THE USE OF NEAR REAL-TIME MICROBIOGRAM FOR TREATMENT DECISION OF PATIENTS WITH PRESUMED SEPSIS

Abstract

Sepsis is a severe condition caused by a series of host responses toward infection and may eventually result in death. High occurrence, mortality, and expensive treatment make it one of the most severe diseases. Despite deeper understanding of sepsis, effective therapies have saved many patients’ lives from it, yet mortality and cost are still high compared to many other diseases. Antimicrobials are effective against infections and mitigate sepsis progression if applied early and accurately. However, such treatment is hard since it needs clinicians’ expertise and a systematic understanding of the patient information. Due to the advance of data science focused on sepsis prediction, we created a near real-time microbiogram dashboard to facilitate clinicians with antimicrobial prescriptions after they have diagnosed presumed sepsis and begin therapy. It is done by displaying environmental data in Maryland and Electronic Health Record Data from Johns Hopkins Medical Institutions together to show clinicians community infection levels for different viruses. A comprehensive keyword grouping with other data management techniques is applied using Structured Query Language (SQL) and Python for data cleaning, data grouping, and filter creation. Data are visualized with Microsoft PowerBI and ESRI ArcGIS, where the information dashboard, mapping dashboard, and user interface are displayed. The microbiogram shows infection rates with precision up to census block group spatially and up to weeks temporally, allowing treatment advice at both macroscopic and microscopic levels. The filter section and reference map layers offer clinicians the freedom to customize settings and only select points of interest. Despite some trivial limitations, the microbiogram is a great starting point that combines patients’ demographics and microbiology lab tests to provide clinicians with more information about patients’ living environments. This may help clinicians to give more accurate initial antimicrobials before blood cultures results are available, and thus reduce mortality rate, reduce the cost, and improve clinical outcomes for sepsis. In addition, it’s a good platform for public health research, which can eventually benefit sepsis treatment. Therefore, it is worthy to research more on expanding the data elements, finding relationships among those, and exploring the potential to add practicability to the microbiogram