An age-independent hospital record-based frailty score correlates with adverse outcomes after heart surgery and increased health care costs
Bibliographic record
Abstract
Background Globally, an increasing number of vulnerable or frail patients are undergoing cardiac surgery. However, large-scale frailty data are often limited by the need for time-consuming frailty assessments. This study aimed to (1) create a retrospective registry-based frailty score (FS), (2) determine its effect on outcomes and age, and (3) health care costs. Methods Retrospective data were obtained from the New Brunswick Heart Centre registry for all cardiac surgery patients between 2012 and 2017. A 20-point FS was created using available binary risk variables. The primary outcomes of interest most relevant to vulnerable patients were prolonged hospitalization, failure to be discharged home, and hospitalization bed cost. Composite outcome of prolonged hospitalization (>8 days) and/or non-home discharge were analyzed using multivariate analysis. Results A total of 3463 patients (mean age, 66 ± 10 years) were included in the final analysis. Tercile-based FSs were: low (0-4; n = 856), medium (5-7; n = 1709), high (≥8; n = 898). In unadjusted data, frail patients were older with more comorbidities. High FS patients had greater risks of prolonged hospitalization (median 7 vs 5 days; P < .001), lower home-discharge rates (51% vs 83%; P < .001), higher 30-day readmission rates (18% vs 10%; P < .001), and increased 30-day mortality rates (≤0.7% [low], >0.7% to ≤1.2% [medium], and >1.2% to 4.8% [high]; P < .001). After statistical adjustment, the FS was an independent predictor of composite outcome (odds ratio, 1.3: 95% CI, 1.26-1.35), and increased hospital bed costs. Conclusions A registry-based FS can be used to identify vulnerable or frail patients undergoing cardiac surgery and was associated with poor outcomes independent of age. This highlights that although frailty defined by increased vulnerability is often associated with older age, it is not a surrogate for aging, thereby having important implications in reducing health system costs and efforts to provide streamlined care to the most vulnerable.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".