Package 'cliot'

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)

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
)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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")

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)

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)

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)

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)

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)

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)

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)

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)

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)

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")

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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")

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)

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)

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)

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)

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)

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)

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)

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")

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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