Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Department of General Internal Medicine, University of California, San Francisco, CA, USA.
J Gen Intern Med. 2023 Oct;38(13):2860-2869. doi: 10.1007/s11606-023-08219-y. Epub 2023 May 30.
Estimated life expectancy for older patients with diabetes informs decisions about treatment goals, cancer screening, long-term and advanced care, and inclusion in clinical trials. Easily implementable, evidence-based, diabetes-specific approaches for identifying patients with limited life expectancy are needed.
Develop and validate an electronic health record (EHR)-based tool to identify older adults with diabetes who have limited life expectancy.
Predictive modeling based on survival analysis using Cox-Gompertz models in a retrospective cohort.
Adults with diabetes aged ≥ 65 years from Kaiser Permanente Northern California: a 2015 cohort (N = 121,396) with follow-up through 12/31/2019, randomly split into training (N = 97,085) and test (N = 24,311) sets. Validation was conducted in the test set and two temporally distinct cohorts: a 2010 cohort (n = 89,563; 10-year follow-up through 2019) and a 2019 cohort (n = 152,357; 2-year follow-up through 2020).
Demographics, diagnoses, utilization and procedures, medications, behaviors and vital signs; mortality.
In the training set (mean age 75 years; 49% women; 48% racial and ethnic minorities), 23% died during 5 years follow-up. A mortality prediction model was developed using 94 candidate variables, distilled into a life expectancy model with 11 input variables, and transformed into a risk-scoring tool, the Life Expectancy Estimator for Older Adults with Diabetes (LEAD). LEAD discriminated well in the test set (C-statistic = 0.78), 2010 cohort (C-statistic = 0.74), and 2019 cohort (C-statistic = 0.81); comparisons of observed and predicted survival curves indicated good calibration.
LEAD estimates life expectancy in older adults with diabetes based on only 11 patient characteristics widely available in most EHRs and claims data. LEAD is simple and has potential application for shared decision-making, clinical trial inclusion, and resource allocation.
对于老年糖尿病患者，预期寿命可用于指导治疗目标、癌症筛查、长期和晚期护理以及临床试验的纳入决策。目前需要一种简单易行、基于证据且专门针对糖尿病的方法，用于识别预期寿命有限的患者。
开发和验证一种基于电子健康记录（EHR）的工具，以识别预期寿命有限的老年糖尿病患者。
使用生存分析中的 Cox-Gompertz 模型进行预测建模，该模型基于回顾性队列。
加利福尼亚州 Kaiser Permanente 北部的年龄≥65 岁的糖尿病成年人：2015 年队列（N=121396），随访至 2019 年 12 月 31 日，随机分为训练集（N=97085）和测试集（N=24311）。在测试集中以及两个时间上不同的队列中进行了验证：2010 年队列（n=89563；通过 2019 年随访 10 年）和 2019 年队列（n=152357；通过 2020 年随访 2 年）。
人口统计学特征、诊断、利用和程序、药物、行为和生命体征；死亡率。
在训练集中（平均年龄 75 岁；49%为女性；48%为少数民族），5 年随访期间有 23%的患者死亡。使用 94 个候选变量开发了一个死亡率预测模型，经过提炼，该模型转化为一个具有 11 个输入变量的预期寿命模型，并转化为风险评分工具——老年人糖尿病预期寿命估算器（LEAD）。LEAD 在测试集中表现良好（C 统计量=0.78），在 2010 年队列（C 统计量=0.74）和 2019 年队列（C 统计量=0.81）中表现良好；观察到的和预测的生存曲线之间的比较表明校准良好。
LEAD 基于大多数 EHR 和理赔数据中广泛可用的 11 个患者特征来估算老年糖尿病患者的预期寿命。LEAD 简单易用，具有用于共同决策、临床试验纳入和资源分配的潜在应用。
