开发和验证一种风险评分模型，以预测 ICU 幸存者的非计划性再入院：一项数据链接研究。

Development and validation of a risk score to predict unplanned hospital readmissions in ICU survivors: A data linkage study.

机构信息

Faculty of Health, University of Technology Sydney, Sydney, NSW, Australia; Royal North Shore Hospital, Northern Sydney Local Health District, Sydney, NSW, Australia.

Sydney School of Public Health, The University of Sydney, Sydney, NSW, Australia; Menzies Centre for Health Policy and Economics, The University of Sydney, Sydney, NSW, Australia.

出版信息

Aust Crit Care. 2024 May;37(3):383-390. doi: 10.1016/j.aucc.2023.05.002. Epub 2023 Jun 18.

DOI:10.1016/j.aucc.2023.05.002

PMID:37339922

Abstract

BACKGROUND

Intensive Care Unit (ICU) follow-up clinics are growing in popularity internationally; however, there is limited evidence as to which patients would benefit most from a referral to this service.

OBJECTIVES

The objective of this study was to develop and validate a model to predict which ICU survivors are most likely to experience an unplanned hospital readmission or death in the year after hospital discharge and derive a risk score capable of identifying high-risk patients who may benefit from referral to follow-up services.

METHODS

A multicentre, retrospective observational cohort study using linked administrative data from eight ICUs was conducted in the state of New South Wales, Australia. A logistic regression model was developed for the composite outcome of death or unplanned readmission in the 12 months after discharge from the index hospitalisation.

RESULTS

12,862 ICU survivors were included in the study, of which 5940 (46.2%) patients experienced unplanned readmission or death. Strong predictors of readmission or death included the presence of a pre-existing mental health disorder (odds ratio [OR]: 1.52, 95% confidence interval [CI]: 1.40-1.65), severity of critical illness (OR: 1.57, 95% CI: 1.39-1.76), and two or more physical comorbidities (OR: 2.39, 95% CI: 2.14-2.68). The prediction model demonstrated reasonable discrimination (area under the receiver operating characteristic curve: 0.68, 95% CI: 0.67-0.69) and overall performance (scaled Brier score: 0.10). The risk score was capable of stratifying patients into three distinct risk groups-high (64.05% readmitted or died), medium (45.77% readmitted or died), and low (29.30% readmitted or died).

CONCLUSIONS

Unplanned readmission or death is common amongst survivors of critical illness. The risk score presented here allows patients to be stratified by risk level, enabling targeted referral to preventative follow-up services.

摘要

背景

重症监护病房（ICU）随访诊所在国际上越来越受欢迎；然而，关于哪些患者最适合转介到该服务，证据有限。

目的

本研究旨在开发和验证一种模型，以预测哪些 ICU 幸存者在出院后一年内最有可能经历非计划性住院再入院或死亡，并得出一个能够识别高风险患者的风险评分，这些患者可能受益于转介至随访服务。

方法

这项多中心、回顾性观察队列研究使用来自澳大利亚新南威尔士州 8 个 ICU 的链接行政数据进行。为出院后 12 个月内死亡或非计划性再入院的复合结局开发了一个逻辑回归模型。

结果

共纳入 12862 例 ICU 幸存者，其中 5940 例（46.2%）患者经历了非计划性再入院或死亡。再入院或死亡的强烈预测因素包括存在预先存在的心理健康障碍（优势比[OR]：1.52，95%置信区间[CI]：1.40-1.65）、危重疾病的严重程度（OR：1.57，95%CI：1.39-1.76）和两种或更多的身体合并症（OR：2.39，95%CI：2.14-2.68）。该预测模型表现出合理的区分度（受试者工作特征曲线下面积：0.68，95%CI：0.67-0.69）和整体性能（缩放 Brier 评分：0.10）。风险评分能够将患者分为三个不同的风险组-高（64.05%再入院或死亡）、中（45.77%再入院或死亡）和低（29.30%再入院或死亡）。