Predicting the 10-year risk of death from other causes in men with localized prostate cancer using patient-reported factors: Development of a tool.
Predicting the 10-year risk of death from other causes in men with localized prostate cancer using patient-reported factors: Development of a tool.
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DOI：
10.1371/journal.pone.0240039
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Ware JE
中科院分区：
文献类型：
--
作者：
Frendl DM;FitzGerald G;Epstein MM;Allison JJ;Sokoloff MH;Ware JE
开发一种工具，用于估计患有局限性前列腺癌的男性因其他原因死亡的10年风险。我们从监测流行病学和最终结果-联邦医疗保险健康结果调查数据库中确定了2425名患者，年龄和年龄；80岁，从1/1 1998-12/31/2009年1月1日至12/31/2009年1月1日至12月31日期间新诊断为临床T1-T3a期前列腺癌，随访至2013年2月28日。考虑到年龄、患者报告的并存疾病、SF-36健康调查的各部分分数和项目、日常生活能力和社会人口学特征，我们建立了10年其他原因死亡率的Fine和灰色竞争风险模型。模型辨别和校正与社会保障寿命表死亡风险估计的预测进行了比较。在平均7.7年的随访中，76名男性死于前列腺癌，465名死于其他原因。对10年其他原因死亡风险的最强预测因素包括确诊时年龄增加、查尔森共病指数评分近似值较高、患者报告的总体健康状况较差(一般或较差与优秀-良好)、确诊时吸烟、婚姻状况(所有其他因素与已婚患者相比)(均P<；0.05)。模式歧视比社会保障生命表(c指数分别为0.70和0.59)有所改善。预测比社会保障生命表中的预测更准确，后者高估了我们人口中的风险。我们提供了一种工具，用于在使用治疗前患者报告的特征做出治疗前列腺癌的决定时，估计10年内死于其他原因的风险。
To develop a tool for estimating the 10-year risk of death from other causes in men with localized prostate cancer. We identified 2,425 patients from the Surveillance Epidemiology and End Results—Medicare Health Outcomes Survey database, age <80, newly diagnosed with clinical stage T1-T3a prostate cancer from 1/1/1998-12/31/2009, with follow-up through 2/28/2013. We developed a Fine and Gray competing-risks model for 10-year other cause mortality considering age, patient-reported comorbid medical conditions, component scores and items of the SF-36 Health Survey, activities of daily living, and sociodemographic characteristics. Model discrimination and calibration were compared to predictions from Social Security life table mortality risk estimates. Over a median follow-up of 7.7 years, 76 men died of prostate-specific causes and 465 died of other causes. The strongest predictors of 10-year other cause mortality risk included increasing age at diagnosis, higher approximated Charlson Comorbidity Index score, worse patient-reported general health (fair or poor vs. excellent-good), smoking at diagnosis, and marital status (all other vs. married) (all p<0.05). Model discrimination improved over Social Security life tables (c-index of 0.70 vs. 0.59, respectively). Predictions were more accurate than predictions from the Social Security life tables, which overestimated risk in our population. We provide a tool for estimating the 10-year risk of dying from other causes when making decisions about treating prostate cancer using pre-treatment patient-reported characteristics.
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