steinhart_ahf_model: Steinhart Model for Acute Heart Failure (AHF)

In cliot: Clinical Indices and Outcomes Tools

Steinhart Model for Acute Heart Failure (AHF)

Description

Calculates the post-test probability of Acute Heart Failure in patients presenting with undifferentiated dyspnea. The model integrates the clinician's pre-test probability assessment with patient age and NT-proBNP levels to improve diagnostic accuracy.

Usage

steinhart_ahf_model(age, pretest_probability_percent, nt_probnp_pg_ml)

Arguments

age	Numeric. Patient age in years.
pretest_probability_percent	Numeric. Clinician's estimated pre-test probability of AHF (0-100%), based on history, physical exam, ECG, and chest X-ray, before NT-proBNP results are known.
nt_probnp_pg_ml	Numeric. Serum N-terminal pro-B-type natriuretic peptide level in pg/mL.

Value

A list containing:

PostTest_Probability	The calculated probability of AHF diagnosis.
Interpretation	Risk categorization (Low <= 20%, Intermediate 21-79%, High >= 80%).

References

Steinhart B, Thorpe KE, Bayoumi AM, et al. Improving the diagnosis of acute heart failure using a validated prediction model. J Am Coll Cardiol. 2012;60(16):1532-1539. doi:10.1016/j.jacc.2012.06.038

Examples

# Example 1: High Probability
# 75yo, Pretest 60%, NT-proBNP 4000
steinhart_ahf_model(75, 60, 4000)

# Example 2: Low Probability
# 50yo, Pretest 20%, NT-proBNP 100
steinhart_ahf_model(50, 20, 100)

cliot documentation built on Dec. 1, 2025, 1:06 a.m.