Abstract 10602: Baseline Clinical and Genetic Data Based Machine Learning Predictions of 1-Year Ischemic Outcomes Following Percutaneous Coronary Intervention
Bibliographic record
Abstract
Background: Ischemic event rates are low after percutaneous coronary intervention (PCI), conversely second generation P2Y12 inhibitors increase bleeding therefore identifying patients at risk to enable precision in prescribing dual anti-platelet therapy (DAPT) is of clinical interest. Prior machine-learning predictive analyses have been limited by use of older generation stents and lack of genetic data. We utilized clinical and genetic data of patients with new generation stents enrolled in TAILOR-PCI, a 40-center international trial to predict ischemic events after PCI. Methods: There were 4398 eligible post PCI patients with acute coronary syndromes or stable coronary artery disease who were randomly split into training (n=3299) and validation (n=1099) cohorts. The primary endpoint comprising of cardiovascular death, myocardial infarction, stroke, or stent thrombosis within 12 months of PCI was predicted with patient characteristics prior to hospital discharge. First, random forests were used for feature selection with 1,000 bootstraps comprising equal numbers of those with and without ischemic events to minimize the bias of sample imbalance. Features were selected by a consensus on variable-importance over bootstraps. Second, using these features in the training cohort, support vector machines (SVM) with a polynomial kernel was trained with repeated 5-fold cross validation to choose a model with highest area under the receiver operating characteristic curve (AUC) which was then used to predict ischemic events in the validation cohort. Results: The primary endpoint occurred in 2.4% of patients. Using 89 clinical and genetic variables in the random-forest feature selection step, variable importance-based consensus identified 50 top predictors. These 50 predictors achieved AUC=0.703 in the SVM training cohort and AUC=0.744 (specificity=0.84; sensitivity =0.43) in the SVM validation cohort. Conclusions: Machine learning methods using baseline clinical and genetic data enables prediction of infrequent ischemic events in post PCI patients receiving new generation stents. The role of such artificial intelligence algorithms in individualization of DAPT by identifying high or low risk patients needs to be explored.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".