The impact of frailty on rectal prolapse repair: a retrospective analysis of the national inpatient sample for clinical outcomes and health resource utilization
Bibliographic record
Abstract
Abstract Purpose Rectal prolapse commonly affects elderly, frail patients. The impact of frailty alone on surgical outcomes for rectal prolapse has not been thoroughly investigated. The aim of this study was to utilize the National Inpatient Sample and the modified frailty index (mFI-11) to compare postoperative outcomes between frail and robust patients undergoing surgery for rectal prolapse. Methods We conducted a retrospective population-based cohort study using the Healthcare Cost and Utilization Project (HCUP) NIS from 2015 to 2019. The mFI-11 was utilized to classify patients as frail or robust. The primary outcomes were overall in-hospital postoperative morbidity and mortality. The secondary outcomes included system-specific postoperative morbidity, length of stay (LOS), total in-hospital healthcare cost, and discharge disposition. These were assessed using univariable and multivariable regressions. Results A total of 2130 patients, 239 frail (mFI > 0.27) and 1,891 robust patients (mFI < 0.27) who underwent rectal prolapse repair were analyzed. After adjustment, frail patients had a higher rate of in-hospital mortality (OR 10.38, 95% CI 0.65–166.59, p = 0.098) and morbidity (OR 2.18, 95% CI 1.31–3.63, p = 0.003), longer LOS (MD 1.60 days, 95% CI 1.05–2.44, p = 0.028), and greater cost of treatment (MD $15,561.56, 95% CI − 6023.12–37,146.25, p = 0.157) than robust patients. Conclusion Frailty increases postoperative morbidity and mortality and cost more to the healthcare system overall for patients undergoing rectal prolapse repair. This retrospective study is limited by selection bias and residual confounding. Consideration of preoperative optimization programs for frail patients undergoing surgery for rectal prolapse is an important next step to mitigate these poor outcomes.
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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 | 0.001 | 0.003 |
| 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".