A machine learning algorithm for stratification of risk of cardiovascular disease in metabolic dysfunction-associated steatotic liver disease.
Eur J Intern Med
; 2024 Jul 16.
Article
em En
| MEDLINE
| ID: mdl-39013699
ABSTRACT
BACKGROUND:
Steatotic liver disease (SLD) is associated with adverse cardiac events. Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a condition characterized by the abnormal accumulation of hepatic lipids that is closely linked to five metabolic disorders overweight or obesity, impaired glucose regulation, hypertension, hypertriglyceridemia, and low high-density lipoprotein-cholesterol. This retrospective study aimed to stratify the risk of cardiac events in patients with MASLD.METHODS:
Patients diagnosed with MASLD through ultrasonography were evaluated. We implemented a machine learning-based approach using a survival classification and regression tree (CART) model to stratify patients based on age, and the number of risk scores was investigated as a predictor of adverse outcomes in the derivation cohort. The primary outcomes were major adverse cardiac events (MACE) including cardiac death, nonfatal myocardial infarction, and revascularization due to coronary artery disease.RESULTS:
Among 2,962 patients (median age, 62 years; men, 53.5 %), the distribution of risk factors was as follows one (10.8 %), two (28.5 %), three (33.0 %), four (19.9 %), and five (7.8 %). Over a median follow-up period of 6.8 years, 170 (5.7 %) patients experienced MACE. In the derivation cohort of 2,073 patients, the CART model identified age ≥60 years old and risk factors ≥4 as significant predictors of MACE. These findings were corroborated in a validation cohort of 889 patients. Patients meeting both criteria exhibited the highest risk of MACE (log-rank test, p < 0.001).CONCLUSIONS:
Patients aged ≥60 years old with risk factors ≥4 indicates at high risk of MACE in patients with MASLD. This risk stratification system provides a practical tool for identifying high-risk individuals in the MASLD population.
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MEDLINE
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En
Ano de publicação:
2024
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Article