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长期的社区融合轨迹:使用住院康复变量进行识别、描述和预测。

Long-term trajectories of community integration: identification, characterization, and prediction using inpatient rehabilitation variables.

机构信息

Department of Research and Innovation, Institut Guttmann, Institut Universitari de Neurorehabilitaciò adscrit a la UAB, Badalona, Spain.

Universitat Autònoma de Barcelona, Barcelona, Spain.

出版信息

Top Stroke Rehabil. 2023 Oct;30(7):714-726. doi: 10.1080/10749357.2023.2188756. Epub 2023 Mar 19.

Abstract

BACKGROUND

Community integration (CI) is often regarded as the foundation of rehabilitation endeavors after stroke; nevertheless, few studies have investigated the relationship between inpatient rehabilitation (clinical and demographic) variables and long-term CI.

OBJECTIVES

To identify novel classes of patients having similar temporal patterns in CI and relate them to baseline features.

METHODS

Retrospective observational cohort study analyzing ( = 287) adult patients with stroke admitted to rehabilitation between 2003 and 2018, including baseline Functional Independence Measure (FIM) at discharge, follow-ups ( = 1264) of Community Integration Questionnaire (CIQ) between 2006 and 2022. Growth mixture models (GMMs) were fitted to identify CI trajectories, and baseline predictors were identified using multivariate logistic regression (reporting AUC) with 10-fold cross validation.

RESULTS

Each patient was assessed at 2.7 (2.2-3.7), 4.4 (3.7-5.6), and 6.2 (5.4-7.4) years after injury, 66% had a fourth assessment at 7.9 (6.8-8.9) years. GMM identified three classes of trajectories.

UNLABELLED

Lowest CI (n=105, 36.6%): The lowest mean total CIQ; highest proportion of dysphagia (47.6%) and aphasia (46.7%), oldest at injury, largest length of stay (LOS), largest time to admission, and lowest FIM.

UNLABELLED

Highest CI (n=63, 21.9%): The highest mean total CIQ, youngest, shortest LOS, highest education (27% university) highest FIM, and Intermediate CI (n=119, 41.5%): Intermediate mean total CIQ and FIM scores. Age at injury OR: 0.89 (0.85-0.93), FIM OR: 1.04 (1.02-1.07), hypertension OR: 2.86 (1.25-6.87), LOS OR: 0.98 (0.97-0.99), and high education OR: 3.05 (1.22-7.65) predicted highest CI, and AUC was 0.84 (0.76-0.93).

CONCLUSION

Novel clinical (e.g. hypertension) and demographic (e.g. education) variables characterized and predicted long-term CI trajectories.

摘要

背景

社区融合(CI)通常被视为中风后康复努力的基础;然而,很少有研究调查住院康复(临床和人口统计学)变量与长期 CI 之间的关系。

目的

确定具有相似 CI 时间模式的新型患者群体,并将其与基线特征相关联。

方法

对 2003 年至 2018 年间接受康复治疗的 287 名成年中风患者进行回顾性观察队列研究,包括出院时的功能性独立测量(FIM)基线,以及 2006 年至 2022 年期间社区融合问卷(CIQ)的 1264 次随访。使用具有 10 倍交叉验证的多变量逻辑回归(报告 AUC)确定基线预测因子,使用增长混合模型(GMM)来识别 CI 轨迹。

结果

每位患者在受伤后 2.7(2.2-3.7)、4.4(3.7-5.6)和 6.2(5.4-7.4)年进行评估,66%的患者在 7.9(6.8-8.9)年进行第四次评估。GMM 确定了三种轨迹类别。

未标注

最低 CI(n=105,36.6%):最低的总 CIQ 平均值;最高比例的吞咽困难(47.6%)和失语症(46.7%),受伤时年龄最大,住院时间最长(LOS),入院时间最长,FIM 最低。

未标注

最高 CI(n=63,21.9%):最高的总 CIQ 平均值,最年轻,最短的 LOS,最高的教育程度(27%的大学),最高的 FIM,以及中等 CI(n=119,41.5%):中等的总 CIQ 和 FIM 评分。受伤时年龄的 OR:0.89(0.85-0.93),FIM 的 OR:1.04(1.02-1.07),高血压的 OR:2.86(1.25-6.87),LOS 的 OR:0.98(0.97-0.99),以及高等教育的 OR:3.05(1.22-7.65),预测最高 CI,AUC 为 0.84(0.76-0.93)。

结论

新的临床(如高血压)和人口统计学(如教育)变量可对长期 CI 轨迹进行特征描述和预测。

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