Assessment of non-alcoholic fatty liver disease in suspected coronary artery disease patients: prognostic value and incremental predictive utility over cardiovascular risk factors and CCTA

对疑似冠状动脉疾病患者进行非酒精性脂肪性肝病评估:预后价值及相对于心血管危险因素和冠状动脉CT血管造影的增量预测效用

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Abstract

OBJECTIVE: This study assessed the prognostic value of non-alcoholic fatty liver disease (NAFLD) in predicting major adverse cardiovascular events (MACE) in patients with suspected coronary artery disease (CAD), using coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR). METHODS: In this retrospective study, patients who underwent both CCTA and non-contrast liver/spleen CT at Dalian Medical University First Affiliated Hospital from January 2017 to December 2018 were included. NAFLD was diagnosed via CT and clinical history. MACE included cardiovascular/cerebrovascular death, all-cause mortality, myocardial infarction, unstable angina hospitalization, unplanned revascularization, and stroke. Patients were divided into NAFLD and non-NAFLD groups. Cox regression assessed the association between NAFLD and MACE, adjusting for cardiovascular risk factors, CCTA findings, and CT-FFR results. Subgroup and time-dependent C-index analyses evaluated prognostic performance across populations and follow-up duration. RESULTS: Among 2,981 patients (737 with NAFLD), 408 experienced MACE over a median 68-month of follow-up. The NAFLD group had higher CAD-RADS scores, high-risk plaque, coronary calcification, and CT-FFR positivity, all p < 0.05. NAFLD independently predicted MACE (adjusted HR: 1.39; 95% CI: 1.15, 1.73; p < 0.001), especially in males, smokers, hypertensive and non-diabetic patients, and those with non-obstructive CAD or normal CT-FFR. Including NAFLD improved model performance at all time points, with C-index at 60 months of 0.753 vs. 0.727 (model2) and 0.695 (model 1), p < 0.001. CONCLUSION: NAFLD serves as an independent prognostic indicator for MACEs in patients with suspected CAD. The incorporation of NAFLD into risk stratification models significantly enhances predictive accuracy, especially within high-risk sub-populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-025-01919-3.

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