Acute Severe Thrombocytopenia Event Associated with Trimethoprim/Sulfamethoxazole Use

Sulfamethoxazole-trimethoprim for pneumocystis pneumonia prophylaxis, causes of discontinuation and thrombocytopenia observed during administration: A single-center retrospective study

INTRODUCTION

The development of pneumocystis pneumonia (PCP) has recently become a growing concern; thus, its prevention has become increasingly important. Sulfamethoxazole-trimethoprim (ST) is a cost-effective first-line and prophylactic treatment for PCP. However, ST administration criteria for PCP prophylaxis remain unclear and are often discontinued because of adverse events (AEs). In this study, we aimed to investigate the causes of ST discontinuation and the associated AEs using objective data.

METHODS

We retrospectively analyzed the data of 162 patients admitted to Kansai Medical University Hospital between January 2018 and December 2020, who received ST for PCP prophylaxis. We compared clinical characteristics, laboratory data, and incidence of AEs between ST non-discontinuation and ST discontinuation groups. Additionally, we divided the patients into non-developing and developing thrombocytopenia (≥ Grade 1) groups based on the investigation results.

RESULTS

No patients developed PCP while receiving ST. The most common causes of ST discontinuation were thrombocytopenia (37%), liver dysfunction (20%), and rash (18%). Multivariate analysis revealed thrombocytopenia (≥ Grade 1) as a factor significantly associated with ST discontinuation. Furthermore, we identified three factors correlated with thrombocytopenia (≥ Grade 1): age ≥50 years, lymphocyte count <1000/μL, and platelet count <180,000/μL.

CONCLUSIONS

Patients with the aforementioned factors are at higher risk of developing thrombocytopenia (≥ Grade 1) during ST administration for PCP prophylaxis. Therefore, platelet count monitoring is essential to enhance safety and efficacy of ST treatment. Nonetheless, further research is warranted to explore additional implications and interventions.

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.