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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and Validation of a Nomogram for Predicting Model for Delirium After Stroke
Chuyun Cui1, Guoqing Han1, Yandi Wang1
1Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
Purpose:
To explore the risk factors of delirium in patients with stroke and develop a nomogram model to predict the occurrence of delirium.
Methods:
Convenience sampling was used to select 502 patients with stroke admitted to a tertiary hospital with a neurology specialty in Tianjin from December 2023 to June 2024, who were categorized into the delirium group (n = 141) and the non-delirium group (n = 361) using the ICU Patient Ambiguity of Consciousness Assessment Scale. We explored the independent risk factors for the occurrence of delirium through univariate and multifactorial logistic regression analyses, established a risk prediction model, developed a nomogram, and validated the model both internally and externally.
Results:
Multifactorial logistic regression analysis revealed that age (OR = 1.04), abnormal vision (OR = 2.74), post stroke infection (OR = 3.49), National Institute of Health Stroke Scale score (NIHSS) (OR = 4.18), whether restrained (OR = 3.44) were independent risk factors for the development of delirium. The consistency index of the nomogram model for the occurrence of delirium in stroke patients was 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 in patients with stroke. It can help healthcare professionals quickly identify high-risk patients for post-stroke delirium, providing a basis for further developing personalized prevention strategies and intervention measures for post-stroke delirium.
