Pilot Algorithm Designed to Help Early Detection of HMG-CoA Reductase Inhibitor-Induced Hepatotoxicity.
10.4258/hir.2017.23.3.199
- Author:
Joo Young HONG
1
;
Hun Sung KIM
;
In Young CHOI
Author Information
1. Division of Biomedical Informatics, Systems Biomedical Informatics Research Centre, Seoul National University College of Medicine, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Hydroxymethylglutaryl-CoA Reductase Inhibitors;
Drug-Related Side Effects and Adverse Reactions;
Drug-Induced Liver Injury;
Electronic Medical Records;
Alanine Transaminase;
Alkaline Phosphatase
- MeSH:
Alanine Transaminase;
Alkaline Phosphatase;
Diagnosis;
Drug-Induced Liver Injury;
Drug-Related Side Effects and Adverse Reactions;
Electronic Health Records;
Humans;
Hydroxymethylglutaryl-CoA Reductase Inhibitors;
Korea;
Liver;
Outpatients;
Oxidoreductases*;
Prescriptions;
Seoul
- From:Healthcare Informatics Research
2017;23(3):199-207
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: To enable early detection of adverse drug reactions (ADRs) in patients using HMG-CoA reductase inhibitors (statins), we developed an algorithm that automatically detects liver injury caused by statins from Electronic Medical Record (EMR) data. We verified the performance of our algorithm through manual ADR assessment and a direct chart review. METHODS: The subjects in this study were patients who had been prescribed a statin for the first time among outpatients in Seoul St. Mary's Hospital in Korea between January 2009 and December 2012. We extracted basic information about the patients, including laboratory information, underlying disease, diagnosis information, prescription information, and concomitant drugs. We developed an automatic ADR detection algorithm by using EMR data. We validated the results of the algorithm through a chart review. RESULTS: We developed the algorithm to assess ADR occurrences based on alanine transaminase (ALT) and alkaline phosphatase (ALP) levels. According to the proposed algorithm, any of these result options could be attained: ADR-free, little association, strong association, and weak association or indeterminable. The results of the ADR assessments obtained using the proposed algorithm showed that the data of 126 patients (1.4% of all 9,241 patients) included suspicious figures, thus indicating the possibility of an ADR. In the EMR chart review for verifying the algorithm, ADRs of 33 patients were not associated with statin use; therefore, the ADR occurrence rate was found to be 1.0% (93/9,241). Therefore, the positive predictive value was calculated to be 73.8% (93/126; 95% confidence interval, 69.2%–77.6%). No differences were observed between statin types (p = 0.472). CONCLUSIONS: For early detection of statin-induced liver injury, we developed an automatic ADR assessment algorithm. We expect that algorithms that are more reliable can be developed if we conduct supplement clinical studies with a focus on adverse drug effects.
