presence of 2 or more of these items warrants a full evaluation.

Usage

bush_francis_catatonia_scale(excitement, immobility, mutism, staring, posturing,
                             grimacing, echophenomena, stereotypy, mannerisms,
                             verbigeration, rigidity, negativism, waxy_flexibility,
                             withdrawal, impulsivity, automatic_obedience, mitgehen,
                             gegenhalten, ambitendency, grasp_reflex, perseverance,
                             combativeness, autonomic_abnormality)

Arguments

Value

A list containing:

References

Bush G, Fink M, Petrides G, Dowling F, Francis A. Catatonia. I. Rating scale and standardized examination. Acta Psychiatr Scand. 1996;93(2):129-36. doi:10.1111/j.1600-0447.1996.tb09814.x

Examples

# Example 1: Positive Screen, High Severity
# Immobility(3), Mutism(3), Staring(2), Posturing(2), Rigidity(3)
# Others 0
bush_francis_catatonia_scale(0, 3, 3, 2, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0,
                             0, 0, 0, 0, 0, 0, 0, 0, 0)

# Example 2: Negative Screen
# All 0
bush_francis_catatonia_scale(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                             0, 0, 0, 0, 0, 0, 0, 0, 0)

BWH Egg Freezing Counseling Tool (EFCT)

Description

Calculates the likelihood of achieving at least one, two, or three live births from elective oocyte cryopreservation (egg freezing). The model is based on data from Brigham and Women's Hospital (BWH) and accounts for the patient's age at retrieval and the number of mature oocytes frozen. It estimates the probability based on age-specific oocyte-to-blastocyst and blastocyst-to-euploidy conversion rates.

Usage

bwh_egg_freezing_tool(age, num_mature_eggs)

Arguments

Value

A list containing:

References

Goldman RH, Racowsky C, Farland LV, et al. Predicting the likelihood of live birth for elective oocyte cryopreservation: a counseling tool for physicians and patients. Hum Reprod. 2017;32(4):853-859. doi:10.1093/humrep/dex008

Examples

# Example 1: 35-year-old with 10 eggs
# Expected: ~69% chance of at least 1 live birth
bwh_egg_freezing_tool(35, 10)

# Example 2: 40-year-old with 20 eggs
# Expected: ~51% chance of at least 1 live birth
bwh_egg_freezing_tool(40, 20)

C-Peptide-Glucose Ratio (CPGR)

Description

Calculates the C-Peptide-Glucose Ratio (CPGR), a metric used to estimate endogenous beta-cell function. This ratio helps differentiate between clinical forms of diabetes, particularly distinguishing Type 1 Diabetes (insulin deficiency) from Type 2 Diabetes or Maturity Onset Diabetes of the Young (MODY) (insulin resistance/preservation).

Usage

c_peptide_glucose_ratio(c_peptide, glucose,
                        c_peptide_units = "ng/mL",
                        glucose_units = "mg/dL")

Arguments

Details

The ratio is calculated using the formula:

CPGR = \frac{C\text{-}Peptide (ng/mL)}{Glucose (mg/dL)} \times 100

While cutoffs vary by study and specific clinical question (e.g., distinguishing HNF1A-MODY from T1DM), a value of 1.7 or higher is often cited as an indicator of preserved beta-cell function indicative of T2DM or MODY.

Value

A list containing:

References

Besser RE, Shields BM, anatole S, et al. Differentiation of Type 1 diabetes, Type 2 diabetes and MODY using C-peptide. Diabetes Care. 2011;34:e53.

Examples

# Example 1: Type 1 Diabetes (Low C-Peptide relative to Glucose)
# C-Pep 0.5 ng/mL, Glucose 200 mg/dL
# Ratio = (0.5 / 200) * 100 = 0.25
c_peptide_glucose_ratio(0.5, 200)

# Example 2: Type 2 Diabetes / Insulin Resistance
# C-Pep 4.0 ng/mL, Glucose 150 mg/dL
# Ratio = (4.0 / 150) * 100 = 2.67
c_peptide_glucose_ratio(4.0, 150)

CAGE Questions for Alcohol Use

Description

Calculates the score for the CAGE questionnaire, a widely used screening tool for detecting alcohol dependence and abuse. The acronym is derived from the four questions: Cut down, Annoyed, Guilty, and Eye-opener. Two or more "Yes" answers typically indicate a positive screen requiring further assessment.

Usage

cage_alcohol_screen(cut_down, annoyed, guilty, eye_opener)

Arguments

Value

A list containing:

References

Ewing JA. Detecting alcoholism. The CAGE questionnaire. JAMA. 1984;252(14):1905-1907. doi:10.1001/jama.252.14.1905

Examples

# Example 1: Positive Screen
# Felt need to cut down (1), Annoyed by criticism (1), No guilt (0), No eye opener (0)
# Score = 2
cage_alcohol_screen(1, 1, 0, 0)

# Example 2: Negative Screen
# No affirmative answers
# Score = 0
cage_alcohol_screen(0, 0, 0, 0)

CAHP (Cardiac Arrest Hospital Prognosis) Score

Description

Calculates the CAHP score to predict neurological outcome (CPC 3-5) in patients admitted to the ICU after out-of-hospital cardiac arrest (OHCA). The score is typically used for patients treated with targeted temperature management (TTM). It stratifies patients into Low, Intermediate, and High risk for poor neurological outcome.

Usage

cahp_cardiac_arrest_score(age, location_home, initial_rhythm_shockable,
                          no_flow_minutes, low_flow_minutes,
                          epinephrine_dose_mg, arterial_ph)

Arguments

Value

A list containing:

References

Maupain C, Bougouin W, Lamhaut L, et al. The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after out-of-hospital cardiac arrest. Eur Heart J. 2016;37(42):3222-3231. doi:10.1093/eurheartj/ehv556

Examples

# Example 1: Good Prognosis
# 40yo (0), Public arrest (0), Shockable (0), No flow 0 (0), Low flow 10 (0), 0 Epi (0), pH 7.4 (0)
# Score = 0
cahp_cardiac_arrest_score(40, 0, 1, 0, 10, 0, 7.4)

# Example 2: Poor Prognosis
# 80yo (53), Home (16), Asystole (27), No flow 5 (29), Low flow 40 (32), 3mg Epi (32), pH 7.1 (25)
# Score = 53 + 16 + 27 + 29 + 32 + 32 + 25 = 214
cahp_cardiac_arrest_score(80, 1, 0, 5, 40, 3, 7.1)

Calcium Correction for Hypoalbuminemia (Payne's Formula)

Description

Calculates the corrected serum calcium level in patients with hypoalbuminemia. Since approximately 40-45% of serum calcium is bound to albumin, low albumin levels can result in a falsely low total serum calcium reading despite normal ionized calcium levels. The formula adjusts the total calcium to estimate what the level would be if albumin were normal (4.0 g/dL).

Usage

calcium_correction_hypoalbuminemia(calcium, albumin,
                                   calcium_units = "mg/dL",
                                   albumin_units = "g/dL")

Arguments

Value

A list containing:

References

Payne RB, Little AJ, Williams RB, Milner JR. Interpretation of serum calcium in patients with abnormal serum proteins. Br Med J. 1973;4(5893):643-646. doi:10.1136/bmj.4.5893.643

Examples

# Example 1: Hypoalbuminemia masking hypercalcemia
# Calcium 10.5 mg/dL (High normal), Albumin 2.5 g/dL (Low)
# Corrected = 10.5 + 0.8 * (4.0 - 2.5) = 11.7 (Hypercalcemia)
calcium_correction_hypoalbuminemia(10.5, 2.5)

# Example 2: SI Units
# Calcium 2.2 mmol/L, Albumin 30 g/L
calcium_correction_hypoalbuminemia(2.2, 30, "mmol/L", "g/L")

Serum Anion Gap

Description

Calculates the Serum Anion Gap (AG) to evaluate metabolic acidosis. The Anion Gap represents the difference between measured cations (Sodium) and measured anions (Chloride and Bicarbonate). An optional calculation for the Albumin-Corrected Anion Gap is included, which adjusts for hypoalbuminemia, a common cause of a falsely low anion gap.

Usage

calculate_anion_gap(sodium, chloride, bicarbonate, albumin = NULL)

Arguments

Details

The basic formula is:

AG = Na - (Cl + HCO3)

The Albumin-Corrected Anion Gap formula is:

Corrected AG = AG + (2.5 \times (4.4 - Albumin))

Normal Anion Gap range is typically considered 4-12 mEq/L, though this varies by laboratory assay.

Value

A list containing:

References

Emmett M, Narins RG. Clinical use of the anion gap. Medicine (Baltimore). 1977;56(1):38-54. Figge J, Jabor A, Kazda A, Fencl V. Anion gap and hypoalbuminemia. Crit Care Med. 1998;26(11):1807-1810.

Examples

calculate_anion_gap(sodium = 140, chloride = 100, bicarbonate = 24)

calculate_anion_gap(sodium = 140, chloride = 100, bicarbonate = 24, albumin = 2.5)

Expanded Disability Status Scale (EDSS) Calculator

Description

Calculates the EDSS score based on Kurtzke's Functional Systems Scores (FSS) and ambulation status. The scale ranges from 0.0 (normal) to 10.0 (death). Steps 0-4.5 are determined by FSS combinations, while steps 5.0-9.5 are defined primarily by impairment to ambulation.

Usage

calculate_edss(pyramidal, cerebellar, brainstem, sensory, bowel_bladder,
               visual, cerebral, ambulation_code)

Arguments

Value

A list containing:

References

Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33(11):1444-1452. doi:10.1212/wnl.33.11.1444

Examples

# Example 1: Mild Disability (Ambulatory)
# Pyramidal 2, others 0/1, Walks >500m
calculate_edss(2, 1, 0, 1, 0, 0, 0, 0)

# Example 2: Moderate Disability (Walking impaired)
# Pyramidal 3, Cerebellar 2, Walks 150m without aid
# Code 3 (>100m no aid) corresponds to 5.5
calculate_edss(3, 2, 1, 0, 1, 0, 0, 3)

# Example 3: Severe Disability (Wheelchair)
# Multiple high FSS, Wheelchair restricted (can transfer)
# Code 6 -> 7.0
calculate_edss(4, 3, 2, 3, 3, 1, 1, 6)

Fractional Excretion of Sodium (FENa)

Description

Calculates the Fractional Excretion of Sodium (FENa) to help differentiate between prerenal azotemia and acute tubular necrosis (ATN) in patients with oliguric acute kidney injury.

Usage

calculate_fena(serum_sodium, serum_creatinine, urine_sodium, urine_creatinine)

Arguments

Value

A list containing:

References

Espinel CH. The FeNa test. Use in the differential diagnosis of acute renal failure. JAMA. 1976;236(16):1795-1797. doi:10.1001/jama.1976.03270170035022

Examples

# Example 1: Prerenal (Dehydration)
# SNa 140, SCr 1.5, UNa 10, UCr 100
# FENa = (10 * 1.5) / (140 * 100) * 100 = 0.11%
calculate_fena(140, 1.5, 10, 100)

# Example 2: Intrinsic (ATN)
# SNa 135, SCr 2.0, UNa 40, UCr 40
# FENa = (40 * 2.0) / (135 * 40) * 100 = 1.48% (Indeterminate range, close to ATN)
calculate_fena(135, 2.0, 40, 40)

Fractional Excretion of Urea (FEUrea)

Description

Calculates the Fractional Excretion of Urea (FEUrea), a diagnostic index used to differentiate between prerenal azotemia and intrinsic acute kidney injury (specifically Acute Tubular Necrosis, ATN). FEUrea is particularly useful when the patient is on diuretics, which can alter sodium excretion and render the FENa test unreliable.

Usage

calculate_feurea(serum_urea, serum_creatinine, urine_urea, urine_creatinine,
                 urea_units = "mg/dL", creatinine_units = "mg/dL")

Arguments

Value

A list containing:

References

Carvounis CP, Nisar S, Guro-Razuman S. Significance of the fractional excretion of urea in the differential diagnosis of acute renal failure. Kidney Int. 2002;62(6):2223-2229. doi:10.1046/j.1523-1755.2002.00683.x

Examples

# Example 1: Prerenal (on diuretics)
# Serum Urea 50 mg/dL, Serum Cr 1.5 mg/dL, Urine Urea 200 mg/dL, Urine Cr 100 mg/dL
# FEUrea = (200 * 1.5) / (50 * 100) * 100 = 6
calculate_feurea(50, 1.5, 200, 100)

# Example 2: Intrinsic (ATN)
# Serum Urea 40, Serum Cr 2.0, Urine Urea 100, Urine Cr 40
# FEUrea = (100 * 2.0) / (40 * 40) * 100 = 12.5
# (100*2)/(40*40) = 200/1600 = 0.125 * 100 = 12.5
# let's try values that give >50
# Intrinsic: U-Urea 300, U-Cr 30, S-Urea 60, S-Cr 2.0
# FEUrea = (300 * 2.0) / (60 * 30) * 100 = 600 / 1800 = 33
# Try U-Urea 600: (600*2)/(60*30) = 1200/1800 = 66
calculate_feurea(60, 2.0, 600, 30)

LDL Calculated (Friedewald Equation)

Description

Calculates Low-Density Lipoprotein (LDL) cholesterol using the Friedewald equation. This formula estimates LDL based on total cholesterol, HDL cholesterol, and triglycerides. It is the standard method for reporting LDL when direct measurement is not performed, provided triglycerides are not significantly elevated.

Usage

calculate_ldl(total_cholesterol, hdl_cholesterol, triglycerides, units = "mg/dL")

Arguments

Details

The formula used depends on the units:

• mg/dL: LDL = Total Chol - HDL - (Triglycerides / 5)

• mmol/L: LDL = Total Chol - HDL - (Triglycerides / 2.2)

The equation is generally considered invalid if triglycerides exceed 400 mg/dL (4.5 mmol/L).

Value

A list containing:

References

Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499-502.

Examples

# Example 1: Standard Calculation (mg/dL)
# Total 200, HDL 50, Trigs 150
# LDL = 200 - 50 - (150/5) = 120
calculate_ldl(200, 50, 150)

# Example 2: High Triglycerides
# Total 250, HDL 40, Trigs 500
calculate_ldl(250, 40, 500)

Mean Arterial Pressure (MAP)

Description

Calculates the Mean Arterial Pressure (MAP), which represents the average arterial pressure during a single cardiac cycle. It is considered a better indicator of perfusion to vital organs than systolic blood pressure alone.

Usage

calculate_map(systolic_bp, diastolic_bp)

Arguments

Details

The formula used is:

MAP = \frac{SBP + (2 \times DBP)}{3}

Value

A list containing:

References

Geddes LA, Cheever E, Tacker WA, et al. The direct measurement of mean blood pressure in the canine. Cardiovasc Res Ctr Bull. 1976;15(4):99-103.

Examples

# Example 1: Normal BP
# 120/80
calculate_map(120, 80)

# Example 2: Hypotension
# 85/50
calculate_map(85, 50)

Model for End-Stage Liver Disease (MELD) / MELD-Na

Description

Calculates the MELD-Na score, used to prioritize candidates for liver transplantation and predict 3-month mortality in patients with end-stage liver disease. This function implements the 2016 OPTN/UNOS update which incorporates serum sodium for patients with an initial MELD > 11.

Usage

calculate_meld_score(bilirubin, inr, creatinine, sodium, on_dialysis = 0,
                     bilirubin_units = "mg/dL", creatinine_units = "mg/dL")

Arguments

Value

A list containing:

References

Kamath PS, Wiesner RH, Malinchoc M, et al. A model to predict survival in patients with end-stage liver disease. Hepatology. 2001;33(2):464-470. Kim WR, Biggins SW, Kremers WK, et al. Hyponatremia and mortality among patients on the liver-transplant waiting list. N Engl J Med. 2008;359(10):1018-1026.

Examples

# Example 1: MELD-Na Adjustment Applied
# Bili 2.0, INR 1.5, Cr 1.5, Na 125 (Low Na increases score)
calculate_meld_score(2.0, 1.5, 1.5, 125, 0)

# Example 2: Standard MELD (Score <= 11)
# Bili 1.0, INR 1.0, Cr 1.0, Na 135
# Score = 6
calculate_meld_score(1.0, 1.0, 1.0, 135, 0)

Morphine Milligram Equivalents (MME) Calculator

Description

Calculates the total daily Morphine Milligram Equivalents (MME) for a patient taking one or more opioid medications. The conversion factors are based on the 2016 CDC Guidelines for Prescribing Opioids for Chronic Pain. This metric helps clinicians assess the cumulative potency of an opioid regimen to mitigate overdose risk.

Usage

calculate_mme(codeine_mg_day = 0, fentanyl_transdermal_mcg_hr = 0,
              hydrocodone_mg_day = 0, hydromorphone_mg_day = 0,
              methadone_mg_day = 0, morphine_mg_day = 0,
              oxycodone_mg_day = 0, oxymorphone_mg_day = 0,
              tapentadol_mg_day = 0, tramadol_mg_day = 0)

Arguments

Value

A list containing: