Clegg Andrew, Bates Chris, Young John, Ryan Ronan, Nichols Linda, Ann Teale Elizabeth, Mohammed Mohammed A, Parry John, Marshall Tom
Academic Unit of Elderly Care and Rehabilitation, University of Leeds, Bradford, West Yorkshire, United Kingdom of Great Britain and Northern Ireland
ResearchOne, TPP, Leeds, West Yorkshire, United Kingdom of Great Britain and Northern Ireland.
Age Ageing. 2016 May;45(3):353-60. doi: 10.1093/ageing/afw039. Epub 2016 Mar 3.
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.
to develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data.
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.
patients aged 65-95, registered with a ResearchOne or THIN practice on 14 October 2008.
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 were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission.
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.
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.
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.
衰弱是人口老龄化中一个特别突出的问题。国际指南建议对衰弱进行常规识别，以提供循证治疗，但目前可用的工具需要额外资源。
利用常规可得的基层医疗电子健康记录数据，开发并验证电子衰弱指数（eFI）。
回顾性队列研究。使用ResearchOne基层医疗数据库的随机拆分样本建立开发队列和内部验证队列。使用THIN数据库建立外部验证队列。
2008年10月14日在ResearchOne或THIN机构注册的65 - 95岁患者。
我们以累积缺陷衰弱模型作为理论框架构建eFI。eFI分数通过个体缺陷的存在与否占总可能数的比例来计算。使用人群四分位数定义了健康、轻度、中度和重度衰弱类别。
结局为1年、3年和5年死亡率、住院率和养老院入住率。
使用双变量和多变量Cox回归分析估计风险比（HRs）。使用受试者工作特征（ROC）曲线评估辨别力。使用伪R²估计评估校准。
我们纳入了总共931,541名患者的数据。eFI纳入了使用2,171个CTV3编码构建的36项缺陷。轻度衰弱的1年调整后死亡率HR为1.92（95%CI 1.81 - 2.04），中度衰弱为3.10（95%CI 2.91 - 3.31），重度衰弱为4.52（95%CI 4.16 - 4.91）。住院率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），养老院入住率分别为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），辨别力良好至中等，但校准估计值较低。
eFI利用常规数据识别轻度、中度和重度衰弱的老年人，对死亡率、住院率和养老院入住率结局具有较强的预测效度。eFI的常规实施可使提供循证干预措施成为可能，以改善这一弱势群体的结局。
