Prognostic Implications of Machine Learning Algorithm–Supported Diagnostic Classification of Myocardial Injury Using the Fourth Universal Definition of Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: With widespread adoption of high-sensitivity troponin assays, more individuals with myocardial injury are now identified, with type 1 myocardial infarction (T1MI) being less common despite having the most well-established evidence base to inform care. This study assesses the temporal time course of cardiovascular events among various forms of myocardial injury. METHOD: Consecutive hospital encounters were identified. Using the first episode of care during the sampling period, myocardial injury classifications (i.e., T1MI, acute injury/type 2 myocardial infarction [T2MI], chronic injury, and no injury) were established via two machine learning algorithms. The temporal time course of increased hazard for mortality, recurrent myocardial infarction, heart failure, and arrhythmia over 3 years were explored. RESULTS: There were 176,787 index episodes; 6.9% were classified as T1MI, 6.0% as acute injury/T2MI, and 26.7% as chronic injury. Although each classification was associated with an early increased risk of all-cause mortality compared with no injury (incidence rate ratio [IRR]<30 days: T1MI: 19.97 [95% confidence interval 12.50-32.69]; acute injury/T2MI: 26.51 [16.80-42.97]; chronic injury: 15.37 [10.22-23.95]), the instantaneous relative hazard for recurrent myocardial infarction was highest in those with initial T1MI (IRR<30 days: T1MI: 28.81 [22.75-36.76]; acute injury/T2MI: 10.23 [7.60-13.77]; chronic injury:5.54 [4.34-7.41]). In contrast, the instantaneous hazard for heart failure in those with initial acute injury/T2MI and chronic injury remained increased over long-term follow up unlike in T1MI (IRR1 3 yrs: T1MI: 5.52 [4.99-6.09]; acute injury/T2MI: 10.36 [9.51-11.30]; chronic injury:7.40 [6.90-7.94]). CONCLUSIONS: The substantial and persistent rate of late cardiac events highlights the need to establish an evidence base for the therapeutic management of "non-T1MI" diagnostic classifications and suggests opportunity to improve late outcomes using existing and emerging therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".