Multimorbidity, functional impairment, and mortality in older patients stable after prior acute myocardial infarction: Insights from the TIGRIS registry
Bibliographic record
Abstract
BACKGROUND: Data on the association of multimorbidity and functional impairment with cardiovascular (CV) and non-CV outcomes among older myocardial infarction (MI) patients are limited. HYPOTHESIS: Multimorbidity and functional impairment among older MI patients are associated with CV and non-CV mortality. METHODS: Patients aged ≥65 years, 1-3 years post-MI, and enrolled between June 2013 and Novemeber 2014 from 349 sites in 25 countries in the global TIGRIS registry were categorized by age, number of comorbidities, and presence and degree of functional impairment. Functional impairment was calculated using five-dimension EuroQol based on three domains-mobility, self-care, and usual activities. The association between age, number of comorbid conditions, and degree of functional impairment with 2-year incidence of CV and non-CV death was evaluated using Poisson regression analysis. RESULTS: Older age was associated with higher number of comorbidities and functional impairment; after adjustment, increasing age was significantly associated with non-CV mortality (p = .03) but not CV mortality (p = .38). Greater functional impairment was associated with a higher rate and relatively equal magnitude risk of CV (rate ratios [RR] 1.52, 95% confidence intervals [CI]: 1.29-1.79, per one-step increase) and non-CV mortality (RR 1.42, 95% CI: 1.17-1.73). Multimorbidity was more strongly associated with CV mortality (RR 1.52, 95% CI: 1.38-1.67, per additional comorbidity) versus non-CV mortality (RR 1.29, 95% CI: 1.14-1.47, per additional comorbidity). CONCLUSIONS: Multimorbidity and functional impairment are prevalent among older post-MI patients and are associated with increased CV and non-CV mortality. These findings highlight the importance of considering comorbid conditions and functional impairment as predictors of risk for adverse outcomes and aspects of medical decision making. Clinical Trial Registration: NCT01866904.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".