P-1121. Resistance-Driven Outbreak Detection and Etiologic Investigation for Hospital-Onset Pseudomonas aeruginosa Using Machine Learning on Electronic Health Records

Abstract Background Hospital outbreak identification often lacks standardization and uses time-intensive surveillance, potentially delaying intervention against hospital-acquired pathogens. We combined space-time permutation analysis with machine learning to statistically identify hospital-onset Pseudomonas aeruginosa outbreaks and contributing etiologies. Methods We retrospectively analyzed hospital-onset P. aeruginosa isolates (2016-2024) using electronic health records (EHR) at a single tertiary care center. Hospital-onset infections were defined as cultures collected more than two days after admission. Susceptibility profiles were standardized by imputing intrinsic resistance and classifying extensive (XDR) or multidrug-resistance (MDR). Space-time permutation analysis (modified WHONET-SatScan) identified resistance-based clusters, validated by whole-genome sequencing (WGS). For each cluster, we conducted case-control studies comparing identified cases with non-case P. aeruginosa during the same period. Feature selection for temporally scaled risk factors was performed using elastic net regularization. Results Among 19,055 hospitalizations (11,112 patients) with P. aeruginosa, 6,557 (34.4%) were hospital-onset. Hospital-onset isolates were 1.9 (95% CI: 1.8–2.1) times more likely to be MDR and 2.2 (95% CI: 1.9–2.6) times more likely to be XDR compared to community-onset. Of 615 unique resistance profiles identified, 53% were susceptible to all antipseudomonal classes. Resistance to antipseudomonal cephalosporins was most common (30.2%). Space-time permutation analysis detected 12 unique clusters, none matching WGS-identified clusters. One cluster defined by cephalosporin resistance (n=17) was further examined. Final elastic net model identified 32 features associated with cluster membership compared to controls (n=213), including open excision 5–9 days prior (OR: 28.1, p< 0.001) and skin graft 2 days prior (OR: 45.2, p< 0.001). Conclusion We demonstrate the use of EHRs to statistically detect related hospital-onset P. aeruginosa and suggest biologically plausible etiologies. Resistance-based models did not align with clusters confirmed by WGS. Future research will establish causal links and extend to other hospital-acquired pathogens. Disclosures Kathryn DeSear, PharmD, Abbvie: Advisor/Consultant|Biomerieux: Advisor/Consultant|Cormedix: Speaking|GSK: Advisor/Consultant|Shionogi: Speaking