Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease: a machine learning approach
| Autor: | Karolina Drożdż, Katarzyna Nabrdalik, Hanna Kwiendacz, Mirela Hendel, Anna Olejarz, Andrzej Tomasik, Wojciech Bartman, Jakub Nalepa, Janusz Gumprecht, Gregory Y. H. Lip |
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| Rok vydání: | 2022 |
| Předmět: |
Male
Non-alcoholic Fatty Liver Disease/complications Endocrinology Diabetes and Metabolism Liver Diseases Liver Diseases/complications Middle Aged Cardiovascular disease Machine Learning Cardiovascular Diseases Risk Factors Heart Disease Risk Factors Non-alcoholic Fatty Liver Disease Metabolic-associated fatty liver disease Humans Female Cardiology and Cardiovascular Medicine Cardiovascular Diseases/diagnosis |
| Zdroj: | Drożdż, K, Nabrdalik, K, Kwiendacz, H, Hendel, M, Olejarz, A, Tomasik, A, Bartman, W, Nalepa, J, Gumprecht, J & Lip, G Y H 2022, ' Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease : a machine learning approach ', Cardiovascular Diabetology, vol. 21, no. 1, 240 . https://doi.org/10.1186/s12933-022-01672-9 |
| ISSN: | 1475-2840 |
| DOI: | 10.1186/s12933-022-01672-9 |
| Popis: | 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 p p p Conclusion A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters. |
| Databáze: | OpenAIRE |
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