1248. A Machine-Learning Approach to Predict the Cefazolin Inoculum Effect in Methicillin-Susceptible Staphylococcus aureus. (4th December 2021)

• Record Type: Journal Article
• Authors: Rios, Rafael; Gomez-Villegas, Sara I; McNeil, Jonathon C; Carvajal, Lina P; Rincon, Sandra; Dinh, An Q; Echeverri, Aura M; Espitia-Acero, Catalina; Vargas, Sandra; Flores, Anthony R; Flores, Anthony R; Sommer, Lauren; Kaplan, Sheldon L; Arias, Cesar A; Diaz, Lorena; Diaz, Lorena; Reyes, Jinnethe
• Is Part Of: Open forum infectious diseases. Volume 8(2021)Supplement 1
• Page Start: S712
• Page End: S713
• Publication Date: 2021-12-04
• Subjects: Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofab466.1440
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: The cefazolin (Cz) inoculum effect (CzIE), defined as an increase in the Cz MIC to ≥16 µg/mL at high inoculum (10 7 CFU/mL), has been associated with poor outcomes in MSSA bacteremia and osteomyelitis. The CzIE is associated with the BlaZ β-lactamase, encoded by blaZ and regulated by BlaR (antibiotic sensor) and BlaI (transcriptional repressor). Here, we aimed to obtain a machine-learning (ML) model to predict the presence of the CzIE based on the nucleotide sequence of the entire bla operon and its regulatory components. Methods: Using whole genome sequencing, we analyzed the nucleotide sequences of the entire bla operon in 436 MSSA isolates recovered from blood, soft-tissue infections or pneumonia in adults ( training-testing cohort, prevalence of the CzIE: 46%). Also, 32 MSSA recovered from pediatric patients with osteomyelitis with the CzIE were included as validation cohort . The CzIE was determined by broth microdilution at high inoculum. K-mer counts were obtained from the bla operon sequences of the isolates from the testing-training cohort, and then used in a ML pipeline which i ) discards uninformative K-mers, ii ) identifies optimal hyper-parameters and, iii ) performs training of the model using 70% of the sequences as training set and 30% as testing set. The pipeline tested 11 different K-mer sizes and 2 models: Logistic Regression (LR) and Support Vector Machine (SVM). Finally, the model with best predictive ability was applied to the sequences of the MSSA osteomyelitis isolates (validation cohort). Results: The ML approach had high specificity ( >90%), accuracy ( >80%) and ROC-AUC values ( >0.7) for detecting the CzIE in the testing set of isolates (Figure 1), independently of the type of model or the K-mer size used. The best predictive ability was with LR using K-mers of 17 nucleotides, with an accuracy of 84%, specificity of 96%, and sensitivity of 70% in the testing set (Figure 2). In the validation cohort, the model was capable to correctly identify all the strains exhibiting the CzIE (100% sensitivity). Figure 1. Prediction metrics of the ML pipeline for the detection of the CzIE in MSSA isolates from the training-test cohort. Predictions are shown accordingly to the model and K-mer sizes tested. Figure 2. ROC of best predictive model (Logistic Regression, K-mer size 17) for the detection of the CzIE in MSSA isolates. Conclusion: The ML approach is a promising genomic application to detect the CzIE in MSSA isolates of a variety of sources, bypassing phenotypic testing. Further validation is needed to evaluate its possible utility in clinical settings. Disclosures: Jonathon C. McNeil, MD, Agency for Healthcare Research and Quality (Research Grant or Support)Allergan (Grant/Research Support)Nabriva (Grant/Research Support, Other Financial or Material Support, Site PI for a multicenter trial) Anthony R. Flores, MD, MPH, PhD, Nothing to disclose Sheldon L. Kaplan, MD, Pfizer (Research Grant or Support) Cesar A. Arias, M.D., MSc, Ph.D., FIDSA, Entasis Therapeutics (Grant/Research Support)MeMed Diagnostics (Grant/Research Support)Merk (Grant/Research Support) Lorena Diaz, PhD, Nothing to disclose