Root Causes and Modifiability of 30-Day Hospital Readmissions after Radical Cystectomy for Bladder Cancer
Bibliographic record
Abstract
PURPOSE: Radical cystectomy is associated with high complication and rehospitalization rates. An understanding of the root causes of hospital readmissions and the modifiability of factors contributing to readmissions may decrease the morbidity associated with radical cystectomy. We characterize the indications for rehospitalization following radical cystectomy, and determine whether these indications represent immutable patient disease and procedure factors or whether they are modifiable. MATERIALS AND METHODS: From MarketScan® databases we identified patients younger than 65 years with a diagnosis of bladder cancer who underwent radical cystectomy between 2008 and 2011 and were readmitted to the hospital within 30 days of radical cystectomy. All associated ICD-9 codes in the index admission, subsequent outpatient claims and readmission claims were independently reviewed by 3 surgeons to determine a root cause of rehospitalization. Causes were broadly categorized as medical, surgical or infectious, and reviewers determined whether the readmission was modifiable. Multivariate logistical regression models were used to identify factors associated with rehospitalization. RESULTS: A total of 1,163 patients were included in the study and 242 (21%) were readmitted to the hospital within 30 days. Of these readmissions 26% were considered modifiable (kappa=0.71). Of the nonmodifiable readmissions an infectious cause accounted for 52% and a medical cause accounted for 48%, whereas of the modifiable readmissions 62% were due to surgical causes, 30% to medical and 8% to infectious causes. On multivariate analysis only discharge to a skilled nursing facility was associated with modifiable (OR 6.12, 95% CI 2.32-16.14) or nonmodifiable (OR 3.27, 95% CI 1.63-6.53) hospital readmissions. CONCLUSIONS: The majority of rehospitalizations after radical cystectomy are attributable its inherent morbidity. However, optimization of aspects of peri-cystectomy care could minimize the morbidity of radical cystectomy.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".