MP46-02 PROSTATE CANCER SCREENING NOMOGRAM PREDICTING 15-YEAR RISK OF CLINICALLY SIGNIFICANT PROSTATE CANCER, DEVELOPED USING COMPETING RISK ANALYSIS (2018)

You have accessJournal of UrologyProstate Cancer: Detection & Screening IV1 Apr 2018MP46-02 PROSTATE CANCER SCREENING NOMOGRAM PREDICTING 15-YEAR RISK OF CLINICALLY SIGNIFICANT PROSTATE CANCER, DEVELOPED USING COMPETING RISK ANALYSIS Michael Brooks, Sigrid Carlsson, Alexander Zajichek, Kevin Chagin, Jonas Hugosson, Michael Kattan, and Andrew Stephenson Michael BrooksMichael Brooks More articles by this author , Sigrid CarlssonSigrid Carlsson More articles by this author , Alexander ZajichekAlexander Zajichek More articles by this author , Kevin ChaginKevin Chagin More articles by this author , Jonas HugossonJonas Hugosson More articles by this author , Michael KattanMichael Kattan More articles by this author , and Andrew StephensonAndrew Stephenson More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1461AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Clinicians aim to reduce over-diagnosis of low-risk prostate cancer (PC), focusing prostate specific antigen (PSA) screening efforts on diagnosis of intermediate and high-risk disease. Increased risk of other-cause mortality (OCM) reduces the probability of diagnosing clinically significant PC. No screening models incorporate both low-risk prostate cancer diagnosis (PCDx) and other cause mortality as competing events in predicting clinically significant PCDx. Therefore our objective was to use competing-risk analysis to develop PC screening nomograms for intermediate and high-risk PCDx at 15-years. METHODS We developed PC screening nomograms for both patients without and those with a known PSA using demographic and follow-up data from two large randomized screening trials: the Goteborg Screening Trial (Lancet Oncol. 2010;11:725-32) and Prostate, Lung, Colon, and Ovarian (PLCO) Cancer Screening Trial (N Engl J Med. 2009;360:1310-9). For model development without a known PSA, patients with screening prior to trial entry were excluded, leaving 59,951 total subjects. Demographic information including age, family history of PC diagnosis, ethnicity, Charlson Comorbidity Index, marital status, education, and control vs. screening arm were used as predictive variables. For model development with a known PSA, only screening-arm patients were included, with PSA as a continuous variable. OCM and low-risk PCDx were treated as competing risks for intermediate or high risk PCDx. Bootstrapping was used to reduce over-fit. Concordance index (c-index) and calibration plots were used to assess predictive accuracy. RESULTS On univariable analysis, all demographics and PSA were significant (P<0.05) for predicting intermediate or high-risk PCDx at 15 years. All demographic variables were significant on multivariable analysis except ethnicity when PSA was included as a continuous variable. Nomogram discriminative accuracy predicting intermediate or high-risk PCDx improved substantially when a single PSA was included, c-index 0.62 vs 0.77, respectively. Calibration plots revealed well-calibrated estimates, without and with inclusion of PSA. CONCLUSIONS Using competing risk analysis, we developed PC screening nomograms, with good accuracy in predicting clinically significant PCDx. A single baseline PSA improved nomogram discrimination substantially. In future efforts, we plan to combine these nomograms with other screening models to develop an integrated screening decision aid for multiple outcomes. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e606 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Michael Brooks More articles by this author Sigrid Carlsson More articles by this author Alexander Zajichek More articles by this author Kevin Chagin More articles by this author Jonas Hugosson More articles by this author Michael Kattan More articles by this author Andrew Stephenson More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...