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A novel dual-strategy combining a predictive risk nomogram with Montessori-based sensory education offers a promising framework for identifying and mitigating post-stroke cognitive impairment.
Post-stroke cognitive impairment (PSCI) remains a significant challenge for survivors, frequently impeding long-term recovery and quality of life, which necessitates reliable methods for early prediction and behavioral management. This study aimed to develop a predictive risk nomogram and pilot a sensory-based intervention strategy to effectively mitigate cognitive decline after cerebral infarction.
The research was carried out in two distinct phases:
Efficacy was determined by assessing changes in SLUMS, Activity of Daily Living (ADL), and Hamilton Depression Rating Scale (HAMD) scores. The predictive model demonstrated high accuracy, identifying age, ADL performance, stroke severity (via National Institutes of Health Stroke Scale [NIHSS]), and HAMD-17 scores as independent risk factors for PSCI (AUC=0.87).
The nomogram exhibited strong calibration and offered significant clinical net benefit across a wide probability range. Furthermore, the pilot intervention phase yielded positive outcomes: the group receiving Montessori-based sensory education showed statistically significant improvements in both cognitive function and daily living activities when compared to the control cohort (P = .002 and .004, respectively).
The study successfully introduced a preliminary nomogram model that leveraged patient-specific variables to forecast cognitive risk, offering clinicians a practical tool for early patient stratification. Concurrently, the Montessori-based sensory approach showed immediate promise in enhancing cognitive and functional outcomes in a clinical setting.
Psychiatry and Clinical Psychopharmacology
Clinical Evaluation and Early Intervention Strategies of Post-Stroke Cognitive Impairment
Xiaoling Zhou et al.
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