Adverse renal outcomes in subjects undergoing nephrectomy for renal tumors: a population-based analysis
Bibliographic record
Abstract
Background: There has been increasing interest in determining renal outcomes after nephrectomy for renal tumors. Previous studies have not assessed all relevant risk factors, including proteinuria. Objective: We sought to determine the risk and predictors for the development of adverse renal outcomes in a population-based cohort of subjects undergoing partial or complete nephrectomy. Design, Setting, and Participants: A large population-based data set was used to identify all subjects undergoing nephrectomy in Alberta, Canada, from 2002 to 2007 using administrative codes. Comorbid conditions were determined using validated algorithms, and baseline estimated glomerular filtration rate (eGFR) and proteinuria status were determined. Measurements: Postsurgical outcomes of end-stage renal disease, acute dialysis, chronic kidney disease (CKD) (eGFR < 30 mL/min per 1.73 m(2)), and rapidly progressive CKD (eGFR < 60 mL/min per 1.73 m(2) and eGFR loss 4 mL/min per 1.73 m(2) per year) were assessed. The risk and risk factors for developing the composite renal outcome were determined using a multivariable Cox proportional hazards model. Results and Limitations: Of 1151 subjects, 10.5% developed an adverse renal outcome over a mean of 32 mo. Complete (vs. partial) nephrectomy was associated with a hazard ratio (HR) of 1.75 (95% confidence interval [CI], 1.02-2.99) for the primary outcome, as was lower baseline eGFR. Subjects with proteinuria were more likely to experience the primary outcome (42% vs. 9%), conferring an adjusted HR of 2.40 (95% CI, 1.47-3.88). Conclusions: Clinically important adverse renal outcomes are common in patients undergoing nephrectomy for renal tumors. In addition to baseline eGFR and the extent of the renal mass removed, proteinuria is a strong independent risk factor. Assessment of proteinuria, in addition to other risk factors, should be performed to inform prognosis and the optimal treatment 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".