Department of Biostatistics and Data Science, Division of Public Health Sciences, Winston-Salem, North Carolina.
Center for Health Care Innovation, Winston-Salem, North Carolina.
J Gerontol A Biol Sci Med Sci. 2019 Oct 4;74(11):1771-1777. doi: 10.1093/gerona/glz017.
The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States.
We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls.
The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI.
Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.
虚弱的累积缺陷模型已被用于开发电子健康记录 (EHR) 虚弱指数 (eFI),该指数已被纳入英国虚弱管理指南。然而,在美国,基于 EHR 的方法的应用有限。
我们使用 EHR 中的就诊、诊断代码、实验室、药物和 Medicare 年度健康访视 (AWV) 数据,为我们的 Medicare 负责医疗组织 (ACO,N = 12798) 的患者构建了一个适应性 eFI。我们研究了 eFI 与死亡率、医疗保健利用和伤害性跌倒的关联。
整个队列中,女性占 55.7%,白人占 85.7%,平均年龄为 74.9(SD = 7.3)岁。在过去的 2 年中,有 32.1%的人有 AWV 数据。eFI 可用于 9013 名(70.4%)ACO 患者。其中,46.5%被归类为虚弱前期(0.10 < eFI ≤ 0.21),40.1%为虚弱(eFI > 0.21)。考虑到年龄、合并症和既往医疗保健利用情况,eFI 独立预测全因死亡率、住院、急诊就诊和伤害性跌倒(均<0.001)。从 AWV 中至少捕获一个功能缺陷与住院和伤害性跌倒的风险增加独立相关,控制了 eFI 的其他成分。
在管理式医疗人群中,从 EHR 构建 eFI 是可行的,可以帮助识别脆弱的老年人。未来需要将 eFI 与基于索赔的方法相结合,并检验其是否可用于有效针对虚弱患者的健康需求定制干预措施。