Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study.
Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study.
复制标题
DOI：
10.1016/s0140-6736(18)30668-8
复制
发表时间：
2018-05-05
期刊：
影响因子：
--
通讯作者：
Conroy S
中科院分区：
文献类型：
--
作者：
Gilbert T;Neuburger J;Kraindler J;Keeble E;Smith P;Ariti C;Arora S;Street A;Parker S;Roberts HC;Bardsley M;Conroy S
全球老年人越来越多地使用医疗保健。我们的目的是确定是否可以使用常规收集的数据来识别具有虚弱特征的老年人和有不良医疗结果风险的老年人。一个三步的方法被用来开发和验证医院虚弱风险评分从国际疾病和相关健康问题统计分类，第十次修订版（ICD-10）诊断代码。首先，我们进行了聚类分析，以确定一组住院的老年人（≥75岁），他们具有高资源使用和与虚弱相关的诊断。其次，我们根据ICD-10编码创建了一个医院脆弱风险评分，以表征这一群体。第三，在不同的队列中，我们测试了分数预测不良结果的效果，以及它是否能识别出与其他脆弱工具相似的群体。在发展队列（n= 22139）中，诊断为虚弱的老年人形成了一个独特的群体，并且有更高的非择期住院使用（2年内为33.6床日，而床日数第二高的群体为23.0床日）。 在国家验证队列（n=1 013 590）中，与风险评分最低的429 762（42.4%）例患者相比，医院虚弱风险评分最高的202 718（20.0%）例患者的30天死亡率（比值比1.71，95%CI 1.68 - 1.75）、住院时间长（6.03，5.92 - 6.10）和30天再入院（1.48，1.46 - 1.50）的比值增加。 这三个结果的个体间c统计量（即模型判别）分别为0.60、0.68和0.56。医院虚弱风险评分显示与二分Fried和罗克伍德量表（kappa评分分别为0.22，95% CI 0.15 - 0.30和0.30，0.22 - 0.38）有一定重叠，与罗克伍德虚弱指数（Pearson相关系数0.41，95% CI 0.38 - 0.47）有中度一致性。医院虚弱风险评分为医院和卫生系统提供了一种低成本、系统化的方法来筛查虚弱，并识别出一组不良结局风险更高的患者，对他们来说，虚弱协调的方法可能是有用的。国立卫生研究院。
Older people are increasing users of health care globally. We aimed to establish whether older people with characteristics of frailty and who are at risk of adverse health-care outcomes could be identified using routinely collected data. A three-step approach was used to develop and validate a Hospital Frailty Risk Score from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnostic codes. First, we carried out a cluster analysis to identify a group of older people (≥75 years) admitted to hospital who had high resource use and diagnoses associated with frailty. Second, we created a Hospital Frailty Risk Score based on ICD-10 codes that characterised this group. Third, in separate cohorts, we tested how well the score predicted adverse outcomes and whether it identified similar groups as other frailty tools. In the development cohort (n=22 139), older people with frailty diagnoses formed a distinct group and had higher non-elective hospital use (33·6 bed-days over 2 years compared with 23·0 bed-days for the group with the next highest number of bed-days). In the national validation cohort (n=1 013 590), compared with the 429 762 (42·4%) patients with the lowest risk scores, the 202 718 (20·0%) patients with the highest Hospital Frailty Risk Scores had increased odds of 30-day mortality (odds ratio 1·71, 95% CI 1·68–1·75), long hospital stay (6·03, 5·92–6·10), and 30-day readmission (1·48, 1·46–1·50). The c statistics (ie, model discrimination) between individuals for these three outcomes were 0·60, 0·68, and 0·56, respectively. The Hospital Frailty Risk Score showed fair overlap with dichotomised Fried and Rockwood scales (kappa scores 0·22, 95% CI 0·15–0·30 and 0·30, 0·22–0·38, respectively) and moderate agreement with the Rockwood Frailty Index (Pearson's correlation coefficient 0·41, 95% CI 0·38–0·47). The Hospital Frailty Risk Score provides hospitals and health systems with a low-cost, systematic way to screen for frailty and identify a group of patients who are at greater risk of adverse outcomes and for whom a frailty-attuned approach might be useful. National Institute for Health Research.