Development and validation of an electronic frailty index using routine primary care electronic health record data
Development and validation of an electronic frailty index using routine primary care electronic health record data
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DOI：
10.1093/ageing/afw039
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发表时间：
2016-05-01
期刊：
影响因子：
6.7
通讯作者：
Marshall, Tom
中科院分区：
文献类型：
--
作者：
Clegg, Andrew;Bates, Chris;Marshall, Tom
背景：虚弱是人口老龄化的一个特别有问题的表现。国际指南建议对虚弱进行常规识别，以提供循证治疗，但目前可用的工具需要额外的资源。目标：利用常规初级保健电子健康记录数据开发和验证电子虚弱指数（eFI）。研究设计和设置：回顾性队列研究。使用ResearchOne初级保健数据库的随机分割样本建立开发和内部验证队列。使用THIN数据库建立外部验证队列。参与者：年龄65-95岁，于2008年10月14日在ResearchOne或THIN诊所注册的患者。预测指标：以累积赤字脆弱性模型为理论框架构建eFI。eFI得分是根据个人赤字的存在与否占总可能赤字的比例来计算的。适应度、轻度、中度和重度虚弱的类别用总体四分位数来定义。结果：结果为1、3和5年死亡率、住院率和养老院入院率。统计分析：使用双变量和多变量Cox回归分析估计风险比（hr）。采用受试者工作特征（ROC）曲线评估辨别力。使用伪r -2估计值评估校准。结果：我们纳入了931,541例患者的数据。eFI包含了使用2171个CTV3代码构建的36个赤字。轻度虚弱的1年校正死亡率比为1.92 (95% CI 1.81-2.04)，中度虚弱为3.10 (95% CI 2.91-3.31)，重度虚弱为4.52 （95% CI 4.16-4.91）。住院的相应估计值分别为1.93 （95% CI 1.86-2.01）、3.04 （95% CI 2.90-3.19）和4.73 (95% CI 4.43-5.06)，养老院入院的相应估计值分别为1.89 （95% CI 1.63-2.15）、3.19 （95% CI 2.73-3.73）和4.76 (95% CI 3.92-5.77)，具有良好至中度判别，但校准估计值较低。结论：eFI使用常规数据来识别轻度、中度和重度虚弱的老年人，对死亡率、住院和养老院入住的结果具有强大的预测有效性。eFI的常规实施可以提供基于证据的干预措施，以改善这一弱势群体的结果。
Background: frailty is an especially problematic expression of population ageing. International guidelines recommend routine identification of frailty to provide evidence-based treatment, but currently available tools require additional resource.Objectives: to develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data.Study design and setting: retrospective cohort study. Development and internal validation cohorts were established using a randomly split sample of the ResearchOne primary care database. External validation cohort established using THIN database.Participants: patients aged 65-95, registered with a ResearchOne or THIN practice on 14 October 2008.Predictors: we constructed the eFI using the cumulative deficit frailty model as our theoretical framework. The eFI score is calculated by the presence or absence of individual deficits as a proportion of the total possible. Categories of fit, mild, moderate and severe frailty were defined using population quartiles.Outcomes: outcomes were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission.Statistical analysis: hazard ratios (HRs) were estimated using bivariate and multivariate Cox regression analyses. Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was assessed using pseudo-R-2 estimates.Results: we include data from a total of 931,541 patients. The eFI incorporates 36 deficits constructed using 2,171 CTV3 codes. One-year adjusted HR for mortality was 1.92 (95% CI 1.81-2.04) for mild frailty, 3.10 (95% CI 2.91-3.31) for moderate frailty and 4.52 (95% CI 4.16-4.91) for severe frailty. Corresponding estimates for hospitalisation were 1.93 (95% CI 1.86-2.01), 3.04 (95% CI 2.90-3.19) and 4.73 (95% CI 4.43-5.06) and for nursing home admission were 1.89 (95% CI 1.63-2.15), 3.19 (95% CI 2.73-3.73) and 4.76 (95% CI 3.92-5.77), with good to moderate discrimination but low calibration estimates.Conclusions: the eFI uses routine data to identify older people with mild, moderate and severe frailty, with robust predictive validity for outcomes of mortality, hospitalisation and nursing home admission. Routine implementation of the eFI could enable delivery of evidence-based interventions to improve outcomes for this vulnerable group.