2441. Automated, Rapid Detection of Potential Healthcare-Acquired Infection Clusters Based on Microbiology And Patient Geotemporal Data

BACKGROUND: Whole-genome sequencing (WGS) has shown promise in identifying transmissions of healthcare-associated infections (HAIs), but it may be costly to sequence all potential HAIs. By automatically identifying samples likely to be HAIs, WGS can be focused on specific samples. We describe an algorithm that quickly identifies potential HAI clusters by analyzing patient geotemporal and pathogen microbiology data. This approach systematically triages potential HAI investigations to aid infection control professionals (ICPs) in their workflow. METHODS: This novel algorithm within Philips IntelliSpace Epidemiology scores the potential of transmission for pairs of infections. Inputs include microbiology (MB) data (genus- or species-level identification and antimicrobial susceptibility test results) and geotemporal (GT) data (timing of sample collection and shared location stays). From the resulting pairwise scores, clusters of potential HAIs are identified. Leveraging 9 months (June, 2018 – March, 2019) of data from a 900-bed US hospital (i.e., 2825 samples, 1814 patients and 13 organisms—of which a subset of 404 samples had WGS performed concomitantly with MB studies), we evaluated the extent to which this algorithm captures genetically similar sample pairs. RESULTS: Pairwise scores enrich for genetically similar samples when considering MB data only (odds ratio: 17.3), GT only (odds ratio: 6.1) and a combination of both (odds ratio: 19.8), with highly significant P-values for all (P < 10(−16)). Considering MB only, 91% of samples group together in potential transmission clusters. With MB and GT data, this fraction drops to 24.6% (694 samples) forming 178 possible clusters, 173 of which contain fewer than ten samples each. The 5 larger clusters contain 40–64 samples each and span multiple units in the hospital. CONCLUSION: The proposed system automatically suggests potential HAI clusters. By combining MB and GT data, the number of samples to review is reduced, enabling ICPs to focus their attention and sequencing efforts. By focusing on a targeted group of higher probability clusters, ICPs may be able to increase their efficiency and effectiveness in controlling the spread of HAIs—thus boosting potential for patient safety and amelioration of cost of care. DISCLOSURES: All authors: No reported disclosures.