Michele Marchioni1, Maximilian Kriegmair2, Mathias Heck3, Thomas Amiel3, Francesco Porpiglia4, Enrico Ceccucci4, Riccardo Campi5, Andrea Minervini5, Andrea Mari5, Siska Van Bruwaene6, Estefania Linares7, Vital Hevia8, Mireia Musquera9, Mauricio D'Anna9, Ithaar Derweesh10, Aaron Bradshaw10, Riccardo Autorino11, Georgi Guruli11, Alessandro Veccia11, Eduard Roussel12, Maarten Albersen12, Nicola Pavan13, Francesco Claps13, Alessandro Antonelli14, Carlotta Palumbo14, Tobias Klatte15, Selcuk Erdem16, Maria Carmen Mir17. 1. Department of Medical, Oral and Biotechnological Sciences, Laboratory of Biostatistics, University "G. D'Annunzio" Chieti-Pescara, Chieti, Italy; Department of Urology, SS Annunziata Hospital, "G. D'Annunzio" University of Chieti, Chieti, Italy. 2. Department of Urology, University Medical Centre Mannheim, Mannheim, Germany. 3. Department of Urology, Technical University of Munich, Munich, Germany. 4. Department of Urology, School of Medicine, University of Turin-San Luigi Gonzaga Hospital, Turin, Italy. 5. Department of Urology, University of Florence, Careggi Hospital, Florence, Italy. 6. Department of Urology, AZ Groeninge, Kortrijk, Belgium. 7. Department of Urology, Hospital La Paz, Madrid, Spain. 8. Department of Urology, Hospital Ramon y Cajal, Madrid, Spain. 9. Department of Urology, Hospital Clinic, Barcelona, Spain. 10. Department of Urology, UC San Diego Moores Cancer Center, University of California San Diego School of Medicine, Louisiana Jolla, CA, USA. 11. Division of Urology, VCU Medical Center, Richmond, VA, USA. 12. Department of Urology, University Hospitals Leuven, Leuven, Belgium. 13. Urology Clinic, Department of Medical, Surgical and Health Science, University of Trieste, Trieste, Italy. 14. Department of Urology, University of Verona, AOUI Verona Hospital, Verona, Italy. 15. Department of Urology, Royal Bournemouth Hospital, University Hospitals Dorset, Bournemouth, UK. 16. Department of Urology, Faculty of Medicine, Istanbul University, Istanbul, Turkey. 17. Department of Urology, Fundacion Instituto Valenciano Oncologia, Valencia, Spain. Electronic address: mirmare@yahoo.es.
Abstract
BACKGROUND: Selection of patients for upfront cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC) has to be improved. OBJECTIVE: To evaluate a new scoring system for the prediction of overall mortality (OM) in mRCC patients undergoing CN. DESIGN, SETTING, AND PARTICIPANTS: We identified a total of 519 patients with synchronous mRCC undergoing CN between 2005 and 2019 from a multi-institutional registry (Registry for Metastatic RCC [REMARCC]). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Cox proportional hazard regression was used to test the main predictors of OM. Restricted mean survival time was estimated as a measure of the average overall survival time up to 36 mo of follow-up. The concordance index (C-index) was used to determine the model's discrimination. Decision curve analyses were used to compare the net benefit from the REMARCC model with International mRCC Database Consortium (IMDC) or Memorial Sloan Kettering Cancer Center (MSKCC) risk scores. RESULTS AND LIMITATIONS: The median follow-up period was 18 mo (interquartile range: 5.9-39.7). Our models showed lower mortality rates in obese patients (p = 0.007). Higher OM rates were recorded in those with bone (p = 0.010), liver (p = 0.002), and lung metastases (p < 0.001). Those with poor performance status (<80%) and those with more than three metastases had also higher OM rates (p = 0.026 and 0.040, respectively). The C-index of the REMARCC model was higher than that of the MSKCC and IMDC models (66.4% vs 60.4% vs 60.3%). After stratification, 113 (22.0%) patients were classified to have a favorable (no risk factors), 202 (39.5%) an intermediate (one or two risk factors), and 197 (38.5%) a poor (more than two risk factors) prognosis. Moreover, 72 (17.2%) and 51 (13.9%) patients classified as having an intermediate and a poor prognosis according to MSKCC and IMDC categories, respectively, would be reclassified as having a good prognosis according to the REMARCC score. CONCLUSIONS: Our findings confirm the relevance of tumor and patient features for the risk stratification of mRCC patients and clinical decision-making regarding CN. Further prospective external validations are required for the scoring system proposed herein. PATIENT SUMMARY: Current stratification systems for selecting patients for kidney removal when metastatic disease is shown are controversial. We suggest a system that includes tumor and patient features besides the systems already in use, which are based on blood tests.
BACKGROUND: Selection of patients for upfront cytoreductive nephrectomy (CN) in metastatic renal cell carcinoma (mRCC) has to be improved. OBJECTIVE: To evaluate a new scoring system for the prediction of overall mortality (OM) in mRCC patients undergoing CN. DESIGN, SETTING, AND PARTICIPANTS: We identified a total of 519 patients with synchronous mRCC undergoing CN between 2005 and 2019 from a multi-institutional registry (Registry for Metastatic RCC [REMARCC]). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Cox proportional hazard regression was used to test the main predictors of OM. Restricted mean survival time was estimated as a measure of the average overall survival time up to 36 mo of follow-up. The concordance index (C-index) was used to determine the model's discrimination. Decision curve analyses were used to compare the net benefit from the REMARCC model with International mRCC Database Consortium (IMDC) or Memorial Sloan Kettering Cancer Center (MSKCC) risk scores. RESULTS AND LIMITATIONS: The median follow-up period was 18 mo (interquartile range: 5.9-39.7). Our models showed lower mortality rates in obesepatients (p = 0.007). Higher OM rates were recorded in those with bone (p = 0.010), liver (p = 0.002), and lung metastases (p < 0.001). Those with poor performance status (<80%) and those with more than three metastases had also higher OM rates (p = 0.026 and 0.040, respectively). The C-index of the REMARCC model was higher than that of the MSKCC and IMDC models (66.4% vs 60.4% vs 60.3%). After stratification, 113 (22.0%) patients were classified to have a favorable (no risk factors), 202 (39.5%) an intermediate (one or two risk factors), and 197 (38.5%) a poor (more than two risk factors) prognosis. Moreover, 72 (17.2%) and 51 (13.9%) patients classified as having an intermediate and a poor prognosis according to MSKCC and IMDC categories, respectively, would be reclassified as having a good prognosis according to the REMARCC score. CONCLUSIONS: Our findings confirm the relevance of tumor and patient features for the risk stratification of mRCC patients and clinical decision-making regarding CN. Further prospective external validations are required for the scoring system proposed herein. PATIENT SUMMARY: Current stratification systems for selecting patients for kidney removal when metastatic disease is shown are controversial. We suggest a system that includes tumor and patient features besides the systems already in use, which are based on blood tests.
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