Abstract 4367453: External Validation of an AI-ECG algorithm for Occlusive Myocardial Infarction Detection in a General Chest Pain Population
Bibliographic record
Abstract
Background: Nearly one-third of patients with Acute Coronary Syndrome have an acute coronary occlusion without diagnostic ST elevations (STE) that often goes unrecognized, leading to treatment delays and poorer outcomes. AI-ECG algorithms can identify subtle, clinically under-recognized ECG patterns indicative of occlusive myocardial infarction (OMI). Our team has previously developed a suite of OMI detection AI-ECG models trained on pre-hospital ECGs from multiple sites in the United States. In this study, we externally validate the performance and generalizability of these models in an unselected general chest pain population from the University of Rochester Medical Center (URMC). Methods: This was a secondary analysis of ACC Chest Pain-MI Registry at URMC. We included consecutive chest pain patients with available 12-lead ECG data between 2019 and 2023. The ECGs were preprocessed using three previously published AI-ECG models for predicting OMI: a random forest (RF) classifier, a convolutional neural network (CNN), and an RF-CNN fusion model. The primary study outcome was OMI, angiographically defined as coronary stenosis >99% with stent placement or stenosis >70% with stent placement and elevated peak troponin x10 folds. STEMI patients with emergent primary PCI met the primary study outcome. Model performance was evaluated using area under ROC curve (AUC) and average precision (AP). We applied previously identified cutoffs from the derivation cohort for rule-in and rule-out thresholds. Results: The sample included 2,254 patients (age 63 ± 14; 35% female). Overall, 946 patients (42%) had OMI, of whom 22% did not have STE on presenting ECG. Figures 1 shows the AUC and AP for AI-ECG models. The RF, CNN, and fusion models ruled in 760 (34%), 869 (39%), and 944 (42%) patients at precisions of 0.98, 0.95, and 0.92, respectively. The models ruled out 1494 (66%), 1385 (61%), and 1310 (58%) patients at recall of 0.86, 0.91, and 0.94, respectively. At “high confidence”, the RF-CNN fusion model had the best rule out accuracy, identifying 33% of patients for early discharge with an overall missed events rate of 3.5%. The RF model had the best rule in accuracy, identifying 15% of patients for immediate PCI with overall false CATH lab activation rate of 4%. Conclusions: Our results demonstrate that our previously developed AI-ECG models for OMI detection generalize well to new unseen data from an external site in the US.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".