Nomogram predicting 30‐day mortality after nephrectomy in the contemporary era: Results from the SEER database
Bibliographic record
Abstract
OBJECTIVES: To assess contemporary 30-day mortality rates after partial and radical nephrectomy in USA, and to develop a predictive model of 30-day mortality. METHODS: We relied on the National Cancer Institute Surveillance, Epidemiology and End Results database. A multivariable logistic regression analysis was fitted to predict 30-day mortality. A nomogram was built based on the coefficients of the logit function. Internal validation was carried out using the leave-one-out cross-validation. Calibration was graphically investigated. RESULTS: A total of 102 146 patients who underwent partial nephrectomy (n = 36 425; 35.7%) or radical nephrectomy (n = 65 721; 64.3%) between 2005 and 2015 were included in the analysis. The median age at diagnosis was 62 years. A total of 11 921 (11.7%) patients were African American. The clinical stage was T1-T2 in 79 452 (77.8%), T3 in 16 141 (15.8%) and T4/T1-4-M1 in 6553 (6.4%) patients. Overall, 497 deaths occurred during the initial 30 days after nephrectomy (0.49% 30-day mortality rate). Stratified by type of surgery, the 30-day mortality rate was 0.16% for partial nephrectomy and 0.67% for radical nephrectomy. At univariate analyses, age, tumor size, stage and surgical procedure emerged as predictors of 30-day mortality (all P < 0.001). All of these covariates were included in the multivariable logistic regression model. The area under the curve after leave-one-out cross-validation was 0.808 (95% confidence interval 0.788-0.828), and the model showed good calibration in the range of predicted probability <10%. CONCLUSIONS: Contemporary rates of 30-day mortality in patients undergoing radical or partial nephrectomy are very low. Age and tumor stage are key determinants of 30-day mortality. We present a predictive model that provides individual probabilities of 30-day mortality after nephrectomy, and it can be used for patient counseling prior surgery.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".