An Observational Study of Sepsis in Takeo Province Cambodia: An in-depth examination of pathogens causing severe infections

The world’s most consequential pathogens occur in regions with the fewest diagnostic resources, leaving the true burden of these diseases largely under-represented. During a prospective observational study of sepsis in Takeo Province Cambodia, we enrolled 200 patients over an 18-month period. By coupling traditional diagnostic methods such as culture, serology, and PCR to Next Generation Sequencing (NGS) and advanced statistical analyses, we successfully identified a pathogenic cause in 46.5% of our cohort. In all, we detected 25 infectious agents in 93 patients, including severe threat pathogens such as Burkholderia pseudomallei and viral pathogens such as Dengue virus. Approximately half of our cohort remained undiagnosed; however, an independent panel of clinical adjudicators determined that 81% of those patients had infectious causes of their hospitalization, further underscoring the difficulty of diagnosing severe infections in resource-limited settings. We garnered greater insight as to the clinical features of severe infection in Cambodia through analysis of a robust set of clinical data.
Author summary We enrolled 200 severely ill patients in an observational study of sepsis in Takeo Province Cambodia. In an effort to provide an in-depth characterization of their illness, we combined standard clinical microbiology practices (culture, microscopy, etc) with serological analyses, newer molecular diagnostics, and next-generation sequencing to produce the most detailed characterization to date of sepsis and microbiological cause for southern Cambodia. Specifically, we were able to identify a causative pathogen in 46.5% of cases. A viral source was identified in 26 (13%) patients and bacterial source in 79 (39.5%) patients. In all, we detected 139 infections in 93 (46.5%) patients. Despite our best efforts though, 52.5% of the cohort remained undiagnosed, further underscoring the urgent need for new infectious disease diagnostics that can identify the cause of infections in near real time.