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

被引:2
|
作者
Garcia-Rudolph, Alejandro [1 ,2 ,3 ,8 ]
Sauri, Joan [1 ,2 ,3 ]
Cisek, Katryna [4 ]
Kelleher, John D. D. [4 ]
Madai, Vince Istvan [5 ,6 ,7 ]
Frey, Dietmar [5 ]
Opisso, Eloy [1 ,2 ,3 ]
Tormos, Josep Maria [2 ,3 ,5 ]
Bernabeu, Montserrat [1 ,2 ,3 ]
机构
[1] UAB, Inst Univ Neurorehabil Adscrit, Inst Guttmann, Dept Res & Innovat, Badalona, Spain
[2] Univ Autonoma Barcelona, Barcelona, Spain
[3] Fundacio Inst Invest Ciencies Salut Germans Trias, Barcelona, Spain
[4] Birmingham City Univ, Fac Comp Engn & Built Environm, Sch Comp & Digital Technol, Birmingham, England
[5] Technol Univ Dublin TU Dublin, Informat Commun & Entertainment Res Inst, Dublin, Ireland
[6] Charite, CLAIM Charite Lab Med, Berlin, Germany
[7] Charite Univ Med Berlin, Berlin Inst Hlth BIH, QUEST Ctr Transforming Biomed Res, Berlin, Germany
[8] Hosp Neurorehabil, Inst Guttmann, Dept Res & Innovat, Cami Can Ruti S-N, Barcelona 08916, Spain
基金
欧盟地平线“2020”;
关键词
Community integration; functional independence; trajectories; latent class modeling; growth mixture modeling; 1ST; 5; YEARS; STROKE; REINTEGRATION; QUESTIONNAIRE; IMPACT;
D O I
10.1080/10749357.2023.2188756
中图分类号
R49 [康复医学];
学科分类号
100215 ;
摘要
BackgroundCommunity 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.ObjectivesTo identify novel classes of patients having similar temporal patterns in CI and relate them to baseline features.MethodsRetrospective observational cohort study analyzing (n = 287) adult patients with stroke admitted to rehabilitation between 2003 and 2018, including baseline Functional Independence Measure (FIM) at discharge, follow-ups (m = 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.ResultsEach 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: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.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).ConclusionNovel clinical (e.g. hypertension) and demographic (e.g. education) variables characterized and predicted long-term CI trajectories.
引用
收藏
页码:714 / 726
页数:13
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