ORIGINAL RESEARCH article
Front. Neurol.
Sec. Stroke
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1597361
Hidden risk patterns among acute ischemic stroke patients identified by latent class analysis
Provisionally accepted
- 1Guangzhou National Laboratory, Guangzhou, Guangdong, China
- 2Ningbo Medical Centre Lihuili Hospital, Ningbo, Zhejiang Province, China
- 3Ningbo First Hospital, Ningbo, Zhejiang Province, China
- 4First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
- 5Yiwu Rehabilitation Hospital, Yiwu, China
- 6Xiang'an Hospital, Xiamen University, Xiamen, Fujian Province, China
- 7Hangzhou Medical College, Hangzhou, Zhejiang Province, China
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Background: Few studies focus on the comprehensive influence of multiple risk factors on the follow-up outcomes of acute ischemic stroke (AIS) patients. To fill this gap, this study aims to identify different subgroups with specific clinical characteristics and risk patterns among patients with AIS and to provide individualized treatment plans accordingly. Method: We obtained clinical follow-up data from 448 AIS patients within 72 hours of admission. Subgroup patients were characterized by latent class analysis (LCA) using 5 risk factors of AIS. Cox proportional hazard regression analysis was used to explore the relationship between classified risk factor patterns and functional outcomes of patients with AIS at 3 months. Findings: We obtained two risk factor patterns as “Elderly with low lymphocytes,” and “Participants with low neutrophils and high lymphocytes”. Class 1 (n=214, 47.8%) had lower lymphocytes levels and was mainly elderly. Patients in Class 2 (N=234, 53.2%) had higher lymphocytes levels and lower neutrophils levels than those in Class 1. In addition, CRP levels were mostly low in both Classes 1 and 2. There was a significant difference in poor functional outcomes between the two patterns after adjusting various confounders (P<0.001). Compared with patients in Class 2, patients in Class 1 had a higher risk of adverse functional outcomes (adjusted Hazard Ratio, 3.21; 95% confidence interval:2.07 -- -4.98; P < 0.001). Interpretation: In our study, LCA was used to identify a 2-Class LCA model that was shown distinct clinical features and laboratory measurements among AIS patients. Our findings are beneficial for health management and therapy.
Keywords: Acute ischemic stroke, latent class analysis, subgroups, risk patterns, poor outcomes
Received: 25 Mar 2025; Accepted: 16 Jul 2025.
Copyright: © 2025 Hou, Gu, Sun, Zhang, Deng, Chen, Li and Cheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiangqing Hou, Guangzhou National Laboratory, Guangzhou, 510005, Guangdong, China
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