Cytoreductive nephrectomy in the elderly: a population‐based cohort from the USA
Bibliographic record
Abstract
UNLABELLED: Study Type - Therapy (cohort) Level of Evidence 2b What's known on the subject? and What does the study add? While cytoreductive nephrectomy is associated with a survival benefit in the context of metastatic renal cell carcinoma, the rates of morbidity and perioperative mortality remain non-negligible. For example, perioperative mortality may be as high as 21% in elderly patients. The study shows that perioperative death amongst the elderly was substantially lower than what was previously reported from a single institutional report. Nonetheless, postoperative adverse outcomes were non-negligible in elderly patients relative to their younger counterparts. In consequence, these rates should be discussed at informed consent and a rigorous patient selection remains essential. OBJECTIVE: • To examine the rate of perioperative mortality (PM), and other adverse outcomes in 'elderly' patients treated with cytoreductive nephrectomy (CNT). MATERIAL AND METHODS: • Patients who underwent CNT for metastatic renal cell carcinoma were abstracted from the Nationwide Inpatient Sample (1998-2007). 'Elderly' was defined as ≥ 75 years, according to previous definition. • Endpoints consisted of PM, intraoperative and postoperative complications, blood transfusions and length of stay. • We adjusted for the effect of elderly status within five separate logistic regression models. Covariates consisted of comorbidity, race, gender, year of surgery and hospital region. RESULTS: • Overall, CNT was performed in 504 (15.3%) elderly patients and in 2796 (84.7%) 'younger' patients (<75 years). • The rate of PM was 4.8% in elderly patients vs 1.9% in the younger patients (P < 0.001). Similarly, the rates of blood transfusions (29.8 vs 21.5%), postoperative complications (27.8 vs 22.8%), and prolonged length of stay (≥ 8 days) were higher in the elderly (45.0 vs 32.0%; all P < 0.001). • In multivariable analyses, elderly patients were 2.2-, 1.5-, and 1.6 fold more likely to experience PM, to receive a blood transfusion and to be hospitalized ≥ 8 days than the younger patients. CONCLUSIONS: • Although the rate of PM was substantially lower than 21%, elderly patients are significantly more likely to die after this type of surgery, to receive a transfusion, and to experience a prolonged length of stay. • These facts and figures should be discussed at informed consent and a rigorous patient selection is essential.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".