Neural Networks for Prognostication of Patients With Heart Failure
Bibliographic record
Abstract
Background Prognostication of heart failure patients from cardiopulmonary exercise test (CPET) currently involves simplification of complex time-series data into summary indices. We hypothesized that prognostication could be improved by considering the totality of the data generated during a CPET, instead of using summary indices alone. Methods and Results Complete data from 1156 CPETs were used to predict clinical deterioration (characterized by initiation of mechanical circulatory support, listing for heart transplantation or mortality) 1 year post-CPET. We compared the prognostic value (area under the receiver operating characteristic curve) of (1) the most predictive summary indices, (2) staged data collected at discrete intervals using multivariable regression models, and (3) breath-by-breath data using a feedforward neural network. The top-performing models were compared with the commonly used CPET risk score, using absolute net reclassification index. All models were trained and assessed using a 100-iteration Monte Carlo cross-validation. A total of 190 (16.4%) patients experienced clinical deterioration. The summary indices demonstrated subpar discriminative value (area under the receiver operating characteristic curve ≤0.800). Each multivariable model outperformed the summary indices, with the neural network incorporating breath-by-breath data achieving the best performance (area under the receiver operating characteristic curve =0.842). When compared with the CPET risk score (area under the receiver operating characteristic curve =0.759), the top-performing model obtained a net reclassification index of 4.9%. Conclusions The current practice of considering summary indices in isolation fails to realize the full value of CPET data. This may lead to less accurate prognostication of patients and in consequence, inaccurate selection of patients for advanced therapy. Clinical practices, like CPET prognostication, must be continuously reevaluated to ensure optimal usage of valuable (and oft-underutilized) data sources.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".