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 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 | 0.000 |
| 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) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".