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Open access

Mohammad Qasim Khan, Vijay Anand, Norbert Hessefort, Ammar Hassan, Alya Ahsan, Amnon Sonnenberg and Claus J. Fimmel

Abstract

Objective

To determine whether advanced cirrhosis - defined by the detection of nodular liver contours or portal venous collaterals on imaging studies - could be predicted by fibrosis algorithms, calculated using laboratory and demographic features extracted from patients’ electronic medical records. To this end, we compared seven EMR-based fibrosis scores with liver imaging studies in a cohort of HCV patients.

Methods

A search of our health system’s patient data warehouse identified 867 patients with chronic HCV infection. A total of 565 patients had undergone at least one liver imaging study and had no confounding medical condition affecting the imaging features or fibrosis scores. Demographic and laboratory data were used to calculate APRI, Fib4, Fibrosis Index, Forns, GUCI, Lok Index and Vira-HepC scores for all viremic patients who had undergone liver imaging. Data points selected for the calculation of these scores were based on laboratory results obtained within the shortest possible time from the imaging study. Areas under the receiver operating curves (AUROC), optimum cut-offs, sensitivities, specificities and positive and negative predictive values were calculated for each score.

Results

Seven algorithms were performed similarly in predicting cirrhosis. Sensitivities ranged from 0.65 to 1.00, specificities from 0.67 to 0.90, positive predictive values from 0.33 to 0.38, and negative predictive values from 0.93 to 1.00. No individual test was superior, as the confidence intervals of all AUROCs overlapped.

Conclusions

EMR-based scoring systems performed relatively well in ruling out advanced, radiologically-defined cirrhosis. However, their moderate sensitivity and positive predictive values limit their reliability for EMR-based diagnosis.

Open access

Amoah Yeboah-Korang, Mohammad I. Beig, Mohammad Q. Khan, Jay L. Goldstein, Don M. Macapinlac, Darryck Maurer, Amnon Sonnenberg and Claus J. Fimmel

Abstract

Background and Objectives

Hepatitis C virus (HCV) testing rates among U.S. birth-cohort patients have been studied extensively, limited data exists to differentiate birth-cohort screening from risk- or liver disease-based testing. This study aims to identify factors associated with HCV antibody (HCV-Ab) testing in a group of insured birth cohort patients, to determine true birth cohort testing rates, and to determine whether an electronic medical record (EMR)-driven Best Practice Alert (BPA) would improve birth cohort testing rates.

Methods

All birth-cohort outpatients between 2010 and 2015 were identified. HCV-Ab test results, clinical, and demographic variables were extracted from the EMR, and factors associated with testing were analyzed by logistic regression. True birth-cohort HCV screening rates were determined by detailed chart review for all outpatient visits during one calendar month. An automated Best Practice Alert was used to identify unscreened patients at the point of care, and to prompt HCV testing. Screening rates before and after system-wide implementation of the BPA were compared.

Results

The historic HCV-Ab testing rate was 11.2% (11,976/106,753). Younger age, female gender, and African American, Asian, or Hispanic ethnicity, and medical comorbidities such as chronic hemodialysis, HIV infection, and rheumatologic and psychiatric comorbidities were associated with higher testing rates. However, during the one-month sampling period, true age cohort-based testing was performed in only 69/10,089 patients (0.68%). Following the system-wide implementation of the HCV BPA, testing rates increased from 0.68% to 10.76% (P<0.0001).

Conclusions

We documented low HCV-Ab testing rates in our baby boomers population. HCV testing was typically performed in the presence of known risk factors or established liver disease. The implementation of an EMR-based HCV BPA resulted in a marked increase in testing rates. Our study highlights current HCV screening gaps, and the utility of the EMR to improve screening rates and population health.