RESUMO
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.
RESUMO
BACKGROUND AND AIMS: The aim of this study was to noninvasively assess the severity of chronic hepatitis C virus (HCV) in large patient populations. It would be helpful if fibrosis scores could be calculated solely on the basis of data contained in the patients' electronic medical records (EMR). We performed a pilot study to identify all HCV-infected patients in a large health care system, and predict their fibrosis stage on the basis of demographic and laboratory data using common data from their EMR. MATERIALS AND METHODS: HCV-infected patients were identified using the EMR. The liver biopsies of 191 HCV patients were graded using the Ishak and Metavir scoring systems. Demographic and laboratory data were extracted from the EMR and used to calculate the aminotransferase to platelet ratio index, Fib-4, Fibrosis Index, Forns, Göteborg University Cirrhosis Index, Lok Index, and Vira-HepC. RESULTS: In total, 869 HCV-infected patients were identified from a population of over 1 million. In the subgroup of patients with liver biopsies, all 7 algorithms were significantly correlated with the fibrosis stage. The degree of correlation was moderate, with correlation coefficients ranging from 0.22 to 0.60. For the detection of advanced fibrosis (Metavir 3 or 4), the areas under the receiver operating characteristic curve ranged from 0.71 to 0.84, with no significant differences between the individual scores. Sensitivities, specificities, and positive and negative predictive values were within the previously reported range. All scores tended to perform better for higher fibrosis stages. CONCLUSIONS: Our study demonstrates that HCV-infected patients can be identified and their fibrosis staged using commonly available EMR-based algorithms.
