Validation of β-lactam minimum inhibitory concentration predictions for pneumococcal isolates with newly encountered penicillin binding protein (PBP) sequences
Yuan Li,
Benjamin J. Metcalf,
Sopio Chochua,
Zhongya Li,
Robert E. Gertz,
Hollis Walker,
Paulina A. Hawkins,
Theresa Tran,
Lesley McGee,
Bernard W. Beall,
on behalf of the Active Bacterial Core surveillance team
Affiliations
Yuan Li
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Benjamin J. Metcalf
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Sopio Chochua
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Zhongya Li
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Robert E. Gertz
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Hollis Walker
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Paulina A. Hawkins
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Theresa Tran
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Lesley McGee
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
Bernard W. Beall
Respiratory Diseases Branch, Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, U.S. Department of Health and Human Services
on behalf of the Active Bacterial Core surveillance team
Abstract Background Genomic sequence-based deduction of antibiotic minimum inhibitory concentration (MIC) has great potential to enhance the speed and sensitivity of antimicrobial susceptibility testing. We previously developed a penicillin-binding protein (PBP) typing system and two methods (Random Forest (RF) and Mode MIC (MM)) that accurately predicted β-lactam MICs for pneumococcal isolates carrying a characterized PBP sequence type (phenotypic β-lactam MICs known for at least one isolate of this PBP type). This study evaluates the prediction performance for previously uncharacterized (new) PBP types and the probability of encountering new PBP types, both of which impact the overall prediction accuracy. Results The MM and RF methods were used to predict MICs of 4309 previously reported pneumococcal isolates in 2 datasets and the results were compared to the known broth microdilution MICs to 6 β-lactams. Based on a method that specifically evaluated predictions for new PBP types, the RF results were more accurate than MM results for new PBP types and showed percent essential agreement (MICs agree within ±1 dilution) >97%, percent category agreement (interpretive results agree) >93%, major discrepancy (sensitive isolate predicted as resistant) rate < 1.2%, and very major discrepancy (resistant isolate predicted as sensitive) rate < 1.4% for all 6 β-lactams. The identification of new PBP types over time was well approximated by a diminishingly increasing curve (Pearson’s r = 0.99) and minimally impacted overall MIC prediction performance. Conclusions MIC prediction using the RF method could be an accurate alternative of phenotypic susceptibility testing even in the presence of previously uncharacterized PBP types.
