“We are pleased to be awarded this SBIR grant and are honored to have the support from NIH to further the development of ksim, our kmer-based algorithm for HAI analysis,” said Jong Lee, CEO and co-founder of Day Zero Diagnostics. “Our goal is to leverage ksim’s precision, speed, and computational efficiency to enhance and expand our recently launched epiXactSM service for investigating suspected HAI outbreaks. Additionally, ksim will enable transformational strategies for outbreak detection and intervention that are not currently possible because it allows the automated processing of large datasets in real-time.” The rising prevalence of antibiotic-resistant organisms has dramatically increased the risks of HAIs, which already affect 4-5% of hospitalized patients in the U.S. and result in 99,000 patient deaths per year.1 Preventing HAIs can lead to fewer patients requiring antibiotic treatment, shorter hospital stays, and reduced exposure to antibiotic-resistant organisms.2 Ksim promises to deliver a faster, more scalable, high-resolution approach for identifying HAI outbreaks. The algorithm processes whole genome sequencing data in seconds, without the need for manual analysis steps, or the degree of computational intensity and dedicated time from a computational biologist required to conduct traditional sequence analysis.
In this Phase I grant, Day Zero Diagnostics will continue the development and initial validation of ksim using data from published hospital outbreaks, a large dataset from a hospital, and data from the company’s epiXact service. EpiXact provides hospitals with a determination of infection relatedness in a suspected outbreak using whole genome sequencing data that is analyzed by the company’s team of expert computational biologists. With ksim powering the epiXact service, infection control teams will be armed with actionable results in less than 24 hours, a timeframe that can have a significant impact on the intervention decisions a hospital might employ to improve patient safety.
1 Klevens, R.M., et al., Estimating Health Care-Associated Infections and Deaths in U.S. Hospitals, 2002.
Public Health Reports, 2007. 122(2): p. 160-166.