Objective This study aimed to construct a column line regression model for identifying cognitive dysfunction in ACS patients and validate its feasibility and efficacy.Methods A study was conducted on 485 patients diagnosed with ACS who were admitted to our cardiac intensive care unit between January 2019 and August 2020.Patients were categorized into two groups based on the presence or absence of cognitive dysfunction:Normal Group(n=284)and Dysfunction Group(n=201).The study aimed to analyze the risk factors leading to cognitive dysfunction in ACS patients,establish a column line regression model,and evaluate its performance.Results Among the 485 ACS patients,201(41.4％)experienced cognitive dysfunction.Logistic regression analysis revealed that age,systolic blood pressure(SBP),fasting blood glucose(FBG),and vascular lesion length were independent risk factors for cognitive dysfunction in ACS patients(P＜0.05).Model B(composed of age,SBP,FBG,and vascular lesion length)demonstrated the largest area under the Receiver Operating Characteristic(ROC)curve,identified as the optimal model for evaluating cognitive dysfunction in ACS.Model B exhibited high accuracy,discrimination,and clinical utility as validated by the decision curve analysis.Conclusion Age,SBP,FBG,and vascular lesion length are independent risk factors for cognitive dysfunction in ACS patients.The construction of a column line regression model based on these factors is effective for screening cognitive dysfunction in ACS.
关键词
急性冠脉综合征/认知功能障碍/危险因素/列线图回归模型
Key words
Acute Coronary Syndrome/Cognitive Dysfunction/Risk Factors/Column Line Regression Model
维普
万方数据