5261Linear ongoing risk of major cardiovascular events in a global prospective registry of high-risk patients with stable coronary disease: insights from the TIGRIS study
Bibliographic record
Abstract
Background: The long-Term rIsk, clinical manaGement and healthcare Resource utilization of stable coronary artery dISease in post myocardial infarction patients (TIGRIS) study was designed as a global prospective registry to evaluate the clinical management, quality of life (QoL), and outcomes during 2-year (y) follow-up in high-risk patients with stable coronary artery disease (CAD) 1–3 y post-myocardial infarction (MI). Methods: A total of 9,176 patients 1–3 y post-MI were enrolled between June 2013 and February 2015 from 369 centres in 25 countries. All patients had ≥1 of the following risk factors: age ≥65 y, diabetes mellitus requiring medication (DM), second prior MI, multivessel CAD, chronic non-end stage renal disease (CKD). The primary outcome over 2 y was a composite of MI, unstable angina (UA) with urgent revascularisation, stroke, or death. The clinical and patient-reported predictors of adverse outcomes were determined using Poisson regression models. Results: Follow-up data were available in 9,044 patients (98.6%). Mean age was 67 y, 76% were male, 30.5% had DM, 10.2% second prior MI, 65.9% multivessel CAD, and 7.6% CKD. The primary outcome occurred in 621 patients (6.9%); with components death 295 (3.3%), MI 195 (2.2%), stroke 58 (0.7%) and UA with urgent revascularisation 103 (1.2%). There was a steady linear accumulation of all events throughout follow-up (see Figure). Statistically significant clinical predictors of the primary outcome (after adjusting for baseline risk, country and region) included: increasing age (risk ratio [RR] 1.30 per 10 y), heart rate ≥80 bpm (RR 1.45 vs <70 bpm), current smoking (RR 1.49), DM (RR 1.69), second prior MI (RR 1.78), CKD (RR 2.16), anaemia (RR 1.71), prior angina (RR 1.42), prior heart failure (RR 1.85), prior stroke (RR 1.41), peripheral vascular disease (RR 1.76), prior major bleed (RR 1.89), and lung disease (RR 1.44). Treatment predictors included lack of revascularisation at index MI (RR 1.76 vs PCI), diuretic use (RR 1.90), and not on a statin (RR 1.30) at enrollment. The main patient-reported predictor was impaired QoL (misery index ≥4 RR 2.51 vs 0, see Figure; misery index is a QoL score based on the EQ-5D ranging from 0 [no problems] to 10 [maximal problems]).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".