Frailty and nutritional status predict postoperative complications in radical cystectomy patients
Bibliographic record
Abstract
INTRODUCTION: Radical cystectomy (RC) is associated with significant morbidity and mortality. While frailty and nutritional status have emerged as important predictors of surgical outcomes, their impact on RC complications remains incompletely characterized. We aimed to evaluate the relationship between frailty (using the Modified Frailty Index-5 [mFI-5], nutritional status (using the Nutritional Risk Index [NRI]), and postoperative outcomes in patients undergoing RC. METHODS: We conducted a retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program database. Frailty was defined as mFI-5 score ≥2 and malnutrition as NRI ≤97.5. Hypoalbuminemia was defined as preoperative albumin ≤3.5. Outcomes included 30-day complications, length of stay, and mortality. RESULTS: Among 8297 patients, 1793 (21.6%) were classified as frail. Frail patients experienced higher rates of infectious (sepsis: 10.2% vs. 6.72%, p<0.001), cardiopulmonary (myocardial infarction: 2.56% vs. 1.09%, p<0.001), and renal (renal insufficiency: 9.53% vs. 5.23%, p<0.001) complications. Mortality was twice as high in frail patients (2.45% vs. 1.17%, p<0.001). Among 8297 patients with nutritional data, 668 (8.05%) were malnourished, and 910 (15.2%) had hypoalbuminemia. Malnourished patients had higher rates of transfusion requirements (46.4% vs. 24.9%, p<0.001) and mortality (2.54% vs. 1.35%, p=0.032). Hypoalbuminemic patients demonstrated increased major complications (56.7% vs. 38.5%, p<0.001). The predictive accuracy of these indices varied by outcome, with area under the curve values ranging from 0.53-0.63. CONCLUSIONS: Both frailty and poor nutritional status are associated with increased postoperative complications and mortality following RC; however, the modest predictive accuracy of these indices indicates they should be used as part of a broader risk assessment strategy.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".