Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance

Lee, Suehyun; Han, Jongsoo; Park, Rae Woong; Kim, Grace Juyun; Rim, John Hoon; Cho, Jooyoung; Lee, Kye Hwa; Lee, Jisan; Kim, Sujeong; Kim, Ju Han

doi:10.1007/s40264-018-0767-7

Cited by 18 publications

(13 citation statements)

References 45 publications

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“…Second, informatics resources allow high-throughput pharmacoinformatics study to screen a large number of ADEs. For instance, algorithms to annotate different coding systems allow more than a hundred ADEs to be identified from longitudinal health data (22)(23)(24). Currently, the development of…”

Section: Accepted Articlementioning

confidence: 99%

“…For instance, algorithms to annotate different coding systems allow more than a hundred ADEs to be identified from longitudinal health data. 22 , 23 , 24 Currently, the development of large‐scale longitudinal health data and informatics resources facilitate a high‐throughput pharmacoinformatic study using large‐scale longitudinal health data. However, the control selection process in the case‐control design requires a tremendous amount of time for data preparation.…”

Section: Introductionmentioning

confidence: 99%

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Random control selection for conducting high‐throughput adverse drug events screening using large‐scale longitudinal health data

Chiang

Zhang

Donneyong

et al. 2021

CPT Pharmacom & Syst Pharma

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Case-control design based high-throughput pharmacoinformatics study using large-scale longitudinal health data is able to detect new adverse drug events (ADEs) signals. Existing control selection approaches for case-control design including dynamic/super control selection approach. Dynamic/super control selection approach requires all individuals to be evaluated at all ADE case index dates, as the individuals' eligibilities as control depend on ADE/enrolment history. Thus, using large-scale longitudinal health data, dynamic/super control selection approach requires extraordinarly high computational time. We proposed a random control selection approach in which ADE case index dates were matched by randomly generated control index dates. Random control selection approach doesn't depend on ADE/enrolment history. It is able to significantly reduce computational time to prepare case-control data sets, as it requires all individuals to be evaluated only once. We compared the performance metrics of all control selection approaches using two large-scale longitudinal health data and a drug-ADE gold standard including 399 drug-ADE pairs. The F-scores for random control selection approach were between 0.586 -0.600 compared to between 0.545 -0.562 for dynamic/super control selection approaches. Random control selection approach was approximately 1,000 times faster than dynamic/super control selection approach on preparing case-control data sets. With large-scale longitudinal health data, case-control design based pharmacoinformatics study using random control selection is able to generate comparable ADE signals than the existing control selection approaches. Random control selection approach also significantly reduces computational time to prepare the case-control data sets.

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Section: Accepted Articlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random control selection for conducting high‐throughput adverse drug events screening using large‐scale longitudinal health data

Chiang

Zhang

Donneyong

et al. 2021

CPT Pharmacom & Syst Pharma

View full text Add to dashboard Cite

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“…Previous, a comprehensive controlled vocabulary-based ADR signal dictionary (CVAD) 17 dedicated to pharmacovigilance was developed for detecting and evaluating ADR signals using multicenter EHR data. Based on the previous study, we developed a new method of term mapping between MedDRA and WHO-ART.…”

Section: Meddra and Who-art Adr Term Mappingmentioning

confidence: 99%

Real-world data-based adverse drug reactions detection from the Korea Adverse Event Reporting System databases with electronic health records-based detection algorithm

Shin

Cha

Lee

et al. 2021

Health Informatics J

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Pharmacovigilance involves monitoring of drugs and their adverse drug reactions (ADRs) and is essential for their safety post-marketing. Because of the different types and structures of medical databases, several previous surveillance studies have analyzed only one database. In the present study, we extracted potential drug–ADR pairs from electronic health record (EHR) data using the MetaNurse algorithm and analyzed them using the Korean Adverse Event Reporting System (KAERS) database for systematic validation. The Medical Dictionary for Regulatory Activities (MedDRA) and World Health Organization (WHO) Adverse Reactions Terminology (WHO-ART) were mapped for signal detection. We used the Side Effect Resource (SIDER) database to select 2663 drug-ADR pairs to investigate unknown drug-induced ADRs. The reporting odds ratio (ROR) value was calculated for the drug-exposed and non-exposed groups of drug–ADR pairs, and 19 potential pairs showed significant signals. Appropriate terminology systems and criteria are needed to handle diverse medical databases.

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“…Several studies have been conducted to develop methods for detecting DITP signals using electronic health records ( Moore et al, 2009 ; Ramirez et al, 2010 ; Lee et al, 2019 ) using laboratory test results and narrative texts. The findings showed that linezolid, ganciclovir, and 5-fluorouracil were potentially associated with thrombocytopenia.…”

Section: Introductionmentioning

confidence: 99%

Detection of Drug-Induced Thrombocytopenia Signals in Children Using Routine Electronic Medical Records

et al. 2021

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Background: Drug-induced thrombocytopenia (DITP) is a severe adverse reaction and a significantly under-recognized clinical problem in children. However, for post-marketing pharmacovigilance purposes, detection of DITP signals is crucial. This study aimed to develop a signal detection model for DITP using the pediatric electronic medical records (EMR) data.Methods: This study used the electronic medical records collected at Beijing Children’s Hospital between 2009 and 2020. A two-stage modeling method was developed to detect the signal of DITP. In the first stage, we calculated the crude incidence by mining cases of thrombocytopenia to select the potential suspected drugs. In the second stage, we constructed propensity score–matched retrospective cohorts of specific screened drugs from the first stage and estimated the odds ratio (OR) and 95% confidence interval (CI) using conditional logistic regression models. The novelty of the signal was assessed by current evidence.Results: In the study, from a total of 839 drugs, 21 drugs were initially screened as potentially inducing thrombocytopenia. In total, we identified 18 positive DITP associations. Of these, potential DITP risk of nystatin (OR: 1.75, 95% CI: 1.37–2.22) and latamoxef sodium (OR: 1.61, 95% CI: 1.38–1.88) were two new DITP signals in both children and adults. Six associations between thrombocytopenia and drugs including imipenem (OR: 1.69, 95% CI: 1.16–2.45), teicoplanin (OR: 4.75, 95% CI: 3.33–6.78), fusidic acid (OR: 2.81, 95% CI: 2.06–3.86), ceftizoxime sodium (OR: 1.83, 95% CI: 1.36–2.45), ceftazidime (OR: 2.16, 95% CI: 1.58–2.95), and cefepime (OR: 5.06, 95% CI: 3.77–6.78) were considered as new signals in children.Conclusion: This study developed a two-stage algorithm to detect safety signals of DITP and found eighteen positive signals of DITP, including six new signals in a pediatric population. This method is a promising tool for pharmacovigilance based on EMR data.

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Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance

Cited by 18 publications

References 45 publications

Random control selection for conducting high‐throughput adverse drug events screening using large‐scale longitudinal health data

Random control selection for conducting high‐throughput adverse drug events screening using large‐scale longitudinal health data

Real-world data-based adverse drug reactions detection from the Korea Adverse Event Reporting System databases with electronic health records-based detection algorithm

Detection of Drug-Induced Thrombocytopenia Signals in Children Using Routine Electronic Medical Records

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