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基于人群的电子健康记录数据评估衰弱评分预测老年人死亡率:病例对照研究。

Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study.

机构信息

Institute of Health and Society, Newcastle University, Newcastle upon Tyne.

Department of Public Health and Primary Care, University of Cambridge, Cambridge.

出版信息

Age Ageing. 2018 Jul 1;47(4):564-569. doi: 10.1093/ageing/afy022.

Abstract

BACKGROUND

recognising that a patient is nearing the end of life is essential, to enable professional carers to discuss prognosis and preferences for end of life care.

OBJECTIVE

investigate whether an electronic frailty index (eFI) generated from routinely collected data, can be used to predict mortality at an individual level.

DESIGN

historical prospective case control study.

SETTING

UK primary care electronic health records.

SUBJECTS

13,149 individuals age 75 and over who died between 01/01/2015 and 01/01/2016, 1:1 matched by age and sex to individuals with no record of death in the same time period.

METHODS

two subsamples were randomly selected to enable development and validation of the association between eFI 3 months prior to death and mortality. Receiver operator characteristic (ROC) analyses were used to examine diagnostic accuracy of eFI at 3 months prior to death.

RESULTS

an eFI > 0.19 predicted mortality in the development sample at 75% sensitivity and 69% area under received operating curve (AUC). In the validation dataset this cut point gave 76% sensitivity, 53% specificity.

CONCLUSIONS

the eFI measured at a single time point has low predictive value for individual risk of death, even 3 months prior to death. Although the eFI is a strong predictor or mortality at a population level, its use for individuals is far less clear.

摘要

背景

认识到患者生命即将结束至关重要,这使专业护理人员能够讨论预后和临终关怀偏好。

目的

研究从常规收集的数据中生成的电子虚弱指数(eFI)是否可用于预测个体水平的死亡率。

设计

历史前瞻性病例对照研究。

地点

英国初级保健电子健康记录。

受试者

年龄在 75 岁及以上的 13149 名个体,他们在 2015 年 1 月 1 日至 2016 年 1 月 1 日期间死亡,与同一时期没有死亡记录的个体按年龄和性别 1:1 匹配。

方法

随机选择两个样本,以确定 eFI 在死亡前 3 个月与死亡率之间的关联。使用接收者操作特征(ROC)分析来检查 eFI 在死亡前 3 个月的诊断准确性。

结果

在发展样本中,eFI>0.19 在 75%的敏感性和 69%的接收者操作曲线(AUC)下预测死亡率。在验证数据集,该切点的敏感性为 76%,特异性为 53%。

结论

即使在死亡前 3 个月,单点测量的 eFI 对个体死亡风险的预测价值也很低。尽管 eFI 是死亡率的一个强有力的预测指标,但它在个体中的应用还远不明确。

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