Clinical Focus ›› 2026, Vol. 41 ›› Issue (9): 824-830.doi: 10.3969/j.issn.1004-583X.2026.09.007

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Development and validation of a logistic regression model for factors influencing post-stroke emotional disorders after acute ischemic stroke

Zhang Shuaijie(), He Chunlei, Cao Aihua   

  1. Department of Neurology, the Second Affiliated Hospital of Luohe Medical College, Luohe 462000, China
  • Received:2026-04-03 Online:2026-09-20 Published:2026-09-17
  • Contact: Zhang Shuaijie E-mail:zshuaijie669@163.com

Abstract:

Objective To investigate the factors influencing post-stroke emotional disorder (PSED) in patients with acute ischemic stroke (AIS) and to construct a logistic regression model for identifying high-risk patients and guiding individualized clinical interventions. Methods A total of 200 patients with AIS treated at our hospital from November 2022 to November 2024 were enrolled, all of whom received intravenous thrombolysis. According to whether PSED occurred within 1 year after treatment (8 patients lost to follow-up), the patients were divided into a PSED group (102 cases) and a non-PSED group (90 cases). Clinical characteristics were compared between the two groups, and the factors influencing PSED after AIS were analyzed. Based on the multivariate analysis results, a logistic regression model was constructed and its predictive value for PSED after AIS was validated. Results Multivariate logistic regression analysis showed that higher National Institutes of Health Stroke Scale (NIHSS) score at admission, per capita monthly income <3000 yuan, poor ability to perform activities of daily living independently, sleep disorders, low social support, poor family care, and elevated cortisol, homocysteine (Hcy), peroxiredoxin 1 (PRDX1), and glial fibrillary acidic protein (GFAP) levels were independent risk factors for PSED after AIS (P<0.05). The logistic regression model yielded an area under the curve (AUC) of 0.904 for predicting PSED after AIS, with sensitivity and specificity of 82.35% and 82.22%, respectively. The Hosmer-Lemeshow test indicated good model fit (χ2=5.542, P=0.129). Bootstrap validation showed good discrimination, with a concordance index of 0.938. Decision curve analysis showed that the model provided good clinical net benefit when the risk threshold ranged from 0.10 to 0.90. Conclusion Higher NIHSS score at admission, per capita monthly income <3, 000 yuan, poor ability to perform activities of daily living independently, sleep disorders, low social support, poor family care, and elevated cortisol, Hcy, PRDX1, and GFAP levels are independent risk factors for PSED after AIS. The logistic regression model constructed on the basis of these factors has a certain predictive value for PSED after AIS. Clinically, targeted interventions should be implemented to reduce the risk of PSED.

Key words: stroke, mood disorders, influencing factors, prediction

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