Accuracy of Non-invasive Indices for Diagnosing Hepatic Steatosis Compared to Imaging in a Real-World Cohort

DIGESTIVE DISEASES AND SCIENCES(2022)

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摘要
Background & Aims Nonalcoholic fatty liver disease is common and under-diagnosed. This study evaluated the accuracy of several previously reported indices, including hepatic steatosis index, alanine aminotransferase (ALT) method, Framingham steatosis index, and Dallas steatosis index, to diagnose hepatic steatosis in a real-world cohort. Methods This study included 701 randomly selected adult patients seen in our integrated healthcare system between 2015 and 2020 with appropriate abdominal imaging and routine outpatient laboratory studies. Information on demographics, comorbidities and existing liver disease, anthropometrics, laboratory studies, and abdominal imaging was collected. The sensitivity, specificity, and C-statistic of each method in detecting hepatic steatosis based on abdominal imaging were determined. Results 202/701 patients (28.8%) had hepatic steatosis on abdominal imaging. These patients were more likely to have metabolic syndrome components and higher body mass index. All indices performed similarly with moderate accuracy in detecting hepatic steatosis based on the C-statistic (95% confidence interval): Hepatic steatosis index 0.76 (0.72–0.79), Framingham steatosis index 0.78 (0.74–0.82), and Dallas steatosis index 0.80 (0.76–0.83). ALT method had sensitivity 44.7% (36.9–52.7%) and specificity 88.6% (85.0–91.7%). Several sensitivity analyses were performed, which did not significantly alter the performance of any index. Conclusion The findings support both the clinical utility of these indices in diagnosing hepatic steatosis in the absence of imaging in real-world settings and the research utility of these indices in generating reliable electronic medical record-based nonalcoholic fatty liver disease cohorts.
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关键词
Nonalcoholic fatty liver disease,Hepatic steatosis,Screening,Alanine aminotransferase,Framingham steatosis index,Dallas steatosis index
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