Subphenotypic classification of COVID-19 survivors and response to telerehabilitation: a latent class analysis
Year: 2025
Type: article
Abstract: OBJECTIVE: Investigating the role of telerehabilitation in aiding recovery and societal reintegration for COVID-19 survivors, this study aims to identify distinct subphenotypes among survivors and assess their responsiveness to telerehabilitation. DESIGN: A secondary analysis of a multicentre, parallel-group randomized controlled trial from April 2020 through to follow-up in 2021. SUBJECTS/PATIENTS: The study included 377 COVID-19 survivors (47.1% male), with a mean age of 56.4 years. METHODS: Data from the Telerehabilitation Programme for COVID-19 (TERECO) were analysed using Latent Class Analysis to identify subphenotypes based on baseline characteristics. Clinical outcomes were compared between subphenotypes and treatment groups. RESULTS: Latent Class Analysis identified 2 phenotypes: Phenotype 1 (52.9%) characterized by impaired lung function and Phenotype 2 (47.1%) with better lung function. Among those receiving corticosteroids, only Phenotype 1 showed significant benefits from the TERECO intervention. Discrimination accuracy using forced expiratory volume in 1 s (FEV1) and peak expiratory flow was high (AUC = 0.936). CONCLUSION: Two distinct phenotypes were identified in COVID-19 survivors, suggesting potential improvements in clinical trial design and personalized treatment strategies based on initial pulmonary function. This insight can guide more targeted rehabilitation approaches, enhancing recovery outcomes for specific survivor groups.
Authors: Yide Wang, Qianqian Xue, Zheng Li, Fengsen Li
Institution: Xinjiang Medical University
Language: English
Subfield: Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,887
OpenAlex ID: W4408882217