Identification and Fibrosis Staging of Hepatitis C Patients Using the Electronic Medical Record System

Anand, Vijay K.; Hyun, Christian; Khan, Qasim M.Rana; Hall, Curtis R.; Hessefort, Norbert; Sonnenberg, Amnon; Fimmel, Claus J.

doi:10.1097/mcg.0000000000000519

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…[6] We observed that the scores performed better at higher fibrosis stages, with the highest AUROCs observed for the detection of cirrhosis. Given this trend, we hypothesized Figure 1: HCV study population.…”

Section: Discussionmentioning

confidence: 53%

“…[6] We noticed that the scores were better suited to detect higher fibrosis stages, although their overall performance as judged by AUROCs was only moderate. We hypothesized that an EMR-based approach could be used to identify patients with the most advanced fibrosis stage.…”

Section: Journal Of Translational Internal Medicine / Jan-mar 2017 / mentioning

confidence: 99%

“…This finding is in agreement with a recent meta-analysis by Chou, [19] and with our own recent study. [6] The fourth major finding was the demonstration that all scoring algorithms had high negative predictive values, indicating that they are well-suited to rule out advanced cirrhosis. This feature could be incorporated into an EMRbased staging approach, as it would identify a lower-risk group.…”

Section: Guci Lokmentioning

confidence: 99%

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Utility of electronic medical recordbased fibrosis scores in predicting advanced cirrhosis in patients with hepatitic C virus infection

Khan

Anand

Hessefort

et al. 2017

Journal of Translational Internal Medicine

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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.

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Section: Discussionmentioning

confidence: 53%

Section: Journal Of Translational Internal Medicine / Jan-mar 2017 / mentioning

confidence: 99%

Section: Guci Lokmentioning

confidence: 99%

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Utility of electronic medical recordbased fibrosis scores in predicting advanced cirrhosis in patients with hepatitic C virus infection

Khan

Anand

Hessefort

et al. 2017

Journal of Translational Internal Medicine

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The relationship between IFNL4 genotype and the rate of fibrosis in hepatitis C patients

Gulati

Wimalanathan

Andersen

et al. 2019

Scandinavian Journal of Gastroenterology

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Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

Lee¹,

Doktorchik²,

Martin³

et al. 2021

JMIR Med Inform

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Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

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Identification and Fibrosis Staging of Hepatitis C Patients Using the Electronic Medical Record System

Abstract: Our study demonstrates that HCV-infected patients can be identified and their fibrosis staged using commonly available EMR-based algorithms.

Cited by 4 publications

References 16 publications

Utility of electronic medical recordbased fibrosis scores in predicting advanced cirrhosis in patients with hepatitic C virus infection

Utility of electronic medical recordbased fibrosis scores in predicting advanced cirrhosis in patients with hepatitic C virus infection

The relationship between IFNL4 genotype and the rate of fibrosis in hepatitis C patients

Electronic Medical Record–Based Case Phenotyping for the Charlson Conditions: Scoping Review

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