To assess in-hospital mortality and complication rates after radical cystectomy (RC) in patients with history of heart-valve replacement. Using the National Inpatient Sample (2000-2019), non-metastatic bladder cancer patients undergoing RC were stratified according to history of heart-valve replacement. Regression models (RM) predicted hospital outcomes. Of 25,535 RC patients, 250 (1.0%) harbored history of heart-valve replacement. Heart-valve replacement patients were older (median 74 vs. 70 years), more frequently male (87.2 vs. 80.6%), and more frequently had Charlson comorbidity index ≥3 (26.8 vs. 18.9%). In RC patients with history of heart-valve replacement vs. others, 62 vs. 2634 (24.8 vs. 10.4%) experienced cardiac complications, 28 vs. 3092 (11.2 vs. 12.2%) intraoperative complications, 11 vs. 1046 (4.4 vs. 4.1%) infections, <11 vs. 594 (<4.4 vs. 2.3%) perioperative bleeding, <11 vs. 699 (<4.4 vs. 2.8%) vascular complications, 74 vs. 6225 (29.6 vs. 24.7%) received blood transfusions, 37 vs. 3054 (14.8 vs. 12.1%) critical care therapy (CCT), and in-hospital mortality was recorded in <11 vs. 463 (<4.4 vs. 1.8%) patients. In multivariable RM, history of heart-valve replacement independently predicted cardiac complications (odds ratio 2.20, 95% confidence interval 1.62-2.99; p < 0.001). Conversely, no statically significant association was recorded between history of heart-valve replacement and length of stay, estimated hospital cost, intraoperative complications, perioperative bleeding, vascular complications, infections, blood transfusions, CCT use, and in-hospital mortality. Radical cystectomy patients with history of heart-valve replacement exhibited a 2.2-fold higher risk of cardiac complications, but no other complications, including no significantly higher in-hospital mortality.

To test for cancer specific mortality (CSM) differences after either radical prostatectomy (RP) or radiotherapy (RT) in incidental prostate cancer (IPCa) patients. Within the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015), IPCa patients were identified. Cumulative incidence plots as well as competing risks regression (CRR) models were fitted to address CSM after adjustment for other-cause mortality (OCM). Furthermore, a subgroup analysis was performed to test for CSM differences between RP and RT according to Gleason sum (GS 6,7, and 8-10). Of 1,466 IPCa patients, 770 (53%) underwent RP vs. 696 (47%) RT. Incidental PCa RT patients were older, and exhibited higher PSA, higher proportion of Gleason sum 8-10, and higher clinical T stage. In cumulative incidence plots, 5-year CSM rates adjusted for OCM were 0.9 for RP vs. 6.8% for RT (Δ = 5.9%). After multivariable adjustment for clinical characteristics (age, PSA, Gleason sum, and clinical T stage) as well as for OCM, RP was associated with a protective hazard ratio (HR) of 0.35 (95% confidence interval [CI] 0.15 - 0.78, p value = .01). Within Gleason sum 8-10 IPCA patients, RP was associated with a protective HR of 0.31 (P = .039). Incidental PCa RT-treated patients exhibited less favorable clinical characteristics than their RP counterparts. Despite full adjustment, RP was associated with a protective effect relative to RT. This effect exclusively applied to the Gleason sum 8-10 subgroup. In consequence, IPCa patients harboring Gleason sum 8-10 should ideally be considered for RP instead of RT.

Within the Surveillance, Epidemiology, and End Results database (2000–2019), we identified 5522 unilateral surgically treated non-metastatic chromophobe kidney cancer (chRCC) patients. This population was randomly divided into development vs. external validation cohorts. In the development cohort, the original Leibovich 2018 and GRANT categories were applied to predict 5- and 10-year cancer-specific survival (CSS). Subsequently, a novel multivariable nomogram was developed. Accuracy, calibration and decision curve analyses (DCA) tested the Cox regression-based nomogram as well as the Leibovich 2018 and GRANT risk categories in the external validation cohort. The accuracy of the Leibovich 2018 and GRANT models was 0.65 and 0.64 at ten years, respectively. The novel prognostic nomogram had an accuracy of 0.78 at ten years. All models exhibited good calibration. In DCA, Leibovich 2018 outperformed the novel nomogram within selected ranges of threshold probabilities at ten years. Conversely, the novel nomogram outperformed Leibovich 2018 for other values of threshold probabilities. In summary, Leibovich 2018 and GRANT risk categories exhibited borderline low accuracy in predicting CSS in North American non-metastatic chRCC patients. Conversely, the novel nomogram exhibited higher accuracy. However, in DCA, all examined models exhibited limitations within specific threshold probability intervals. In consequence, all three examined models provide individual predictions that might be suboptimal and be affected by limitations determined by the natural history of chRCC, where few deaths occur within ten years from surgery. Further investigations regarding established and novel predictors of CSS and relying on large sample sizes with longer follow-up are needed to better stratify CSS in chRCC.