Analysis of the therapeutic efficacy of bacterial infections through medical big data

In recent years, many studies have been conducted on various diseases to evaluate clinical efficacy reflecting actual clinical conditions through comprehensive analysis using medical big data, which include various patient groups and factors in clinical practice. On the other hand, there are still very few research reports in the world related to the treatment of infectious diseases using medical big data. This is due to the fact that much medical big data lacks information on the causative organisms of infectious diseases and on determining the effectiveness of infectious disease treatment. In this paper, we introduce a research case study in which analysis on the effectiveness of infectious disease treatment was conducted using medical big data. In this study, we performed a retrospective analysis of two real databases with the aim of validating the usefulness of cefmetazole and flomoxef in urinary tract infections (UTI) in which broad-spectrum β-lactamase (ESBL)-producing bacteria are the primary initiating organisms. Third-generation cephalosporin-resistant E. coli and K. pneumoniae, including ESBL-producing strains, were similarly susceptible to flomoxef and cefmetazole. JMDC Claims data analysis showed that the median time of hospital stay duration was significantly shorter in the flomoxef group than in the cefmetazole group. Flomoxef exhibits effectiveness that is comparable to cefmetazole in treating UTI. When using currently available medical big data to conduct analyses related to infectious disease treatment, valuable analysis results may be obtained by understanding the characteristics of the database and collaborating with clinicians who are familiar with infectious disease treatment.