Predictive Models Aid Prognostication: Secondary Analysis Integrating Model and Physician Prognostic Estimates in...

In a recent multicenter Canadian study in heart failure (HF), model predictions proved more accurate than physicians.

Simulating clinical practice, the authors evaluated the predictive value of combining model predictions with physician estimated 1-year mortality in HF outpatients.

This post hoc analysis of a Canadian multicenter cohort study included HF outpatients (left ventricular ejection fraction ≤40%). HF cardiologists and family doctors estimated patient 1-year mortality using clinical judgment. The Seattle HF Model (SHFM) predicted mortality. All patients were followed for 1 year to collect mortality. Stratified by specialty, we compared the performance of SHFM and physician estimates alone, with a model integrating physician and SHFM predictions using a random forest survival model, evaluating discrimination (C-statistic), calibration (observed vs predicted event rate), risk reclassification, and clinical net benefit.

In 1,643 HF patients, 1-year mortality was 9% (95% CI: 8%-11%). The SHFM had adequate discrimination (C-statistic 0.76; 95% CI: 0.72-0.80) and excellent calibration. Physicians showed adequate discrimination (0.75; 95% CI: 0.71-0.79 for cardiologists; 0.72; 95% CI: 0.66-0.78 for family doctors) and poor calibration with significant risk overestimation. Integrating SHFM and physician predictions, discrimination significantly improved (0.82; 95% CI: 0.78-0.86 for cardiologists; 0.87; 95% CI: 0.83-0.91 for family doctors) with excellent calibration. By risk reclassification, among patients without events, the integrated model better risk-classified 71% (95% CI: 70%-72%) vs cardiologists and 60% (95% CI: 58%-61%) vs family doctors; among patients with events, the model misclassified 45% (95% CI: 58%-63%) vs cardiologists and 11% (95% CI: 25% to 3%) vs family doctors. The integrated model led to higher clinical benefit.

Integrating SHFM predictions with physician judgment improved accuracy. Model-informed assessment provides prognostic accuracy for clinical decision-making. (Predicted Prognosis in Heart Failure Intuition; NCT04009798).