Morbidity and Mortality Following Colorectal Surgery in Patients With End-Stage Renal Failure: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: The risk of abdominal surgery in patients with end-stage renal failure is poorly defined. Our objective was to describe outcomes of colorectal surgery in dialysis patients from a population-based perspective. METHODS: We analyzed the 1993 to 2007 Nationwide Inpatient Sample to identify patients hospitalized for colorectal surgery. The effect of renal failure on mortality, complications, length of stay, and charges was evaluated using logistic regression models. RESULTS: Between 1993 and 2007, there were 755,343 admissions for colorectal surgery in the Nationwide Inpatient Sample database; 5806 patients (0.77%) were receiving dialysis treatment (87.4% hemodialysis, 4.9% peritoneal dialysis, 7.7% method not specified). Patients undergoing dialysis had an increased risk of mortality (22.1% vs 2.8%; adjusted OR 4.83; 95% CI 4.58-5.31) and complications (52.1% vs 34.0%; adjusted OR 2.04; 95% CI 1.90-2.17). Dialysis patients undergoing nonelective procedures had a 2-fold higher mortality rate than patients having had elective surgery (25.5% vs 10.3%; adjusted OR 2.01; 95% CI 1.65-2.43). In nonelective surgery, independent predictors of mortality included procedures with an end-stoma (adjusted OR 1.86; 95% CI 1.58-2.18), age over 60 (adjusted OR 1.73; 95% CI 1.43-2.08), total colectomy (adjusted OR 1.68; 95% CI 1.27-2.22), vascular insufficiency as surgical indication (adjusted OR 1.58; 95% CI 1.32-1.90), nonprivate insurance coverage (adjusted OR 1.38; 95% CI 1.07-1.77) and malnutrition (adjusted OR 1.26; 95% CI 1.01-1.59). CONCLUSIONS: Patients receiving dialysis treatment have an increased risk of morbidity and mortality following colorectal surgery. Elective procedures are associated with a 10% rate of mortality in this population. Dialysis patients are especially susceptible to infectious and pulmonary complications after colorectal resection. Additional studies are necessary to refine risk stratification in this high-risk 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".