Abstract 12563: Creation of an Artificial Intelligence Model For Prediction of Major Adverse Cardiovascular Events Late After Tetralogy of Fallot Repair
Bibliographic record
Abstract
Introduction and Hypothesis: Despite successful tetralogy of Fallot repair (rTOF) in childhood, advancing age is associated with escalating risk of morbidity and mortality. Published risk scores for prediction of major adverse cardiovascular events (MACE) typically incorporate invasive and/or specialized measures. We hypothesized that an artificial intelligence (AI) model using an array of parameters available in routine clinical practice could successfully predict MACE in rTOF. Methods: Adults with rTOF were identified using institutional databases (the prospective observational Comprehensive Outcomes Registry Late After Tetralogy of Fallot Repair [CORRELATE] and the retrospective Toronto Outcomes Registry of Congenital Heart disease [TORCH]). A random forest AI model for prediction of MACE was trained on CORRELATE using repeated random sub-sampling validation. External validation was achieved using non-overlapping retrospective data from TORCH. The MACE composite outcome included all-cause mortality, resuscitated sudden death, sustained ventricular tachycardia (>30 seconds) or heart failure (hospital admission>24 hours). Results: In total, 804 subjects were studied and MACE was observed in 73 (9%) subjects. Patients were either included in the training dataset (n=312, 59% male, median age 32 years [IQR 23-43], median follow-up 6 years [IQR 4-7]) or the testing dataset (n=492, 58% male, median age 26 years [IQR 20-37], median follow up 10 years [IQR 6-10]). Variables incorporated into the AI model are shown (Figure 1). Predictive capacity of the AI models were similar in testing (AUC 0.81, CI 0.75-0.86) and training (AUC 0.88, CI 0.71-0.99) datasets with excellent receiver operating characteristics (Figure 1). Conclusions: An AI model based on routinely used and widely available clinical and imaging variables could successfully predict MACE in rTOF. Further study is required to determine the value of AI for risk management in rTOF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".