CATCH-HF Hospitalization Predictor

CATCH-HF Hospitalization Predictor: Explanation and Clinical Context This page implements a configurable logistic risk model intended to predict the probability of heart failure (HF) hospitalization using readily available clinical variables (age, NYHA class, prior HF admission, NT-proBNP, eGFR, systolic blood pressure, heart rate, hemoglobin). The calculator itself does not assume or embed any particular published coefficients; instead it accepts a JSON set of model coefficients (intercept + variable betas) so the exact derivation or validation coefficients from the original CATCH-HF publication can be entered to reproduce the original model precisely. Clinical use and interpretation: A validated HF-hospitalization risk model should be used as an adjunct to clinical judgement.

Predicted probabilities depend entirely on the derived coefficients and on appropriate variable measurement (for example, NT-proBNP assay units and timing). Before clinical use, ensure the model coefficients are from a peer-reviewed derivation and external validation, confirm the population and endpoint match your intended setting, and consider local recalibration if necessary.