Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases

Journal：Neuroepidemiology

Publication type: Article

IF(IF): 4

PMID: 39965545 Link To Pubmed Full text Download

Stroke is the primary cause of disability and the second leading cause of death globally. Acute ischemic stroke (AIS) is the most common type of stroke, comprising approximately 87% of all stroke. It is estimated that 70% to 80% of AIS patients suffer from functional impairments, which hinder their ability to live independently and place a considerable strain on the healthcare system, the economy, and families. The impact of AIS is significantly heavier in developing countries, including China, compared to developed nations. The disability rate from AIS in China is 2.5 times higher than in European and American countries, and the mortality rate is five times higher. Therefore, to explore potential factors that may affect the prognosis of AIS and to improve the healthcare strategies for AIS patients is of clinical and public health relevance. Comorbidity refers to the occurrence of two or more diseases or unfavorable health conditions in the same person, and the presence of these additional conditions has a combined influence on the index disease and its prognosis. AIS is a chronic disease characterized by prolonged progression, complex development, and diverse subtypes. It has been reported that 94.2% of AIS patients commonly have more than one comorbidity, which will worsen their prognosis. With the aging population and improved survival rates, the prevalence of comorbidity in AIS patients is expected to continue rising. Despite its importance, few studies have thoroughly examined the associations between AIS-related comorbidities and the prognosis of AIS. Addressing this gap is essential for effective treatment of AIS and comprehensive management of AIS comorbid disorders, as well as for improving the functional outcomes of AIS patients. This study aims to comprehensively assess associations between a wide range of AIS-related comorbidities and functional outcome of patients with AIS, and further explore the potential comorbidity patterns related to the AIS prognosis.

Eighteen AIS-related comorbidities were considered in the analysis and the frequent pattern mining was employed to identify potential comorbidity patterns among AIS patients. Neurological functional outcome after one year was measured by the Modified Rankin Scale (mRS). We used logistic regression models to assess the association of comorbidities and their patterns with AIS prognosis. Additionally, association rule mining was used to explore the hidden comorbidity combinations and their relationship with functional outcomes based on the identified patterns.