Abstract 18211: Does Frailty Matter in High Risk PCI
Bibliographic record
Abstract
Background: There are limited data depicting the relationship between frailty and in-hospital outcomes for high-risk PCI. Methods: The Cath-PCI registry (2018 - 2020) was used to examine the association of frailty as defined by the Canadian Study of Health and Aging (CSHA) frailty index and in-hospital complications for patients undergoing PCI. We conducted a logistic regression analysis for in-hospital mortality. Results: The sample size was 1,316,390 individuals aged ≥ 65 years undergoing PCI. The frailty distribution was: 78,921 not frail (6.0%), 892,695 pre-frail (67.8%), 284,444 frail (21.6%), and 60,330 severely frail (2.2%). The median age of patients who were frail was 76 years, 39.4% were female, and 87.2% were white. In-hospital rates for any bleeding stratified by frailty status were: not frail: 1.0%, pre-frail: 1.5%, frail 3.3%, severely frail 8.9%. The adjusted odds ratio (aOR) regarding in-hospital mortality among PCI patients at high bedside mortality risk for pre-frail, 2.58 (95% CI: 1.42 - 1.80) for frail, and 5.14 (95% CI: 4.56 - 5.80) for severely frail. The adjusted association for in-hospital mortality across sub-groups stratified by frailty status was: left main PCI ~ pre-frail aOR 1.44 (95% CI: 1.01 - 2.05), frail aOR 2.18 (95% CI: 1.53 - 3.11) and severely frail aOR 3.98 (95% CI: 2.79 - 5.70). While the aORs for chronic total occlusion were ~ pre-frail 1.57 (95% CI: 0.97 - 2.52), frail 2.31 (95% CI: 1.43 - 3.75) and severely frail 6.04 (5.40 - 6.75) and cardiogenic shock ~ pre-frail 1.33 (95% CI: 1.08 - 1.64), frail 1.54 (95% CI: 1.25 - 1.90) and severely frail 2.44 (1.99 - 3.00). Conclusions: Contemporary PCI is commonly performed on frail patients. Frailty is associated with higher risk of mortality across high-risk PCI categories; however, severe frailty depicts a distinct and exceptionally high-risk cohort.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".