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      Survival Outcomes Associated With Cytoreductive Nephrectomy in Patients With Metastatic Clear Cell Renal Cell Carcinoma

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          Key Points

          Question

          Is cytoreductive nephrectomy associated with improved overall survival for patients with metastatic clear cell renal cell carcinoma?

          Findings

          In this cohort study of 12 766 patients, receipt of a cytoreductive nephrectomy was not associated with improved overall survival using instrumental variable analysis to adjust for bias due to unmeasured confounding. Methods that do not adjust for this bias would have suggested a substantial overall survival benefit.

          Meaning

          Consistent with contemporary Level I evidence, instrumental variable analysis demonstrated that cytoreductive nephrectomy was not associated with improved overall survival for patients with metastatic clear cell renal cell carcinoma.

          Abstract

          This cohort study of patients with metastatic clear cell renal cell carcinoma assesses whether cytoreductive nephrectomy is associated with improved overall survival.

          Abstract

          Importance

          Level I evidence has failed to demonstrate an overall survival (OS) advantage for cytoreductive nephrectomy in patients with metastatic clear cell renal cell carcinoma (ccRCC) in the modern era, which is at odds with observational studies reporting a marked OS benefit associated with these operations. These observational studies were not designed to adjust for unmeasured confounding.

          Objective

          To assess whether cytoreductive nephrectomy is associated with improved OS in patients with metastatic ccRCC.

          Design, Setting, and Participants

          This cohort study identified patients with metastatic ccRCC in the National Cancer Database from January 1, 2006, to December 31, 2016, who received systemic targeted therapy. The analysis was finalized on July 23, 2021.

          Exposures

          Receipt of cytoreductive nephrectomy.

          Main Outcomes and Measures

          The primary outcome was OS from the date of diagnosis to death or censoring at last follow-up. Distance from the patients’ zip code of residence to the treating facility was identified as a valid instrument and was used in a 2-stage residual inclusion instrumental variable analysis. Conventional adjustments for selection bias, multivariable Cox proportional hazards regression, and propensity score matching were performed for comparison. Measured covariates adjusted for in all analyses included age, sex, race, Charlson-Deyo score, facility type, year of diagnosis, clinical T stage, and clinical N stage.

          Results

          The final study population included 12 766 patients (median age, 63 years; IQR, 56-70 years; 8744 [68%] male; 11 206 [88%] White). Cytoreductive nephrectomy was performed in 5005 patients (39%). Conventional adjustments for selection bias demonstrated a significant OS benefit associated with cytoreductive nephrectomy (multivariable Cox proportional hazards regression: hazard ratio [HR], 0.49; 95% CI, 0.47-0.51; propensity score matching: HR, 0.48; 95% CI, 0.46-0.50). Instrumental variable estimates did not demonstrate an association between cytoreductive nephrectomy and OS (HR, 0.92; 95% CI, 0.78-1.09).

          Conclusions and Relevance

          Instrumental variable analysis did not demonstrate a survival advantage associated with cytoreductive nephrectomy for patients with metastatic ccRCC. This discrepancy likely reflects the fact that surgical indication for cytoreductive nephrectomy is primarily driven by factors that are not commonly measured or available in observational data sets.

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          Most cited references32

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          • Abstract: found
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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

            The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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              Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.

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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                16 May 2022
                May 2022
                16 May 2022
                : 5
                : 5
                : e2212347
                Affiliations
                [1 ]Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center, Tampa, Florida
                [2 ]Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, Florida
                [3 ]Department of Urology, University of Michigan Medical School, Ann Arbor
                [4 ]Department of Urology, Beth Israel Deaconess Medical Center, Boston, Massachusetts
                [5 ]Department of Urology, University of Iowa Hospitals & Clinics, Iowa City
                Author notes
                Article Information
                Accepted for Publication: March 23, 2022.
                Published: May 16, 2022. doi:10.1001/jamanetworkopen.2022.12347
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Chakiryan NH et al. JAMA Network Open.
                Corresponding Author: Nicholas H. Chakiryan, MD, Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Dr, Tampa, FL 33612 ( nicholas.chakiryan@ 123456moffitt.org ).
                Author Contributions: Dr Chakiryan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Chakiryan, Jiang, Hajiran, Hugar, Gilbert.
                Acquisition, analysis, or interpretation of data: Chakiryan, Gore, Reich, Dunn, Gillis, Green, Hugar, Zemp, Zhang, Jain, Chahoud, Spiess, Manley, Sexton, Hollenbeck, Gilbert.
                Drafting of the manuscript: Chakiryan, Hajiran, Hugar.
                Critical revision of the manuscript for important intellectual content: Chakiryan, Gore, Reich, Dunn, Jiang, Gillis, Green, Zemp, Zhang, Jain, Chahoud, Spiess, Manley, Sexton, Hollenbeck, Gilbert.
                Statistical analysis: Chakiryan, Gore, Reich, Dunn, Green.
                Administrative, technical, or material support: Jiang, Gillis, Hugar, Spiess, Manley, Hollenbeck, Gilbert.
                Supervision: Hajiran, Zemp, Spiess, Manley, Sexton, Gilbert.
                Conflict of Interest Disclosures: Dr Reich reported receiving grants from the National Institutes of Health during the conduct of the study. Dr Zhang reported receiving personal fees from Pfizer and Bayer outside the submitted work. Dr Jain reported receiving personal fees from Aveo Pharma, Seattle Genetics, Gilead, Bristol Myers Squibb, and EMD Serono outside the submitted work. Dr Chahoud reported receiving consultancy fees from Aveo Pharma, Exelixis, and other from Pfizer outside the submitted work. Dr Manley reported serving as an Expert Opinion National Comprehensive Cancer Network Kidney Cancer Panel Member for Merck. Dr Sexton reported serving on the advisory board for Urogen Pharmaceuticals outside the submitted work. Dr Hollenbeck reported receiving grants from the Agency for Healthcare Research and Quality, American Cancer Society, and National Cancer Institute and serving as an associate editor for Urology outside the submitted work. No other disclosures were reported.
                Additional Contributions: Arvid Sjölander, PhD, Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden, and Andrew Ying, PhD, Westmead Hospital, Sydney, Australia (creators of the ivtools and tsriadditive R packages, respectively), provided transparent and timely communication regarding the use and underlying mechanics of their statistical code. They were not compensated for their work.
                Article
                zoi220366
                10.1001/jamanetworkopen.2022.12347
                9112069
                35576003
                99ed3eb2-32ab-4512-9a75-e7819338a6c8
                Copyright 2022 Chakiryan NH et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 18 January 2022
                : 23 March 2022
                Categories
                Research
                Original Investigation
                Online Only
                Oncology

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