Development and validation of a life expectancy calculator for US patients with prostate cancer

Abstract

Objective To develop and validate an accurate, usable prediction model for other‐cause mortality (OCM) in patients with prostate cancer diagnosed in the United States. Materials and Methods Model training was performed using the National Health and Nutrition Examination Survey 1999–2010 including men aged >40 years with follow‐up to the year 2014. The model was validated in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial prostate cancer cohort, which enrolled patients between 1993 and 2001 with follow‐up to the year 2015. Time‐dependent area under the curve (AUC) and calibration were assessed in the validation cohort. Analyses were performed to assess algorithmic bias. Results The 2420 patient training cohort had 459 deaths over a median follow‐up of 8.8 years among survivors. The final model included eight predictors: age; education; marital status; diabetes; hypertension; stroke; body mass index; and smoking. It had an AUC of 0.75 at 10 years for predicting OCM in the validation cohort of 8220 patients. The final model significantly outperformed the Social Security Administration life tables and showed adequate predictive performance across race, educational attainment, and marital status subgroups. There is evidence of major variability in life expectancy that is not captured by age, with life expectancy predictions differing by 10 or more years among patients of the same age. Conclusion Using two national cohorts, we have developed and validated a simple and useful prediction model for OCM for patients with prostate cancer treated in the United States, which will allow for more personalized treatment in accordance with guidelines.

Do not treat Bill Gates for prostate cancer! Algorithmic bias and causality in medical prediction

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Chase et al [1] recently published in the BJUI a lifeexpectancy prediction model for patients with prostate cancer. They expressly recommended this be used to guide treatment decision-making for patients, and even provide an easy-to-use app for doing so.

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“…Life expectancy is indeed a critical determinant of prostate cancer treatment and many current approaches, such as using social security tables, or informal clinical assessment, are known to be invalid [2]. The problem with the Chase et al [1] model is that it includes education and marital status. If you take two men with identical tumours, and who are also the same age and have the same comorbidities, you might recommend one but not the other to undergo curative treatment based on his higher level of education, or because he is married.…”

“…The problem with the Chase et al. [1] model is that it includes education and marital status. If you take two men with identical tumours, and who are also the same age and have the same comorbidities, you might recommend one but not the other to undergo curative treatment based on his higher level of education, or because he is married.…”

“…As it turns out, Bill Gates, an unmarried college drop‐out billionaire, might be one of the men who would be given a different prediction because the Chase et al. [1] model includes education and marital status. Indeed, the Bill Gates problem tells us exactly why we should avoid correlates of social determinants of health in prediction models.…”

“…Laudably, Chase et al. [1] provide a version of their model that avoids education and marital status as inputs. I would encourage anyone interested in using the model to use this version.…”