Abstract 15095: CSHA Frailty Scale as a Predictor of Hospital Readmission After PCI
Bibliographic record
Abstract
Introduction: The Canadian Study on Health and Aging (CSHA) Frailty Scale was included in the National Cardiovascular Data Registry’s (NCDR) CathPCI registry beginning April 2018. The value of this frailty assessment as an independent predictor of hospital readmission is unknown. Methods: A retrospective analysis was performed of patients who underwent PCI within the University of North Carolina Medical System between 04/2018 and 12/2018. Outcome data was obtained from our electronic medical record data repository and procedural data from the institutional CathPCI registry. The primary outcome was repeat hospital admission within 1 year of PCI. Significant covariates (p<0.05) in the univariate analyses were considered for inclusion in the multivariate model. Multivariate logistic regression was then performed to determine if CSHA Frailty Scale was an independent predictor of hospital readmission. Results: 1,592 subjects were identified with 367 readmission events. Patients in the readmission cohort were older, had a higher frailty score, and had more comorbidities. Table 1 summarizes the comorbidities included in the logistic regression. CSHA Frailty Scale did not meet significance requirements (P<.05) to be independently associated with readmission. Covariates that were significant independent predictors of readmission included age (OR 1.024, 95% CI 1.012-1.036); cerebrovascular disease (OR 1.688, 95% CI 1.234-2.308); dialysis (OR 2.983, 95% CI, 1.583-5.622); and CHF (OR 2.465, 95% CI, 1.851-3.282). Conclusions: The CSHA Frailty Scale was not independently associated with readmission in the setting of PCI in our health system. This particular assessment of frailty may not provide added value over traditional comorbidities in this patient population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".