Promotion of the appropriate use of antimicrobial agents by utilizing medical big data

Abstract

The global surge in antimicrobial resistance (AMR) highlights the critical need for the development of innovative therapies and the appropriate use of antimicrobial agents. Our research focused on preventing, managing, and mitigating the adverse effects of treatments for infection with methicillin-resistant Staphylococcus aureus. In this review, we present our investigations utilizing medical big data. The first study aimed to elucidate the relationship between renal outcome and survival following the onset of vancomycin-associated nephrotoxicity (VAN). An initial analysis using the US Food and Drug Administration Adverse Events Reporting System (FAERS) database revealed elevated mortality rates among patients with VAN compared with those without VAN, forming the basis for further investigation. A subsequent, more rigorous, retrospective analysis using electronic medical records confirmed that poor survival outcomes were significantly associated with non-recovery from VAN, particularly when progression to acute kidney injury of stage ≥2 occurred. Therefore, preventing progression to severe VAN is critical for enhancing survival outcomes. The second study investigated the relationship between statin use and daptomycin-related musculoskeletal adverse events. By employing a mixed-method approach combining meta-analysis with disproportionality analysis of the FAERS data, a significant association between statin therapy and daptomycin-related rhabdomyolysis was identified. This highlights the importance of cautious statin and daptomycin use, with careful consideration of potential safety risks. Each medical big-data database possesses unique characteristics that require careful consideration during analysis. The accurate interpretation of medical big data, coupled with its integration with complementary methodologies, will produce more robust and reliable research outcomes across diverse fields.