Frailty and Outcomes After Myocardial Infarction: Insights From the CONCORDANCE Registry
Bibliographic record
Abstract
Background Little is known about the prognostic implications of frailty, a state of susceptibility to stressors and poor recovery to homeostasis in older people, after myocardial infarction ( MI ). Methods and Results We studied 3944 MI patients aged ≥65 years treated at 41 Australian hospitals from 2009 to 2016 in the CONCORDANCE ( Australian Cooperative National Registry of Acute Coronary Care, Guideline Adherence and Clinical Events ) registry. Frailty index ( FI ) was determined using the health deficit accumulation method. All-cause and cardiac-specific mortality at 6 months were compared between frail ( FI >0.25) and nonfrail ( FI ≤0.25) patients. Among 1275 patients with ST-segment-elevation MI (STEMI), 192 (15%) were frail, and among 2669 non-STEMI ( NSTEMI) patients, 902 (34%) were frail. Compared with nonfrail counterparts, frail STEMI patients received 30% less reperfusion therapy and 22% less revascularization during index hospitalization; frail NSTEMI patients received 30% less diagnostic angiography and 39% less revascularization. Unadjusted 6-month all-cause mortality ( STEMI : 13% versus 3%; NSTEMI : 13% versus 4%) and cardiac-specific mortality ( STEMI : 6% versus 1.4%, NSTEMI : 3.2% versus 1.2%) were higher among frail patients. After adjustment for known prognosticators, FI was significantly associated with higher 6-month all-cause ( STEMI : odds ratio: 1.74 per 0.1 FI [ 95% confidence interval, 1.37-2.22], P<0.001; NSTEMI : odds ratio: 1.62 per 0.1 FI [95% confidence interval, 1.40-1.87], P<0.001) but not cardiac-specific mortality ( STEMI : P=0.99; NSTEMI : P=0.93). Conclusions Frail patients receive lower rates of invasive cardiac care during MI hospitalization. Increased frailty was independently associated with increased postdischarge all-cause mortality but not cardiac-specific mortality. These findings inform identification of frailty during MI hospitalization as a potential opportunity to address competing risks for mortality in this high-risk population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".