The Effect of Chronic Kidney Disease on Adverse In‐Hospital Outcomes at Radical Prostatectomy
Bibliographic record
Abstract
OBJECTIVE: Radical prostatectomy (RP) may be a treatment option for prostate cancer in patients with chronic kidney disease (CKD). However, the effect of CKD on adverse in-hospital outcomes after RP is not well known. METHODS: Descriptive analyses, propensity score matching (PSM), and multivariable logistic and Poisson regression models were used to address National Inpatient Sample RP patients between 2005 and 2019. CKD severity was stratified as mild (stage I/II) versus moderate (stage III) versus severe (stage IV/V). RESULTS: Of 191 050 RP patients, 4349 (2.3%) had CKD. Of those, 2301 (52.9%), 1416 (32.6%), and 632 (14.5%) were classified as mild, moderate, or severe CKD, respectively. The CKD rate increased from 0.3% to 5.6% (2005-2019, EAPC: + 15.3%, p < 0.001). CKD patients invariably exhibited higher rates of adverse in-hospital outcomes, except for in-hospital mortality. The absolute differences were largest for overall complications (+ 12.5%), length of stay > 2 days (+ 11.8%), and blood transfusions (+ 3.7%, all p < 0.001). CKD was an independent predictor in all comparisons except for in-hospital mortality (p < 0.05). The detrimental effect was most pronounced for dialysis for acute kidney failure (multivariable odds ratio [OR] 10.49), genitourinary complications (OR: 2.47), and critical care therapies (OR: 2.45, all p < 0.001). Finally, a dose-response relationship of CKD severity (mild vs. moderate vs. severe) and its effect on adverse in-hospital outcomes was observed in seven of 14 comparisons. CONCLUSIONS: CKD patients invariably exhibited higher rates of adverse in-hospital outcomes after RP. The presence of CKD should be carefully considered when RP represents a management option.
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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 | 0.009 |
| 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 | 0.000 | 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".