Alessandro Larcher1, Vincent Trudeau2, Paolo Dell'Oglio3, Zhe Tian4, Katharina Boehm5, Nicola Fossati6, Umberto Capitanio7, Alberto Briganti7, Francesco Montorsi7, Pierre Karakiewicz2. 1. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada; Division of Oncology, Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy. Electronic address: alelarcher@gmail.com. 2. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada; Department of Urology, University of Montreal Health Center, Montreal, QC, Canada. 3. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada; Division of Oncology, Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy. 4. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. 5. Cancer Prognostics and Health Outcomes Unit, University of Montreal Health Center, Montreal, QC, Canada; Martini-Clinic, Prostate Cancer Center Hamburg-Eppendorf, Hamburg, Germany. 6. Division of Oncology, Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy; Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY. 7. Division of Oncology, Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.
Abstract
OBJECTIVE: To predict the risk of cancer-specific mortality (CSM) or other-cause mortality (OCM) for T1 kidney cancer patients, aiming at identifying those who would benefit from surgery over observation. PATIENTS AND METHODS: Overall, 11,192 T1 kidney cancer patients treated with surgery or observation in the Surveillance, Epidemiology, and End Results-Medicare database were assessed. A competing risk regression (CRR) model was fitted to predict CSM and OCM after surgery or observation. Covariates consisted of age, gender, race, Charlson comorbidity index (CCI), history of acute kidney injury or chronic kidney disease, tumor size, and year of diagnosis. RESULTS: At a median follow-up of 64 months, the 5-year rates of CSM and OCM were 6.7% and 24%, respectively. At CRR predicting CSM, surgery (hazard ratio [HR] 0.46; P < .0001) and year of diagnosis (HR 0.96; P < .0001) were associated with lower CSM risk. Conversely, age (HR 1.05; P < .0001), CCI (HR 1.07; P < .0001), and tumor size (HR 1.03; P < .0001) were associated with higher CSM risk. At CRR predicting OCM, surgery (HR 0.66; P < .0001), female gender (HR 0.83; P < .0001), Other race (HR 0.82; P < .0001), and year of diagnosis (HR 0.95; P < .0001) were associated with lower OCM risk. Conversely, age (HR 1.06; P < .0001), African American race (HR 1.16; P < .01), CCI (HR 1.17; P < .0001), and acute kidney injury or chronic kidney disease (HR 1.35; P < .0001) were associated with higher OCM risk. CONCLUSION: The benefit of surgery over observation was more pronounced in younger and healthier patients with larger tumors. The proposed model can aid in clinical decision-making, providing crucial information on CSM and OCM risk after either treatment modality.
OBJECTIVE: To predict the risk of cancer-specific mortality (CSM) or other-cause mortality (OCM) for T1 kidney cancerpatients, aiming at identifying those who would benefit from surgery over observation. PATIENTS AND METHODS: Overall, 11,192 T1 kidney cancerpatients treated with surgery or observation in the Surveillance, Epidemiology, and End Results-Medicare database were assessed. A competing risk regression (CRR) model was fitted to predict CSM and OCM after surgery or observation. Covariates consisted of age, gender, race, Charlson comorbidity index (CCI), history of acute kidney injury or chronic kidney disease, tumor size, and year of diagnosis. RESULTS: At a median follow-up of 64 months, the 5-year rates of CSM and OCM were 6.7% and 24%, respectively. At CRR predicting CSM, surgery (hazard ratio [HR] 0.46; P < .0001) and year of diagnosis (HR 0.96; P < .0001) were associated with lower CSM risk. Conversely, age (HR 1.05; P < .0001), CCI (HR 1.07; P < .0001), and tumor size (HR 1.03; P < .0001) were associated with higher CSM risk. At CRR predicting OCM, surgery (HR 0.66; P < .0001), female gender (HR 0.83; P < .0001), Other race (HR 0.82; P < .0001), and year of diagnosis (HR 0.95; P < .0001) were associated with lower OCM risk. Conversely, age (HR 1.06; P < .0001), African American race (HR 1.16; P < .01), CCI (HR 1.17; P < .0001), and acute kidney injury or chronic kidney disease (HR 1.35; P < .0001) were associated with higher OCM risk. CONCLUSION: The benefit of surgery over observation was more pronounced in younger and healthier patients with larger tumors. The proposed model can aid in clinical decision-making, providing crucial information on CSM and OCM risk after either treatment modality.