Attributable Perioperative Cost of Frailty after Major, Elective Noncardiac Surgery: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patients with frailty consistently experience higher rates of perioperative morbidity and mortality; however, costs attributable to frailty remain poorly defined. This study sought to identify older patients with and without frailty using a validated, multidimensional frailty index and estimated the attributable costs in the year after major, elective noncardiac surgery. METHODS: The authors conducted a retrospective population-based cohort study of all patients 66 yr or older having major, elective noncardiac surgery between April 1, 2012, and March 31, 2018, using linked health data obtained from an independent research institute (ICES) in Ontario, Canada. All data were collected using standard methods from the date of surgery to the end of 1-yr follow-up. The presence or absence of preoperative frailty was determined using a multidimensional frailty index. The primary outcome was total health system costs in the year after surgery using a validated patient-level costing method capturing direct and indirect costs. Secondary outcomes included costs to postoperative days 30 and 90 along with sensitivity analyses and evaluation of effect modifiers. RESULTS: Of 171,576 patients, 23,219 (13.5%) were identified with preoperative frailty. Unadjusted costs were higher among patients with frailty (ratio of means 1.79, 95% CI 1.76 to 1.83). After adjusting for confounders, an absolute cost increase of $11,828 Canadian dollar (ratio of means 1.53; 95% CI, 1.51 to 1.56) was attributable to frailty. This association was attenuated with additional control for comorbidities (ratio of means 1.24, 95% CI, 1.22 to 1.26). Among contributors to total costs, frailty was most strongly associated with increased postacute care costs. CONCLUSIONS: For patients with preoperative frailty having elective surgery, the authors estimate that attributable costs are increased 1.5-fold in the year after major, elective noncardiac surgery. These data inform resource allocation for patients with frailty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".