Cardiac care after myocardial infarction in cancer survivors: A population-based study.
Bibliographic record
Abstract
9578 Background: Cancer survivors (CS) may receive suboptimal non-cancer related care, such as lower rates of bypass surgery after myocardial infarction (MI) in the US. Secondary prevention (SP) after MI is an important aspect of survivorship care. We aim to examine the use of medications and interventions for SP after MI in CS vs. non-cancer patients (NCP). Methods: All acute MI patients (pts) hospitalized in Ontario between 1995 and 2012 were identified from the Canadian Institute of Health Information databases, and linked to the Ontario Cancer Registry to determine whether they were CS or NCP. Those who were diagnosed with cancer < 1 year before their MI were excluded. The cohort was linked to other administrative databases to determine demographics, comorbidities, cardiac risk factors, hospital-based interventions and, for those over age 65, outpatient-based medication use. Propensity scores derived from baseline characteristics were used to create a 1:4 (CS:NCP) matched cohort. The use of medications and interventions within the first 90 days of MI, and medication adherence (measured by proportion of days covered (PDC) within the first year of MI) were compared between CS and NCP using matched analyses. Results: We identified 102,415 MI pts (CS = 20,483; NCP = 81,932) with 57% male and 86% > age 65. Slightly fewer CS vs. NCP received angiograms (37.4% vs. 38.6%; p = 0.003) and percutaneous coronary interventions (17.0% vs. 17.8%; p = 0.01), but similar CS and NCP received bypass surgery (2.5% vs. 2.5%). For pts who were > age 65 and active users of the public drug programs with MI < 5 years after cancer diagnosis, fewer CS vs. NCP received ACEi/ARB (67.0% vs. 70.6%; p < 0.001), statins (54.9% vs. 60.0%; p < 0.001), and clopidogrel (27.8% vs. 33.8%; p < 0.001), but the differences for those with MI > 5 years after cancer diagnosis were much less (all interaction p < 0.01). Similar CS and NCP received beta-blockers and nitrates. Both groups had similar degree of medication adherence, except for ACEi/ARB (PDC: 76.0% (CS) vs. 76.9% (NCP), p = 0.03). Conclusions: Slightly fewer CS than NCP received SP after MI, especially for those occurred within 5 years after cancer diagnosis. Further studies are needed to examine the outcome implications of this finding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".