Gastens Viktoria, Chiolero Arnaud, Feller Martin, Bauer Douglas C, Rodondi Nicolas, Del Giovane Cinzia
Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland.
Graduate School for Health Sciences, University of Bern, Bern, Switzerland.
Diagn Progn Res. 2025 Mar 4;9(1):5. doi: 10.1186/s41512-025-00185-9.
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.
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.
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.
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.
ClinicalTrials.gov NCT02986425. First submitted 21/10/2016. First posted 08/12/2016.
随着人口老龄化，需要复杂护理的患有多种慢性病的老年患者数量不断增加。尽管临床指南建议根据预期寿命进行个性化护理，但目前尚无工具来估计患有多种疾病的患者的预期寿命。因此，我们的目标是开发并在内部验证一种专门针对患有多种疾病的老年成年人的预期寿命估计器。
我们分析了瑞士伯尔尼的OPERAM（优化治疗以预防患有多种疾病的老年人可避免的住院）研究中的数据。纳入年龄在70岁及以上、患有多种疾病（3种或更多慢性疾病）且使用多种药物（使用5种或更多药物超过30天）的参与者。在3年的随访期间评估全因死亡率。我们构建了一个3年死亡率预测指数，并将该指数转化为预期寿命估计器。死亡率风险候选预测因素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、养老院居住情况）和住院情况。我们使用自举技术在内部验证并校正了模型的乐观偏差。我们使用Gompertz生存函数将死亡率预测指数转化为预期寿命估计器。
805名参与者纳入分析。在3年的随访期间，292名参与者（36%）死亡。年龄、转移性癌症、药物数量、较低的体重指数、体重减轻、住院次数和较低的Barthel指数（功能障碍）被选为最终多变量模型中的预测因素。我们的模型显示出中等区分度，乐观校正后的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3.5年）。分为三个预期寿命组在Kaplan-Meier曲线中视觉上有良好的区分。我们还开发了一个网络应用程序来计算个人的预期寿命估计值。
基于内部验证的3年死亡率风险指数，开发了一种针对患有多种疾病的老年成年人的预期寿命估计器。在将该评分应用于实践之前，需要在各种患有多种疾病的患者群体中进行进一步验证。
ClinicalTrials.gov NCT02986425。首次提交于2016年10月21日。首次发布于2016年12月8日。
