Desogestrel/Ethinyl Estradiol

It is critical to assess the extent and progression of liver fibrosis for patients to receive suitable treatments, but its diagnostic methods remain unmet. Extracellular matrix protein 1 (ECM1) has previously been reported to be a key factor in the induction and progression of liver fibrosis. However, little is known about the use of ECM1 as a biomarker to evaluate fibrosis. In a CCl₄-induced mouse model of liver fibrosis, the present study demonstrated that ECM1 decreased with gradually increasing fibrosis. Using biopsy as a reference, the serum ECM1 levels decreased with increasing fibrosis stage in 247 patients with liver fibrosis, but there were no significant changes between fibrosis stage 2 and stage 0-1. To improve the performance of ECM1, age, platelet count, and ECM1 concentration were combined to calculate an EPA (ECM1-platelet-age) score (ranging from 0 to 10). The areas under the receiver operating characteristic curve of the EPA scores for the detection of F⩾2, F⩾3, and F4 were 0.6801, 0.7377, and 0.8083, respectively, which showed a comparable or significantly greater diagnostic performance for assessing fibrosis than that of the AST/ALT ratio, APRI score, or FIB-4 score. In HBV patients following antiviral treatment, the dynamics of the EPA score depended on the status of liver fibrosis development. The accuracy of the EPA score in predicting fibrosis regression and progression was 66.00% and 71.43%, respectively, while that of the LSM, another useful method for monitoring hepatic fibrosis changes during treatment, was only 52.00% and 7.14%, respectively. Compared with healthy controls, there were lower levels of serum ECM1 in HBV patients and individuals with HCV infection, MAFLD, ALD, PBC, and DILI. These findings suggested that individuals with reduced ECM1 levels may have a risk of developing liver injury, and further examinations or medical care are needed. In conclusion, the ECM1-containing EPA score is a valuable noninvasive test for staging fibrosis and predicting the progression of liver fibrosis. Additionally, ECM1 alone is an indicator for distinguishing patients with liver injury from healthy controls.

Nonalcoholic fatty liver disease (NAFLD) is the most common global cause of chronic liver disease and remains under‐recognized within healthcare systems. Therapeutic interventions are rapidly advancing for its inflammatory phenotype, nonalcoholic steatohepatitis (NASH) at all stages of disease. Diagnosis codes alone fail to recognize and stratify at‐risk patients accurately. Our work aims to rapidly identify NAFLD patients within large electronic health record (EHR) databases for automated stratification and targeted intervention based on clinically relevant phenotypes. We present a rule‐based phenotyping algorithm for efficient identification of NAFLD patients developed using EHRs from 6.4 million patients at Columbia University Irving Medical Center (CUIMC) and validated at two independent healthcare centers. The algorithm uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model and queries structured and unstructured data elements, including diagnosis codes, laboratory measurements, and radiology and pathology modalities. Our approach identified 16,006 CUIMC NAFLD patients, 10,753 (67%) previously unidentifiable by NAFLD diagnosis codes. Fibrosis scoring on patients without histology identified 943 subjects with scores indicative of advanced fibrosis (FIB‐4, APRI, NAFLD–FS). The algorithm was validated at two independent healthcare systems, University of Pennsylvania Health System (UPHS) and Vanderbilt Medical Center (VUMC), where 20,779 and 19,575 NAFLD patients were identified, respectively. Clinical chart review identified a high positive predictive value (PPV) across all healthcare systems: 91% at CUIMC, 75% at UPHS, and 85% at VUMC, and a sensitivity of 79.6%. Our rule‐based algorithm provides an accurate, automated approach for rapidly identifying, stratifying, and sub‐phenotyping NAFLD patients within a large EHR system.