ABSTRACT NUMBER: ESOC2026A877 CORE SYMPTOMS IN STROKE RECOVERY IDENTIFIED THROUGH SYMPTOM NETWORK ANALYSIS: A SCOPING REVIEW

Abstract Background and aims During stroke recovery, patients frequently experience multiple interacting symptoms that limit rehabilitation participation. Symptom network analysis provides a data-driven approach to identify “core symptoms” that may sustain the symptom system and represent high-impact intervention targets. Identifying core symptoms may guide symptom-targeted rehabilitation strategies. We aimed to map the evidence on how core symptoms are identified using symptom network methods in stroke populations and summarize core symptoms reported. Methods Following the Joanna Briggs Institute scoping review methodology, we searched nine databases (MEDLINE, PubMed, EMBASE, Web of Science, ScienceDirect, CBM, CNKI, VIP, and Wanfang) from inception to August 26, 2025. Studies applying symptom network analysis to adults with stroke and reporting core symptoms using centrality metrics were included. We charted methodological features and synthesized reported core symptoms and rehabilitation-related applications. Results Eighteen studies (n=195–2185) were included. Core symptom identification commonly relied on validated symptom scales, symptom preprocessing, Gaussian graphical models with EBICglasso, and centrality indices, particularly expected influence and strength. Across multidimensional networks, the most consistently reported core symptoms were impaired self-care, reduced mobility, pain, fatigue, decreased attention, worry, and low confidence in rehabilitation. In single-domain networks, sadness and loneliness were most frequently identified. Several studies incorporated external variables (e.g., outcomes and disease characteristics), enabling exploration of symptom–outcome pathways. Conclusions Core symptoms during stroke recovery span physical, psychological, and cognitive domains and may represent high-impact targets to improve rehabilitation engagement and functional recovery. Future research should standardize core symptom identification and test whether targeting core symptoms improves symptom burden and rehabilitation outcomes. Conflict of interest Yunting Wu: nothing to disclose. Weige Sun: nothing to disclose. Fei Wu: nothing to disclose. Ran Zhang: nothing to disclose. Tong Wang: nothing to disclose. Minghui Liu: nothing to disclose. Yuying Wang: nothing to disclose. Weixin Cai: nothing to disclose.

Authors

Institutions

Publication Details

Journal
European Stroke Journal
Published
2026-05-01
DOI
https://doi.org/10.1093/esj/aakag023.1435
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ABSTRACT NUMBER: ESOC2026A877 CORE SYMPTOMS IN STROKE RECOVERY IDENTIFIED THROUGH SYMPTOM NETWORK ANALYSIS: A SCOPING REVIEW

Minghui Liu, Ran Zhang, Tong Wang, Yuxing Wang et al.
European Stroke Journal
Stroke Rehabilitation and Recovery
article

ABSTRACT NUMBER: ESOC2026A877 CORE SYMPTOMS IN STROKE RECOVERY IDENTIFIED THROUGH SYMPTOM NETWORK ANALYSIS: A SCOPING REVIEW

Minghui Liu, Ran Zhang, Tong Wang, Yuxing Wang, Weige Sun, Fei Wu, Weixin Cai, Yunting Wu
article en

Abstract

Abstract Background and aims During stroke recovery, patients frequently experience multiple interacting symptoms that limit rehabilitation participation. Symptom network analysis provides a data-driven approach to identify “core symptoms” that may sustain the symptom system and represent high-impact intervention targets. Identifying core symptoms may guide symptom-targeted rehabilitation strategies. We aimed to map the evidence on how core symptoms are identified using symptom network methods in stroke populations and summarize core symptoms reported. Methods Following the Joanna Briggs Institute scoping review methodology, we searched nine databases (MEDLINE, PubMed, EMBASE, Web of Science, ScienceDirect, CBM, CNKI, VIP, and Wanfang) from inception to August 26, 2025. Studies applying symptom network analysis to adults with stroke and reporting core symptoms using centrality metrics were included. We charted methodological features and synthesized reported core symptoms and rehabilitation-related applications. Results Eighteen studies (n=195–2185) were included. Core symptom identification commonly relied on validated symptom scales, symptom preprocessing, Gaussian graphical models with EBICglasso, and centrality indices, particularly expected influence and strength. Across multidimensional networks, the most consistently reported core symptoms were impaired self-care, reduced mobility, pain, fatigue, decreased attention, worry, and low confidence in rehabilitation. In single-domain networks, sadness and loneliness were most frequently identified. Several studies incorporated external variables (e.g., outcomes and disease characteristics), enabling exploration of symptom–outcome pathways. Conclusions Core symptoms during stroke recovery span physical, psychological, and cognitive domains and may represent high-impact targets to improve rehabilitation engagement and functional recovery. Future research should standardize core symptom identification and test whether targeting core symptoms improves symptom burden and rehabilitation outcomes. Conflict of interest Yunting Wu: nothing to disclose. Weige Sun: nothing to disclose. Fei Wu: nothing to disclose. Ran Zhang: nothing to disclose. Tong Wang: nothing to disclose. Minghui Liu: nothing to disclose. Yuying Wang: nothing to disclose. Weixin Cai: nothing to disclose.

European Stroke JournalVol. 11(Supplement_1)
Beijing Tian Tan Hospital (CN), Hainan Provincial Hospital of Traditional Chinese Medicine (CN), Hainan 301 Hospital (CN)
Openalex Percentile: Top 12%
Stroke Rehabilitation and Recovery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.