External validation and extension of the <scp>TIMI</scp> risk score for heart failure in diabetes for patients with recent acute coronary syndrome: An analysis of the <scp>EXAMINE</scp> trial
Bibliographic record
Abstract
Abstract Aims The Thrombolysis in Myocardial Infarction Risk Score for Heart Failure (HF) in Diabetes (TRS‐HF DM ) prognosticates HF hospitalization in people with type 2 diabetes (T2D). This study aimed to externally validate and extend its use for those with recent acute coronary syndrome (ACS). Materials and Methods The TRS‐HF DM was externally validated in the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trial (n = 5380) and extended with natriuretic biomarkers. Missing data were multiply imputed. Initial TRS‐HF DM variables were previous HF (2 points), atrial fibrillation (1 point), coronary artery disease (1 point), estimated glomerular filtration rate <60 ml/min/1.73 m 2 (1 point), and urine albumin‐to‐creatinine ratio 30‐300 mg/g (1 point) and >300 mg/g (2 points). Results In total, HF hospitalization occurred in 193 (3.6%) patients. Based on the TRS‐HF DM , 25% of patients were classified as intermediate risk (1 point), 30% were classified as high risk (2 points), 19% were classified as very‐high risk (3 points) and 26% were classified as severe risk (≥4 points). Before model extension, discrimination (C‐index 0.76, 95%·CI 0.73‐0.80) and calibration (calibration slope 0.82, 95%·CI 0.65‐1.0; calibration‐in‐the‐large −0.15, 95%·CI −0.37‐0.64) were moderate‐to‐good in individuals with T2D and recent ACS. The extension of TRS‐HF DM with the addition of N‐terminal pro‐B‐type natriuretic peptide (NT‐ProBNP) improved discrimination (C‐index 0.82, 95%·CI 0.79‐0.85) and calibration (calibration slope 0.84, 95%·CI 0.66‐1.02; calibration‐in‐the‐large −0.12, 95%·CI −0.33‐0.081) for this higher‐risk population. Conclusion The TRS‐HF DM with the extension of NT‐ProBNP improves risk stratification and generalizes the use of the risk score for patients with T2D and ACS. Future validation studies in ACS populations may be warranted.
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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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".