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

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.