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Purpose: To explore the risk factors of delirium in patients with stroke and develop a nomogram modelto predict the occurrence of delirium.

Methods: Convenience sampling was used to select 502 patients with stroke admitted to a tertiaryhospital with a neurology specialty in Tianjin from December 2023 to June 2024, who were categorizedinto the delirium group (n ¼ 141) and the non-delirium group (n ¼ 361) using the ICU Patient Ambiguityof Consciousness Assessment Scale. We explored the independent risk factors for the occurrence ofdelirium through univariate and multifactorial logistic regression analyses, established a risk predictionmodel, developed a nomogram, and validated the model both internally and externally.

Results: Multifactorial logistic regression analysis revealed that age (OR ¼ 1.04), abnormal vision(OR ¼ 2.74), post stroke infection (OR ¼ 3.49), National Institute of Health Stroke Scale score (NIHSS)(OR ¼ 4.18), whether restrained (OR ¼ 3.44) were independent risk factors for the development ofdelirium. The consistency index of the nomogram model for the occurrence of delirium in stroke patientswas 0.92, with a sensitivity of 83.0% and a specificity of 90.0%.

Conclusions: This study has developed and validated a predictive nomogram for identifying delirium inpatients with stroke. It can help healthcare professionals quickly identify high-risk patients for poststrokedelirium, providing a basis for further developing personalized prevention strategies and interventionmeasures for post-stroke delirium.

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