Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
BJU Int. 2022 Oct;130(4):496-506. doi: 10.1111/bju.15740. Epub 2022 Apr 24.
To develop and validate an accurate, usable prediction model for other-cause mortality (OCM) in patients with prostate cancer diagnosed in the United States.
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.
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.
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.
开发和验证一种准确且可用的预测模型，用于预测在美国诊断为前列腺癌的患者的其他原因死亡率（OCM）。
使用 1999-2010 年全国健康和营养调查（NHANES）的数据进行模型训练，该研究纳入了年龄>40 岁的男性，随访至 2014 年。在前列腺、肺、结肠和卵巢癌症筛查试验（PLCO）前列腺癌队列中对模型进行验证，该队列纳入了 1993 年至 2001 年期间的患者，随访至 2015 年。在验证队列中评估时间依赖性曲线下面积（AUC）和校准。分析用于评估算法偏差。
在 2420 例患者的训练队列中，在幸存者的中位随访 8.8 年期间有 459 人死亡。最终模型包括 8 个预测因子：年龄、教育程度、婚姻状况、糖尿病、高血压、中风、体重指数和吸烟。在 8220 例患者的验证队列中，该模型在预测 10 年内 OCM 的 AUC 为 0.75。最终模型显著优于社会保障管理局生命表，并在种族、教育程度和婚姻状况亚组中表现出良好的预测性能。有证据表明预期寿命存在重大差异，而不仅仅由年龄决定，同一年龄的患者之间预期寿命的预测差异可达 10 年或以上。
使用两个全国性队列，我们开发并验证了一种用于美国治疗的前列腺癌患者 OCM 的简单而有用的预测模型，这将使我们能够根据指南更个性化地治疗患者。
