Burden of Medical Co-Morbidities and Benefit from Surgical Revascularization in Patients with Ischaemic Cardiomyopathy
Bibliographic record
Abstract
AIMS: The landmark STICH trial found that surgical revascularization compared to medical therapy alone improved survival in patients with heart failure (HF) of ischaemic aetiology and an ejection fraction (EF) ≤ 35%. However, the interaction between the burden of medical co-morbidities and the benefit from surgical revascularization has not been previously described in patients with ischaemic cardiomyopathy. METHODS AND RESULTS: The STICH trial (ClinicalTrials.gov Identifier: NCT00023595) enrolled patients ≥ 18 years of age with coronary artery disease amenable to coronary artery bypass grafting (CABG) and an EF ≤ 35%. Eligible participants were randomly assigned 1:1 to receive medical therapy (MED) (n = 602) or MED/CABG (n = 610). A modified Charlson co-morbidity index (CCI) based on the availability of data and study definitions was calculated by summing the weighted points for all co-morbid conditions. Patients were divided into mild/moderate (CCI 1-4) and severe (CCI ≥ 5) co-morbidity. Cox proportional hazards models were used to evaluate the association between CCI and outcomes and the interaction between severity of co-morbidity and treatment effect. The study population included 349 patients (29%) with a mild/moderate CCI score and 863 patients (71%) with a severe CCI score. Patients with a severe CCI score had greater functional limitations based on 6-min walk test and impairments in health-related quality of life as assessed by the Kansas City Cardiomyopathy Questionnaire. A total of 161 patients (Kaplan-Meier rate = 50%) with a mild/moderate CCI score and 579 patients (Kaplan-Meier rate = 69%) with a severe CCI score died over a median follow-up of 9.8 years. After adjusting for baseline confounders, patients with a severe CCI score were at higher risk for all-cause mortality (hazard ratio 1.44, 95% confidence interval 1.19-1.74; P < 0.001). There was no interaction between CCI score and treatment effect on survival (P = 0.756). CONCLUSIONS: More than 70% of patients had a severe burden of medical co-morbidities at baseline, which was independently associated with increased risk of death. There was not a differential benefit of surgical revascularization with respect to survival based on severity of co-morbidity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".