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目的:明确心房颤动患者认知功能障碍的危险因素，并构建风险预测模型，为改善患者认知功能提供数据支持。方法:采用便利抽样法对2020年1 —12月济宁医学院附属医院心内科收治的260例心房颤动患者进行评估。使用蒙特利尔认知功能评估量表（MoCA）对患者的认知功能进行评估。采用单因素分析筛选对认知功能障碍发生具有影响的自变量，将具有统计学意义的变量纳入多因素Logistic回归模型，根据具有统计学意义的变量的回归系数绘制列线图，构建心房颤动患者认知功能障碍的风险预测模型。结果:认知功能障碍患者209例，非认知功能障碍患者51例。单因素分析显示，非认知功能障碍的患者和认知功能障碍的患者在性别、年龄、吸烟史、饮酒史、文化程度、游离甲状腺素、血红蛋白、D-二聚体、BMI方面比较，差异有统计学意义（n χ2值为4.08 ~ 18.83，n t值为-6.04 ~ 2.94，n Z=-2.76，n P<0.05）。多因素Logistic回归分析显示，年龄（n OR=1.13）、文化程度（n OR值为0.01 ~ 0.05）、已戒烟（n OR=0.36）、饮酒史（n OR=0.35）、游离甲状腺素（n OR=1.14）差异有统计学意义（n P 0.8，该模型有较好的临床预测能力。n 结论:心房颤动患者认知功能障碍风险预测模型的构建能够对高危因素进行提前预防，进而实施干预，方便在临床推广使用。“,”Objective:To identify the risk factors of cognitive dysfunction in patients with atrial fibrillation and to establish a risk prediction model.Methods:The convenience sampling method was used to evaluate 260 patients with atrial fibrillation who were hospitalized in the Department of Cardiology of the Affiliated Hospital of Jining Medical College from January to December 2020. The cognitive function of the patients was evaluated with the Montreal Cognitive Function Assessment Scale (MoCA). Univariate analysis was used to screen the independent variables that had influence on the occurrence of cognitive dysfunction, and the statistically significant variables were included in the multivariate Logistic regression model. According to the regression coefficients of statistically significant variables, a line map was drawn to construct the risk prediction model of cognitive dysfunction in patients with atrial fibrillation.Results:There were 209 cases with cognitive impairment and 51 cases without cognitive impairment. Univariate analysis showed that sex, age, smoking history, drinking history, education level, free thyroxine, hemoglobin, D-dimer and BMI (n χ2 values were 4.08-18.83, n t values were -6.04-2.94, n Z=-2.76) were significantly different between the patients with or without cognitive dysfunction. The results of multivariate Logistic regression analysis showed that age (n OR values were 1.13), education level (n OR=0.01-0.05), quit smoking history (n OR=0.36), drinking history (n OR=0.35) and free thyroxine(n OR=1.14) had significantly statistical significance (n P0.8, this model had good clinical prediction ability.n Conclusions:The construction of cognitive dysfunction risk prediction model for patients with atrial fibrillation can prevent or intervene high risk factors in advance, facilitate clinical use, and provide data support for the improvement of cognitive function in patients with atrial fibrillation.