Development and Validation of Multivariable Models to Predict Mortality and Hospitalization in Patients with Heart Failure
Bibliographic record
Abstract
INTRODUCTION: From a prospective multicentre multicountry clinical trial, we developed and validated risk models to predict prospective all-cause mortality and hospitalizations because of heart failure (HF) in patients with HF. METHODS AND RESULTS: BIOSTAT-CHF is a research programme designed to develop and externally validate risk models to predict all-cause mortality and HF hospitalizations. The index cohort consisted of 2516 patients with HF from 69 centres in 11 European countries. The external validation cohort consisted of 1738 comparable patients from six centres in Scotland, UK. Patients from the index cohort had a mean age of 69 years, 27% were female, 83% were in New York Heart Association (NYHA) class II-III and the mean left ventricular ejection fraction (LVEF) was 31%. The full prediction models for mortality, hospitalization owing to HF, and the combined outcome, yielded c-statistic values of 0.73, 0.69, and 0.71, respectively. Predictors of mortality and hospitalization owing to HF were remarkably different. The five strongest predictors of mortality were more advanced age, higher blood urea nitrogen and N-terminal pro-B-type natriuretic peptide, lower haemoglobin, and failure to prescribe a beta-blocker. The five strongest predictors of hospitalization owing to HF were more advanced age, previous hospitalization owing to HF, presence of oedema, lower systolic blood pressure and lower estimated glomerular filtration rate. Patients from the validation cohort were aged 74 years, 34% were female, 85% were in NYHA class II-III, and mean LVEF was 41%; c-statistic values for the full and compact model were comparable to the index cohort. CONCLUSION: A small number of variables, which are usually readily available in the routine clinical setting, provide useful prognostic information for patients with HF. Predictors of mortality were remarkably different from predictors of hospitalization owing to HF.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".