Electronic health records (EHRs) are expected to be a good source of data for pharmacovigilance. However, current quantitative methods are not applicable to EHR data. We propose a novel quantitative postmarketing surveillance algorithm, the Comparison of Laboratory Extreme Abnormality Ratio (CLEAR), for detecting adverse drug reaction (ADR) signals from EHR data. The methodology involves calculating the odds ratio of laboratory abnormalities between a specific drug-exposed group and a matched unexposed group. Using a 10-year EHR data set, we applied the algorithm to test 470 randomly selected drug-event pairs. It was found possible to analyze a single drug-event pair in just 109 ± 159 seconds. In total, 120 of the 150 detected signals corresponded with previously reported ADRs (positive predictive value (PPV) = 0.837 ± 0.113, negative predictive value (NPV) = 0.659 ± 0.180). By quickly and efficiently identifying ADR signals from EHR data, the CLEAR algorithm can significantly contribute to the utilization of EHR data for pharmacovigilance.

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Background Azithromycin exposure has been reported to increase the risk of QT prolongation and cardiovascular death. However, findings on the association between azithromycin and cardiovascular death are controversial, and azithromycin is still used in actual practice. Additionally, quantitative assessments of risk have not been performed, including the risk of QT prolongation when patients are exposed to azithromycin in a real-world clinical setting. Therefore, in this study, we aimed to evaluate the risk of exposure to azithromycin on QT prolongation in a real-world clinical setting using a 21-year medical history database of a tertiary medical institution. Methods We analyzed the electrocardiogram results and relevant electronic health records of 402,607 subjects in a tertiary teaching hospital in Korea from 1996 to 2015. To evaluate the risk of QT prolongation of azithromycin, we conducted a case-control analysis using amoxicillin for comparison. Multiple logistic regression analysis was performed to correct for age, sex, accompanying drugs, and disease. Results The odds ratio (OR) for QT prolongation (QTc>450 ms in male and >460 ms in female) on azithromycin exposure was 1.40 (95% confidence interval [CI], 1.23-1.59), and the OR for severe QT prolongation (QTc>500 ms) was 1.43 (95% CI, 1.13-1.82). On the other hand, the ORs on exposure to amoxicillin were 1.06 (95% CI, 0.97-1.15) and 0.88 (95% CI, 0.70-1.09). In a subgroup analysis, the risk of QT prolongation in patients aged between 60 and 80 years was significantly higher when they are exposed to azithromycin. Conclusions The risk of QT prolongation was increased when patients, particularly the elderly aged 60-79 years, were exposed to azithromycin. Therefore, clinicians should pay exercise caution using azithromycin or consider using other antibiotics, such as amoxicillin, instead of azithromycin.

Purpose Quantitative analytic methods are being increasingly used in postmarketing surveillance. However, currently existing methods are limited to spontaneous reporting data and are inapplicable to hospital electronic medical record (EMR) data. The principal objectives of this study were to propose a novel algorithm for detecting the signals of adverse drug reactions using EMR data focused on laboratory abnormalities after treatment with medication, and to evaluate the potential use of this method as a signal detection tool. Methods We developed an algorithm referred to as the Comparison on Extreme Laboratory Test results, which takes an extreme representative value pair according to the types of laboratory abnormalities on the basis of each patient's medication point. We used 10 years' EMR data from a tertiary teaching hospital, containing 32 033 710 prescriptions and 115 241 147 laboratory tests for 530 829 individual patients. Ten drugs were selected randomly for analysis, and 51 laboratory values were matched. The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were calculated. Results The mean number of detected laboratory abnormality signals for each drug was 27 (±7.5). The sensitivity, specificity, positive predictive value, and negative predictive value of the algorithm were 64 -100%, 22 -76%, 22 -75%, and 54 -100%, respectively. Conclusions The results of this study demonstrated that the Comparison on Extreme Laboratory Test results algorithm described herein was extremely effective in detecting the signals characteristic of adverse drug reactions. This algorithm can be regarded as a useful signal detection tool, which can be routinely applied to EMR data.

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ObjectivesA distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).MethodsWe transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.ResultsPatient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.ConclusionsWe successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.

Our findings suggest that DPP-4i use did not increase the risk of HF compared with sulfonylurea. In addition, the risks for cardiovascular outcomes were not elevated in DPP-4i-treated patients compared with sulfonylurea-treated patients.

Infection occurs frequently in patients with systemic lupus erythematosus (SLE), and has been a major cause of morbidity and mortality. However, no large-scale comprehensive studies have estimated the effect of clinical characteristics on serious infection in actual clinical practice yet. We investigated the influence of clinical characteristics on serious infections using electronic medical records data. We conducted a nested case-control study. Patients with SLE who developed serious infection which needs hospitalization or intravenous antibiotics (n = 120) were matched to controls (n = 240) who didn’t. Odds ratios (OR) and 95% confidence intervals (CIs) for infection associated with clinical features were obtained by conditional logistic regression analyses. The conditional logistic regression analysis with adjustment showed that serositis (OR, 2.76; 95% CI, 1.33–5.74), hematologic involvement (OR, 2.53; 95% CI, 1.32–4.87), and use of higher than the low dose of glucocorticoids (GCs; >7.5 mg/d prednisolone-equivalent) (OR, 2.65; 95% CI, 1.31–5.34) were related to serious infections in SLE. Serositis, hematologic involvement, and use of higher than the low dose of GCs were associated with serious infections in patients with SLE.