The Predictive Role of Cardiac Troponin Elevation Ratio Combined With Heart Function Index Model in the Prognosis of Non-ST-Segment Elevation Myocardial Infarction Patients
Bibliographic record
Abstract
Background: Non-ST-segment elevation myocardial infarction (NSTEMI) is a common form of coronary artery disease, and its prognosis is influenced by multiple factors. This study aimed to analyze the predictive role of the combined application of cardiac troponin and cardiac function indices in NSTEMI patients' prognosis. Methods: NSTEMI patients were screened and included in the study. Cardiac troponin elevation ratio (cardiac troponin I (cTnI)/upper limit of normal (ULN)) was measured upon admission, and cardiac function was assessed. General clinical data, laboratory parameters, Grace score, New York Heart Association (NYHA) functional class, complications, and mortality data were collected. The correlation between mortality in NSTEMI patients and clinical parameters was analyzed, and a nomogram prediction model for NSTEMI patient mortality was established. Results: A total of 252 NSTEMI patients were included. Female gender, elevated high-sensitivity C-reactive protein (H-CRP), left ventricular ejection fraction (LVEF) < 50%, NYHA class III and IV, and cTnI/ULN elevation by 36.25-fold were significantly independently associated with mortality outcomes. Multifactorial logistic analysis indicated that these indices remained associated with mortality. A nomogram model predicting NSTEMI patient mortality was constructed using these indices, with an area under the curve (AUC) of 0.911, sensitivity of 97.5%, and specificity of 72.8%. This predictive model outperformed the Grace score (AUC = 0.840). Conclusions: In NSTEMI patients, a 36.25-fold increase in cTnI/ULN, coupled with NYHA class III and IV, independently predicted prognosis. We developed a nomogram model integrating cTnI/ULN and cardiac function indices, aiding clinicians in assessing risk and implementing early interventions for improved outcomes.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".