Analysis of risk factors and construction of risk prediction model of cognitive dysfunction in patients with atrial fibrillation / 中国实用护理杂志

ABSTRACT

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 ( χ2 values were 4.08-18.83, t values were -6.04-2.94, Z=-2.76) were significantly different between the patients with or without cognitive dysfunction. The results of multivariate Logistic regression analysis showed that age ( OR values were 1.13), education level ( OR=0.01-0.05), quit smoking history ( OR=0.36), drinking history ( OR=0.35) and free thyroxine( OR=1.14) had significantly statistical significance ( P<0.05). The area under ROC curve (AUC) = 0.878 and AUC>0.8, this model had good clinical prediction ability. 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.