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一种用于预测养老机构居民跌倒后个性化急诊科出院决策的临床预测模型。

A Clinical Prediction Model for Personalised Emergency Department Discharge Decisions for Residential Care Facility Residents Post-Fall.

作者信息

Guan Gigi, Michel Kadison, Corke Charlie, Ranmuthugala Geetha

机构信息

Department of Rural Health, Melbourne Medical School, The University of Melbourne, Shepparton, VIC 3630, Australia.

Critical Care Unit, Goulburn Valley Health, Shepparton, VIC 3630, Australia.

出版信息

J Pers Med. 2025 Jul 30;15(8):332. doi: 10.3390/jpm15080332.

Abstract

: Falls are the leading cause of Emergency Department (ED) presentations among residents from residential aged care facilities (RACFs). While most current studies focus on post-fall evaluations and fall prevention, limited research has been conducted on decision-making in post-fall management. : To develop and internally validate a model that can predict the likelihood of RACF residents being discharged from the ED after being presented for a fall. : The study sample was obtained from a previous study conducted in Shepparton, Victoria, Australia. Consecutive samples were selected from January 2023 to November 2023. Participants aged 65 and over were included in this study. : A total of 261 fall presentations were initially identified. One patient with Australasian Triage Scale category 1 was excluded to avoid overfitting, leaving 260 presentations for analysis. Two logistic regression models were developed using prehospital and ED variables. The ED predictor model variables included duration of ED stay, injury severity, and the presence of an advance care directive (ACD). It demonstrated excellent discrimination (AUROC = 0.83; 95% CI: 0.79-0.89) compared to the prehospital model (AUROC = 0.77, 95% CI: 0.72-0.83). A simplified four-variable Discharge Eligibility after Fall in Elderly Residents (DEFER) score was derived from the prehospital model. The score achieved an AUROC of 0.76 (95% CI: 0.71-0.82). At a cut-off score of ≥5, the DEFER score exhibited a sensitivity of 79.7%, a specificity of 60.3%, a diagnostic odds ratio of 5.96, and a positive predictive value of 85.0%. : The DEFER score is the first validated discharge prediction model for residents of RACFs who present to the ED after a fall. Importantly, the DEFER score advances personalised medicine in emergency care by integrating patient-specific factors, such as ACDs, to guide individualised discharge decisions for post-fall residents from RACFs.

摘要

跌倒是老年护理机构(RACF)居民前往急诊科(ED)就诊的主要原因。虽然目前大多数研究集中在跌倒后评估和预防跌倒方面,但关于跌倒后管理决策的研究有限。

为了开发并在内部验证一个模型,该模型能够预测RACF居民因跌倒就诊后从急诊科出院的可能性。

研究样本取自之前在澳大利亚维多利亚州谢珀顿进行的一项研究。从2023年1月至2023年11月选取连续样本。本研究纳入了65岁及以上的参与者。

最初共识别出261例跌倒就诊病例。排除1例澳大利亚分诊量表1级患者以避免过度拟合,剩余260例就诊病例用于分析。使用院前和急诊科变量建立了两个逻辑回归模型。急诊科预测模型变量包括急诊科停留时间、损伤严重程度以及预先护理计划(ACD)的存在情况。与院前模型(AUROC = 0.77,95% CI:0.72 - 0.83)相比,它表现出出色的区分能力(AUROC = 0.83;95% CI:0.79 - 0.89)。从院前模型中得出了一个简化的四变量老年居民跌倒后出院资格(DEFER)评分。该评分的AUROC为0.76(95% CI:0.71 - 0.82)。在截断分数≥5时,DEFER评分的敏感性为79.7%,特异性为60.3%,诊断比值比为5.96,阳性预测值为85.0%。

DEFER评分是首个针对跌倒后前往急诊科就诊的RACF居民的经过验证的出院预测模型。重要的是,DEFER评分通过整合患者特定因素(如ACD),推动了急诊护理中的个性化医疗,以指导RACF跌倒后居民的个性化出院决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4256/12387276/9071cef77b52/jpm-15-00332-g001.jpg

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