Assessment of a Clinical Trial-Derived Survival Model in Patients With Metastatic Castration-Resistant Prostate Cancer

• Published in: JAMA network open 2021-01, Vol.4 (1), p.e2031730-e2031730
• Main Authors: Coquet, Jean, Bievre, Nicolas, Billaut, Vincent, Seneviratne, Martin, Magnani, Christopher J, Bozkurt, Selen, Brooks, James D, Hernandez-Boussard, Tina
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Clinical trials; Decision Making, Computer-Assisted; Electronic Health Records; Health Informatics; Humans; Machine Learning; Male; Metastasis; Middle Aged; Models, Statistical; Mortality; Online Only; Original Investigation; Prognosis; Prostate cancer; Prostatic Neoplasms, Castration-Resistant - diagnosis; Prostatic Neoplasms, Castration-Resistant - epidemiology; Prostatic Neoplasms, Castration-Resistant - mortality; Randomized Controlled Trials as Topic; Survival Analysis; Young Adult
• ISSN: 2574-3805; 2574-3805
• DOI: 10.1001/jamanetworkopen.2020.31730

Randomized clinical trials (RCTs) are considered the criterion standard for clinical evidence. Despite their many benefits, RCTs have limitations, such as costliness, that may reduce the generalizability of their findings among diverse populations and routine care settings. To assess the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic castration-resistant prostate cancer (CRPC) when the model is applied to real-world data from electronic health records (EHRs). The RCT-trained model and patient data from the RCTs were obtained from the Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge for prostate cancer, which occurred from March 16 to July 27, 2015. This challenge included 4 phase 3 clinical trials of patients with metastatic CRPC. Real-world data were obtained from the EHRs of a tertiary care academic medical center that includes a comprehensive cancer center. In this study, the DREAM challenge RCT-trained model was applied to real-world data from January 1, 2008, to December 31, 2019; the model was then retrained using EHR data with optimized feature selection. Patients with metastatic CRPC were divided into RCT and EHR cohorts based on data source. Data were analyzed from March 23, 2018, to October 22, 2020. Patients who received treatment for metastatic CRPC. The primary outcome was the performance of an RCT-derived prognostic model that predicts survival among patients with metastatic CRPC when the model is applied to real-world data. Model performance was compared using 10-fold cross-validation according to time-dependent integrated area under the curve (iAUC) statistics. Among 2113 participants with metastatic CRPC, 1600 participants were included in the RCT cohort, and 513 participants were included in the EHR cohort. The RCT cohort comprised a larger proportion of White participants (1390 patients [86.9%] vs 337 patients [65.7%]) and a smaller proportion of Hispanic participants (14 patients [0.9%] vs 42 patients [8.2%]), Asian participants (41 patients [2.6%] vs 88 patients [17.2%]), and participants older than 75 years (388 patients [24.3%] vs 191 patients [37.2%]) compared with the EHR cohort. Participants in the RCT cohort also had fewer comorbidities (mean [SD], 1.6 [1.8] comorbidities vs 2.5 [2.6] comorbidities, respectively) compared with those in the EHR cohort. Of the 101 variables used in the RCT-derived model, 10 were not available in the EHR data set, 3 of w