Door-to-Balloon Time Outperforms ST-Segment Elevation in Predicting the STEMI vs. NSTEMI Final Diagnosis
Bibliographic record
Abstract
Background: The STEMI/NSTEMI classification guides management and quality metrics for acute myocardial infarction (AMI). We examined whether the final cath-lab diagnosis of STEMI versus NSTEMI correlates more closely with door-to-balloon (D2B) time than with either ST-segment elevation (STE) on pre-angiogram ECG or a culprit lesion with TIMI 0-1 flow. Methods: This retrospective study analyzed 410 patients with AMI from the DOMI-ARIGATO database who underwent coronary angiography. For each patient, we recorded FDx coded by the interventional cardiologist, D2B < 120 min versus > 120 min, STE criteria (Fourth Universal Definition), and angiographic TIMI 0-1 culprit. Predictors of FDx-STE discordance were evaluated with multivariable logistic regression. Results: Among 410 angiographed AMI patients (mean age 63 ± 13; 71% male), 165 (40.2%) received an FDx-STEMI and 245 (59.8%) an FDx-NSTEMI. D2B time showed 94% agreement with FDx (160/165 FDx-STEMI treated < 120 min; 225/245 FDx-NSTEMI treated > 120 min), exceeding concordance for STE (82%; p < 0.001) and TIMI 0-1 flow (75%; p < 0.001). FDx and STE diverged in 75 patients (18%): 60 rapidly treated STE-negative cases were labelled STEMI, whereas 15 delayed STE-positive cases were labelled NSTEMI. In regression analysis, D2B < 120 min remained the sole independent predictor of discordance (adjusted OR 6.7, 95% CI 3.5–13.8). Conclusions: In this registry, the cath-lab label “STEMI” showed the strongest correlation with meeting a 120 min benchmark, exceeding correlations for STE or angiographic occlusion. These findings suggest that quality-metric compliance, rather than electrocardiographic or anatomic criteria, predominantly drives final diagnosis.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".