Comprehensive prediction of outcomes in patients with ST elevation myocardial infarction (STEMI) using tree-based machine learning algorithms
Bibliographic record
Abstract
ST elevation myocardial infarction (STEMI), a subtype of acute coronary syndrome, is one of the leading causes of morbidity and mortality. Revascularization using primary percutaneous coronary intervention (PPCI) is the gold standard treatment. Despite the restoration of myocardial blood flow, some patients experience adverse outcomes. Early detection of high-risk patients would facilitate timely management, potentially improving their morbidity, mortality, and quality of life. In-depth characterization of the aortic pressure (AP) waveform may identify a high-risk patient cohort. We present tree-based classifiers and features extracted from the AP signals to identify patients at risk of adverse outcomes. This is a single-center, retrospective cohort study that included 605 eligible STEMI patients [64.2 ± 13.2 years, 71.4 % (432) males] treated with PPCI. Outcomes, including mortality (within 30-day and 1-year), and in-hospital events such as prolonged in-hospital stay (>4 days) for medical reasons, a new diagnosis of heart failure (HF), diuretic use for more than 24 h, intubation-ventilation or BiPAP use, and inotropic and/or vasopressor use, were recorded. We extracted features mainly from denoised AP signals recorded during PPCI, followed by different feature selection algorithms and classification methods to predict outcomes. Various classifiers such as tree-based classifiers, including random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and CatBoost, were used. Using recursive feature elimination (RFE) as the feature selection method and the CatBoost classifier, we achieved a receiver operating characteristic curve's area under the curve (AUC) of 80 % for all outcomes except for the new diagnosis of HF and diuretic use (>24 h). For the new diagnosis of HF and diuretic use (>24 h), the AUC values were 73 % and 79 %, respectively. In conclusion, tree-based classifiers using features extracted from AP traces can effectively identify patients at risk of adverse outcomes in patients with STEMI. • A medically intuitive framework for predicting adverse outcomes in patients with STEMI is developed. • The aortic pressure recorded during the PPCI can serve as marker for identifying patients at risk of adverse outcomes. • Ensemble tree-based classifiers (RF, AdaBoost, XGBoost, and CatBoost) are effective in adverse outcomes prediction. • The RFE method successfully highlighted important features for the prediction of each outcome. • Ejection systolic time is associated with adverse outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".