Successful external validation of a model to predict other cause mortality in localized prostate cancer. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Kent, Matthew; Penson, David; Albertsen, Peter; Goodman, Michael; Hamilton, Ann; Stanford, Janet; Stroup, Antoinette; Ehdaie, Behfar; Scardino, Peter; Vickers, Andrew
• Is Part Of: BMC medicine. Volume 14:Issue 1(2016)
• Page Start: 1
• Page End: 7
• Publication Date: 2016-12
• Subjects: Life expectancy -- Clinical decision support -- Prediction -- Radical prostatectomy; Medicine -- Periodicals; 610.5
• DOI: 10.1186/s12916-016-0572-z
• Languages: English
• ISSNs: 1741-7015

Abstract

Abstract Background Although life expectancy estimation is vital to decision making for localized prostate cancer, there are few, if any, valid and usable tools. Our goal was to create and validate a prediction model for other cause mortality in localized prostate cancer patients that could aid clinician's initial treatment decisions at the point of care. Methods We combined an adjusted Social Security Administration table with a subset of comorbidities from a UK actuarial life expectancy model. Life tables were adjusted on the basis of survival data from a cohort of almost 10, 000 radical prostatectomy patients treated at four major US academic institutions. Comorbidity-specific odds ratios were calculated and incorporated with baseline risk of mortality. We externally validated the model on 2898 patients from the Prostate Cancer Outcomes Study, which included men diagnosed with prostate cancer in six SEER cancer registries. These men had sufficient follow-up for our endpoints of 10- and 15-year mortality and also had self-reported comorbidity data. Results Life expectancy for prostate cancer patients were close to that of a typical US man who was 3 years younger. On external validation, 10- and 15-year concordance indexes were 0.724 and 0.726, respectively. Our model exhibited excellent calibration. Taking into account differences between how comorbidities are used in the model versus how they were recorded in the validation cohort, calibration would improve for most patients, but there would be overestimation of the risk of death in the oldest and sickest patients. Conclusions We successfully created and externally validated a new life expectancy prediction model that, while imperfect, has clear advantages to any alternative. We urge consideration of its use in counseling patients with localized prostate cancer.