1248. A Machine-Learning Approach to Predict the Cefazolin Inoculum Effect in Methicillin-Susceptible Staphylococcus aureus
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 
Published: December 2021

Abstract

Background
The cefazolin (Cz) inoculum effect (CzIE), defined as an increase in the Cz MIC to ≥16 µg/mL at high inoculum (107 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 ( &gt;90%), accuracy ( &gt;80%) and ROC-AUC values ( &gt;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