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Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline. Part I: Risk Stratification, Shared Decision Making, and Care Options.临床局限性前列腺癌:AUA/ASTRO/SUO 指南。第 I 部分:风险分层、共同决策和治疗选择。
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Trends in Management for Patients With Localized Prostate Cancer, 1990-2013.1990 - 2013年局限性前列腺癌患者的管理趋势
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Predicting Life Expectancy in Men Diagnosed with Prostate Cancer.预测前列腺癌男性患者的预期寿命
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Undertreatment of Men in Their Seventies with High-risk Nonmetastatic Prostate Cancer.七十岁以上高危局限性前列腺癌男性治疗不足。
Eur Urol. 2015 Jul;68(1):53-8. doi: 10.1016/j.eururo.2014.12.026. Epub 2015 Mar 23.
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Self-Reported Health Status Predicts Other-Cause Mortality in Men with Localized Prostate Cancer: Results from the Prostate Cancer Outcomes Study.自我报告的健康状况可预测局限性前列腺癌男性的其他原因死亡率:前列腺癌结局研究结果
J Gen Intern Med. 2015 Jul;30(7):924-34. doi: 10.1007/s11606-014-3171-8. Epub 2015 Feb 13.
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Cancer statistics, 2015.癌症统计数据,2015 年。
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Prediction of long-term other-cause mortality in men with early-stage prostate cancer: results from the Prostate Cancer Outcomes Study.早期前列腺癌男性患者长期其他原因死亡率的预测:前列腺癌结局研究结果
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Variation in treatment associated with life expectancy in a population-based cohort of men with early-stage prostate cancer.基于人群的早期前列腺癌男性队列中,与预期寿命相关的治疗差异。
Cancer. 2014 Dec 1;120(23):3642-50. doi: 10.1002/cncr.28926. Epub 2014 Jul 17.
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利用患者报告的因素预测局限性前列腺癌男性患者因其他原因导致的 10 年死亡风险:工具的开发。

Predicting the 10-year risk of death from other causes in men with localized prostate cancer using patient-reported factors: Development of a tool.

机构信息

Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America.

Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States of America.

出版信息

PLoS One. 2020 Dec 7;15(12):e0240039. doi: 10.1371/journal.pone.0240039. eCollection 2020.

DOI:10.1371/journal.pone.0240039
PMID:33284845
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7721137/
Abstract

OBJECTIVE

To develop a tool for estimating the 10-year risk of death from other causes in men with localized prostate cancer.

SUBJECTS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

目的

开发一种工具,用于估计局限性前列腺癌男性患者因其他原因死亡的 10 年风险。

方法

我们从监测、流行病学和最终结果-医疗保险健康结果调查数据库中确定了 2425 名年龄<80 岁、新诊断为临床分期 T1-T3a 前列腺癌的患者,随访至 2013 年 2 月 28 日。我们开发了一种 Fine and Gray 竞争风险模型,用于考虑年龄、患者报告的合并症医疗状况、SF-36 健康调查的组成评分和项目、日常生活活动以及社会人口统计学特征的 10 年其他原因死亡率。将模型的区分度和校准度与社会保障生命表死亡率风险估计的预测值进行了比较。

结果

在中位随访 7.7 年期间,76 名男性死于前列腺癌特异性原因,465 名男性死于其他原因。10 年其他原因死亡率的最强预测因素包括诊断时年龄的增加、较高的近似 Charlson 合并症指数评分、患者报告的一般健康状况较差(一般或差与极好或好)、诊断时吸烟和婚姻状况(所有其他与已婚)(均 P<0.05)。模型的区分度优于社会保障生命表(分别为 0.70 和 0.59)。预测比社会保障生命表更准确,后者高估了我们人群的风险。

结论

我们提供了一种工具,用于在使用治疗前列腺癌的治疗前患者报告特征做出决策时,估计因其他原因死亡的 10 年风险。