A nomogram-based prediction model for motoric cognitive risk syndrome in patients with coronary artery disease: a cross-sectional study

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BACKGROUND: Coronary artery disease (CAD) is well known to be associated with dementia, motoric cognitive risk syndrome (MCR) has been identified as a predictor of dementia, with MCR and CAD potentially sharing common pathophysiological mechanisms. Identifying MCR in CAD patients is beneficial for the prevention of dementia. This study aims to investigate the incidence and identify the risk factors of MCR in CAD patients, and further establish a visual risk prediction model. METHODS: A cross-sectional study. From September 2023 to December 2023, we enrolled 413 CAD patients for this study. Patients were randomly grouped into a training cohort (80%) and a validation cohort (20%). The least absolute shrinkage and selection operator regression model and multivariate logistic regression analysis were used to select variables and develop a prediction model in the training cohort. In both the training and validation cohorts: ROC curve was used to evaluate the differentiation of the nomogram model; the calibration curve was used to evaluate the consistency of the model; the decision curve analysis was used to evaluate the efficiency of the nomogram. RESULTS: In this study, the prevalence of MCR was 13.8%. Four risk predictors, namely polypharmacy, handgrip strength, Gensini score, and neutrophil counts, were screened and used to develop a nomogram model. The ROC curve of the training set was 0.781 (95%CI: 0.71, 0.86). Similar ROC curve was achieved at validation set 0.780 (95%CI: 0.62, 0.94). The Hosmer-Lemeshow test in the training, and testing cohorts were p = 0.993, and p = 0.782, calibration curve analysis demonstrated that the model was well-calibrated. DCA exhibited this model with clinical utility. CONCLUSION: We developed a nomogram that could help clinicians identify high-risk groups of MCR in middle-aged and elderly CAD patients for early intervention.

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