Suppr超能文献

预测出院后再入院和死亡:传统衰弱测量与基于电子健康记录的评分比较。

Predicting readmission and death after hospital discharge: a comparison of conventional frailty measurement with an electronic health record-based score.

机构信息

BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK.

University of Edinburgh Medical School, Edinburgh, UK.

出版信息

Age Ageing. 2021 Sep 11;50(5):1641-1648. doi: 10.1093/ageing/afab043.

Abstract

BACKGROUND

frailty measurement may identify patients at risk of decline after hospital discharge, but many measures require specialist review and/or additional testing.

OBJECTIVE

to compare validated frailty tools with routine electronic health record (EHR) data at hospital discharge, for associations with readmission or death.

DESIGN

observational cohort study.

SETTING

hospital ward.

SUBJECTS

consented cardiology inpatients ≥70 years old within 24 hours of discharge.

METHODS

patients underwent Fried, Short Physical Performance Battery (SPPB), PRISMA-7 and Clinical Frailty Scale (CFS) assessments. An EHR risk score was derived from the proportion of 31 possible frailty markers present. Electronic follow-up was completed for a primary outcome of 90-day readmission or death. Secondary outcomes were mortality and days alive at home ('home time') at 12 months.

RESULTS

in total, 186 patients were included (79 ± 6 years old, 64% males). The primary outcome occurred in 55 (30%) patients. Fried (hazard ratio [HR] 1.47 per standard deviation [SD] increase, 95% confidence interval [CI] 1.18-1.81, P < 0.001), CFS (HR 1.24 per SD increase, 95% CI 1.01-1.51, P = 0.04) and EHR risk scores (HR 1.35 per SD increase, 95% CI 1.02-1.78, P = 0.04) were independently associated with the primary outcome after adjustment for age, sex and co-morbidity, but the SPPB and PRISMA-7 were not. The EHR risk score was independently associated with mortality and home time at 12 months.

CONCLUSIONS

frailty measurement at hospital discharge identifies patients at risk of poorer outcomes. An EHR-based risk score appeared equivalent to validated frailty tools and may be automated to screen patients at scale, but this requires further validation.

摘要

背景

虚弱测量可以识别出院后有衰退风险的患者,但许多测量方法需要专家评估和/或额外的测试。

目的

比较在出院时使用经过验证的虚弱工具与常规电子健康记录 (EHR) 数据,以评估其与再入院或死亡的相关性。

设计

观察性队列研究。

地点

医院病房。

受试者

在出院后 24 小时内同意接受心脏病学治疗的 70 岁以上住院患者。

方法

患者接受了 Fried、Short 身体表现电池 (SPPB)、PRISMA-7 和临床虚弱量表 (CFS) 的评估。从 31 种可能的虚弱标志物中出现的比例得出 EHR 风险评分。通过电子方式进行了为期 90 天的再入院或死亡的主要结局的随访。次要结局为 12 个月时的死亡率和在家中存活的天数(“在家时间”)。

结果

共纳入 186 名患者(79±6 岁,64%为男性)。55 名(30%)患者发生了主要结局。Fried(每增加一个标准差的危险比[HR]1.47,95%置信区间[CI]1.18-1.81,P<0.001)、CFS(每增加一个标准差的 HR 1.24,95% CI 1.01-1.51,P=0.04)和 EHR 风险评分(每增加一个标准差的 HR 1.35,95% CI 1.02-1.78,P=0.04)在调整年龄、性别和合并症后与主要结局独立相关,但 SPPB 和 PRISMA-7 则不然。EHR 风险评分与 12 个月时的死亡率和在家时间独立相关。

结论

出院时的虚弱测量可以识别预后较差的患者。基于 EHR 的风险评分似乎与经过验证的虚弱工具相当,并且可以自动化筛选大量患者,但这需要进一步验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e528/8437069/3c6c04f55166/afab043f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验