Study on risk factors for comorbidity of ischemic cardiovascular and cerebrovascular diseases based on principal component analysis
10.20039/j.cnki.1007-3949.2025.06.007
- VernacularTitle:基于主成分分析的缺血性心脑血管疾病共患危险因素研究
- Author:
Qian ZHAO
1
;
Hamulati XIEYIRE
;
Fen LIU
;
Xiaomei LI
;
Yining YANG
Author Information
1. 新疆医科大学第一附属医院心脏中心,新疆乌鲁木齐市 830054;新疆维吾尔自治区人民医院心内科,新疆乌鲁木齐市 830001
- Publication Type:Journal Article
- Keywords:
cardiovascular and cerebrovascular diseases;
comorbidity;
predictive model;
principal component analysis;
risk factors
- From:
Chinese Journal of Arteriosclerosis
2025;33(6):507-514
- CountryChina
- Language:Chinese
-
Abstract:
Aim To systematically analyze the risk factors for comorbid ischemic cardiovascular and cerebrovas-cular diseases using principal component analysis.It seeks to identify key risk factors influencing comorbidity and to con-struct a predictive model to serve as a screening tool for the prevention and management of comorbidities.Methods This retrospective study included patients diagnosed with coronary artery disease from December 1,2009 to June 30,2020,at the First Affiliated Hospital of Xinjiang Medical University,using data from the hospital's integrated coronary heart dis-ease prevention platform.Patients were divided into two groups:those with both cardiovascular and cerebrovascular disea-ses and those with only coronary artery disease based on inclusion and exclusion criteria.Clinical indicators during hospi-tal admission were collected.The sample was randomly divided into a modeling set and a validation set in a 7∶3 ratio.In the modeling set,principal component analysis was used to explore the distribution differences in risk factors between the comorbid group and the single-disease group.By analyzing factor load contributions,the most significant variables were i-dentified.Logistic regression was then used to construct a predictive model,and a nomogram was generated.The model's predictive ability and robustness were evaluated in the validation set.Results A total of 11 808 participants were included,with 2 781(23.6%)in the comorbid group and 9 027(76.4%)in the coronary heart disease-only group.Compared with the single-disease group,the comorbid group had a higher average age,a greater proportion of females,and higher systolic blood pressure levels at admission(P<0.001).Additionally,thelevelsof hemoglobin,platelet distri-bution width,low density lipoprotein,and blood sodium were significantly higher in the comorbid group(P<0.05).A total of 8 265 participants were randomly assigned to the modeling set.Principal component analysis identified seven key factors with factor load contributions greater than 5:sodium level,systolic blood pressure,diastolic blood pressure,age,hemoglobin concentration,platelet distribution width,and total bilirubin level.Using these factors,a nomogram was con-structed via Logistic regression.The nomogram's area under the receiver operating characteristic curve for predicting co-morbid ischemic cardiovascular and cerebrovascular diseases was 0.630(95%CI:0.600～0.768,P<0.001).A total of 3 543 participants were randomly assigned to the validation set.In the validation set,the receiver operating characteristic curve was 0.628.Conclusion Elevated sodium level,higher systolic and diastolic blood pressure at admission,older age,increased hemoglobin concentration,higher platelet distribution width,and lower total bilirubin level are risk factors for comorbid ischemic cardiovascular and cerebrovascular diseases.The nomogram constructed has clinical value for screening patients with such comorbidities.
