Short‐ and long‐term outcomes of acute diverticulitis in patients with transplanted kidneys
Bibliographic record
Abstract
AIM: The safety of nonoperative treatment for patients with transplanted kidneys who develop acute diverticulitis is unclear. Our primary aim was to examine the long-term sequelae of nonoperative management in this group. METHOD: We performed a population-based retrospective cohort study using linked administrative databases housed at ICES in Ontario, Canada. We included adult (≥18 years) patients admitted with acute diverticulitis between April 2002 and December 2019. Patients with a functioning kidney transplant were compared with those without a transplant. The primary outcome was failure of conservative management (operation, drainage procedure or death due to acute diverticulitis) beyond 30 days. The cumulative incidence function and a Fine-Grey subdistribution hazard model were used to evaluate this outcome accounting for competing risks. RESULTS: We examined 165 patients with transplanted kidneys and 74 095 without. Patients with transplanted kidneys were managed conservatively 81% of the time at the index event versus 86% in nontransplant patients. Short-term outcomes were comparable, but cumulative failure of conservative management at 5 years occurred in 5.6% (95% CI 2.3%-11.1%) of patients with transplanted kidneys versus 2.1% (95% CI 2.0%-2.3%) in those without. Readmission for acute diverticulitis was also higher in transplanted patients at 5 years at 16.7% (95% CI 10.1%-24.7%) versus 11.6% (95% CI 11.3%-11.9%). Adjusted analyses showed increased failure of conservative management [subdistribution hazard ratio (sHR) 3.24, 95% CI 1.69-6.22] and readmissions (sHR 1.55, 95% CI 1.02-2.36) for patients with transplanted kidneys. CONCLUSION: Most patients with transplanted kidneys are managed conservatively for acute diverticulitis. Although long-term readmission and failure of conservative management is higher for this group than the nontransplant population, serious outcomes are infrequent, substantiating the safety of this approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".