多病老年人预期寿命估算器的开发和内部验证。

Viktoria Gastens, Arnaud Chiolero, Martin Feller, Douglas C Bauer, Nicolas Rodondi, Cinzia Del Giovane
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引用次数: 0

摘要

背景:随着人口老龄化,需要复杂护理的多种慢性疾病老年患者数量增加。尽管临床指南建议根据预期寿命进行个性化护理,但没有工具来估计多病患者的预期寿命。因此,我们的目标是开发并内部验证一个专门针对老年多病成年人的预期寿命估算器。方法:我们分析了来自瑞士伯尔尼的OPERAM(优化治疗以防止多病老年人可避免住院)研究的数据。参与者年龄在70岁或以上,患有多种疾病(3种或3种以上的慢性疾病)和多种药物(使用5种或5种以上的药物,持续30天)。在3年随访期间评估全因死亡率。我们建立了一个3年死亡率预测指数,并将该指数转化为预期寿命估计值。死亡风险候选预测因子包括人口统计学变量(年龄、性别)、临床特征(转移性癌症、药物数量、体重指数、体重减轻)、吸烟、功能状态变量(barthel指数、跌倒、养老院居住)和住院。我们内部验证和乐观修正模型使用自举技术。我们使用Gompertz生存函数将死亡率预后指数转换为预期寿命估计值。结果:850名参与者被纳入分析。在3年的随访中,292名参与者(36%)死亡。年龄、转移性癌症、药物数量、较低的身体质量指数、体重减轻、住院次数和较低的barthel指数(功能损害)被选为最终多变量模型的预测因子。我们的模型显示出适度的歧视,乐观校正的C统计量为0.70。乐观校正后的校准斜率为0.96。我们样本中gompertz预测的平均预期寿命为5.4年(标准差为3.5年)。将预期寿命分为三个组,在Kaplan-Meier曲线中有很好的视觉分离。我们还开发了一个计算个人预期寿命的web应用程序。结论:建立了一种基于内部验证的3年死亡风险指数的多病老年人预期寿命估计器。在实施之前,需要在不同人群的多病患者中进一步验证该评分。试验注册:ClinicalTrials.gov NCT02986425。首次提交于2016年10月21日。首次发布于2016年8月12日。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and internal validation of a new life expectancy estimator for multimorbid older adults.

Background: As populations are aging, the number of older patients with multiple chronic diseases demanding complex care increases. Although clinical guidelines recommend care to be personalized accounting for life expectancy, there are no tools to estimate life expectancy among multimorbid patients. Our objective was therefore to develop and internally validate a life expectancy estimator specifically for older multimorbid adults.

Methods: We analyzed data from the OPERAM (OPtimising thERapy to prevent avoidable hospital admissions in multimorbid older people) study in Bern, Switzerland. Participants aged 70 years old or more with multimorbidity (3 or more chronic medical conditions) and polypharmacy (use of 5 drugs or more for > 30 days) were included. All-cause mortality was assessed during 3 years of follow-up. We built a 3-year mortality prognostic index and transformed this index into a life expectancy estimator. Mortality risk candidate predictors included demographic variables (age, sex), clinical characteristics (metastatic cancer, number of drugs, body mass index, weight loss), smoking, functional status variables (Barthel-Index, falls, nursing home residence), and hospitalization. We internally validated and optimism corrected the model using bootstrapping techniques. We transformed the mortality prognostic index into a life expectancy estimator using the Gompertz survival function.

Results: Eight hundred five participants were included in the analysis. During 3 years of follow-up, 292 participants (36%) died. Age, metastatic cancer, number of drugs, lower body mass index, weight loss, number of hospitalizations, and lower Barthel-Index (functional impairment) were selected as predictors in the final multivariable model. Our model showed moderate discrimination with an optimism-corrected C statistic of 0.70. The optimism-corrected calibration slope was 0.96. The Gompertz-predicted mean life expectancy in our sample was 5.4 years (standard deviation 3.5 years). Categorization into three life expectancy groups led to visually good separation in Kaplan-Meier curves. We also developed a web application that calculates an individual's life expectancy estimation.

Conclusion: A life expectancy estimator for multimorbid older adults based on an internally validated 3-year mortality risk index was developed. Further validation of the score among various populations of multimorbid patients is needed before its implementation into practice.

Trial registration: ClinicalTrials.gov NCT02986425. First submitted 21/10/2016. First posted 08/12/2016.

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