Impact of frailty on outcomes of elderly patients undergoing percutaneous coronary intervention: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Frailty is a common condition in elderly patients who receive percutaneous coronary intervention (PCI). However, how frailty affects clinical outcomes in this group is unclear. AIM: To assess the link between frailty and the outcomes, such as in-hospital complications, post-procedural complications, and mortality, in elderly patients post-PCI. METHODS: The PubMed/MEDLINE, EMBASE, Cochrane Library, and Web of Science databases were screened for publications up to August 2023. The primary outcomes assessed were in-hospital and all-cause mortality, major adverse cardiovascular events (MACEs), and major bleeding. The Newcastle-Ottawa Scale was used for quality assessment. RESULTS: Twenty-one studies with 739693 elderly patients undergoing PCI were included. Frailty was consistently associated with adverse outcomes. Frail patients had significantly higher risks of in-hospital mortality [risk ratio: 3.45, 95% confidence interval (95%CI): 1.90-6.25], all-cause mortality [hazard ratio (HR): 2.08, 95%CI: 1.78-2.43], MACEs (HR: 2.92, 95%CI: 1.85-4.60), and major bleeding (HR: 4.60, 95%CI: 2.89-7.32) compared to non-frail patients. CONCLUSION: Frailty is a pivotal determinant in the prediction of risk of mortality, development of MACEs, and major bleeding in elderly individuals undergoing percutaneous coronary intervention.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".