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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".