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

Xiaolu Nie; Lulu Jia; Xiaoxia Peng; Houyu Zhao; Yuncui Yu; Zhenping Chen; Liqiang Zhang; Xiaoling Cheng; Yaqi Lyu; Wang Cao; Xiaoling Wang; Xin Ni; Siyan Zhan

doi:10.3389/fphar.2021.756207

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

Front Pharmacol. 2021 Nov 12:12:756207. doi: 10.3389/fphar.2021.756207. eCollection 2021.

Authors

Xiaolu Nie^{1

2}, Lulu Jia³, Xiaoxia Peng², Houyu Zhao¹, Yuncui Yu³, Zhenping Chen⁴, Liqiang Zhang⁵, Xiaoling Cheng³, Yaqi Lyu⁶, Wang Cao³, Xiaoling Wang³, Xin Ni⁷, Siyan Zhan^{1

8}

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
² Center for Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
³ Department of Pharmacy, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
⁴ Hematologic Disease Laboratory, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
⁵ Hematology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
⁶ Department of Medical Record Management, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
⁷ Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
⁸ Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, China.

Abstract

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.

Keywords: children; drug-induced thrombocytopenia; electronic medical records; post-marketing pharmacovigilance; signal detection.