Latent profile classification of cognitive function and its influencing factors in elderly patients with chronic pain after stroke
TL;DR
There is significant population heterogeneity in the cognitive function of elderly patients with chronic pain after stroke and medical staff should implement precise interventions for patients in different categories to delay cognitive decline and improve their quality of life.
Abstract
Objectives To identify the latent profile classification of cognitive function in elderly patients with chronic pain after stroke and explore the associated factors for patients in different categories. Methods Elderly patients with chronic pain after stroke hospitalized in the Department of Neurology of a hospital in Nanjing from August 2025 to January 2026 were selected as research subjects. Data were collected using a General Information Questionnaire, the Montreal Cognitive Assessment (MoCA), Hamilton Anxiety Scale (HAMA), Center for Epidemiological Studies Depression Scale (CES-D), Pain Catastrophizing Scale (PCS), and Elderly Social Participation Scale. Latent Profile Analysis (LPA) was used for patient classification, and multivariate logistic regression analysis was performed to identify predictive factors for different groups (P < 0.05). Results Patients’ cognitive function was classified into three latent profiles: High Cognitive Function-Low Abstraction Group, Moderate Cognitive Function-Low Orientation Group, and Low Cognitive Function Group. Logistic regression analysis showed that gender, pain type, pain intensity, anxiety, and depression were significant influencing factors of cognitive function across different categories (P < 0.05). Conclusion There is significant population heterogeneity in the cognitive function of elderly patients with chronic pain after stroke. Medical staff should implement precise interventions for patients in different categories to delay cognitive decline and improve their quality of life.