Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach

Abstract

Background Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD. Methods In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient’s parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods. Results In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77–0.90, p < 0.0001), 0.86 (95% CI 0.80–0.91, p < 0.0001) and 0.87 (95% CI 0.82–0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81–0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients. Conclusion A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters.

Relationship of vitamin D deficiency to cardiovascular disease and glycemic control in patients with type 2 diabetes mellitus: The Silesia Diabetes-Heart Project

“…Finding the difference in IMT only might seem to be an accidental finding and it certainly requires further research. However, in our previous study in which we exploited machine learning methods, we found that the plaque score of the right carotid artery, among others, can identify patients with metabolic-associated fatty liver disease and prevalent CVD [50].…”

Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach

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Development of a core outcome set for cardiovascular diabetology: a methodological framework

“…Developing a high-quality systematic review is of great value to the disease’s diagnosis, treatment, and prevention ( 16 ). Therefore, trials have been published in cardiovascular diabetology ( 2 , 10 – 12 ). However, because the results of cardiovascular diabetes are complicated, the reported outcomes are also different in trials ( 2 , 10 – 12 ).…”

“…Therefore, trials have been published in cardiovascular diabetology ( 2 , 10 – 12 ). However, because the results of cardiovascular diabetes are complicated, the reported outcomes are also different in trials ( 2 , 10 – 12 ). In some trials, cardiovascular events were subjectively reported by the patients, and insufficient and under-reported problems always existed ( 1 , 12 ).…”