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

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 (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.

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:

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).

Conclusion

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

KEYWORDS:

• Community integration
• functional independence
• trajectories
• latent class modeling
• growth mixture modeling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10749357.2023.2188756.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This research was partially funded by PRECISE4Q Personalized Medicine by Predictive Modelling in Stroke for better Quality of Life–European Union's Horizon 2020 research and innovation program under grant agreement No. 777107.