Predicting death or readmission following heart failure hospitalisation: the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) readmission and mortality rates remain high among HF patients. Improved and robust risk prediction models for better monitoring, informed decision-making, targeted interventions and improved patient outcomes are required. We developed and validated a patient-centric model to predict long-term outcomes of death or a repeat HF-hospitalisation using a modern model selection approach. METHODS: We used data from a contemporary registry of patients discharged alive from an HF-hospitalisation between 1 April 2015 and 31 March 2019. An integrated and multifaceted selection approach (combining backward selection, least absolute shrinkage and selection operator and expert opinion) to Cox-proportional hazard models was used for model development. To account for model uncertainty and improve generalisability, bootstrap-Bayesian Model Averaging was used to derive the final risk model. RESULTS: The cohort included 1842 patients with a median follow-up time of 529 days (range 2-1459 days). 790 (43%) patients experienced the outcome, with 68 (8.6%) having the outcome within 30 days. The final risk model included 12 variables, of which 8 were identified as being dominant. The top predictors with >99% probability for model inclusion were increasing age (HR 1.07, 95% CI 1.00 to 1.11/5 years), prior HF-diagnoses (1.47, 95% CI 1.13 to 1.71) and lower discharge haemoglobin (1.10, 95% CI 1.05 to 1.15/10 g/L). Other predictors (~>60% model-selection probability) included lower admitting systolic blood pressure, higher loop-diuretic discharge requirements, persistent smoking, an admitting non-sinus rhythm and absence of discharge angiotensin-converting enzyme inhibitor, angiotensin receptor blocker or angiotensin receptor-neprilysin inhibitor prescription. The 3-year cross-validated c-statistic was 0.63 (95% CI 0.61 to 0.65). CONCLUSIONS: A clinically oriented prognostic model with moderate discrimination, to predict adverse events postdischarge for HF, has been developed and internally validated. This model, leveraging an integrated approach to selection, shows promise in personalising discharge planning. Future external validation is necessary to confirm its applicability and potential impact on clinical practice.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".