Predicting 1-Year Mortality in Outpatients With Heart Failure With Reduced Left Ventricular Ejection Fraction: Do Empiric Models Outperform Physician Intuitive Estimates? A Multicenter Cohort Study
Bibliographic record
Abstract
BACKGROUND: Many studies have demonstrated that physicians often err in estimating patient prognosis. No studies have directly compared physician to model predictive performance in heart failure (HF). We aimed to compare the accuracy of physician versus model predictions of 1-year mortality. METHODS: This multicenter prospective cohort study on 11 HF clinics in 5 provinces in Canada included consecutive consented outpatients with HF with reduced left ventricular ejection fraction (<40%). By collecting clinical data, we calculated predicted 1-year mortality using the Seattle HF Model (SHFM), the Meta-Analysis Global Group in Chronic HF score, and the HF Meta-Score. HF cardiologists and family doctors, blinded to model predictions, estimated patient 1-year mortality. During 1-year follow-up, we recorded the composite end point of mortality, urgent ventricular assist device implant, or heart transplant. We compared physicians and model discrimination (C statistic), calibration (observed versus predicted event rate), and risk reclassification. RESULTS: The study included 1643 patients with ambulatory HF with a mean age of 65 years, 24% female, and mean left ventricular ejection fraction of 28%. Over 1-year follow-up, 9% had an event. The SHFM had the best discrimination (SHFM C statistic 0.76; HF Meta-Score 0.73; Meta-Analysis Global Group in Chronic Heart Failure 0.70) and calibration. Physicians' discrimination differed little (0.75 for HF cardiologists and 0.73 for family doctors) but both physician groups substantially overestimated risk by >10% in both low- and high-risk patients (poor calibration). In risk reclassification analysis, among patients without events, the SHFM better classified 51% in comparison to HF cardiologists and 43% in comparison to family doctors. In patients with events, the SHFM erroneously assigned lower risk to 44% in comparison to HF cardiologists and 34% in comparison to family doctors. CONCLUSIONS: Family doctors and HF cardiologists showed adequate risk discrimination, with however substantial overestimation of absolute risk. Predictive models showed higher accuracy. Incorporating models in family and HF cardiology practices may improve patient care and resource use in HF with reduced left ventricular ejection fraction. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04009798.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".