When dealing with multidrug-resistant pathogens, every minute counts and any way to speed up the prediction of antimicrobial resistance (AMR) is key to reducing morbidity and mortality.
Infections with multidrug-resistant (MDR) Pseudomonas aeruginosa
are increasing worldwide, and the organism is especially prevalent in health care-associated settings. But rapid AMR predictions could help with providing optimal care to patients.
Investigators in Texas have developed a new algorithm, variant mapping and prediction of AMR (VAMPr) using publicly available whole genome sequencing and antibiotic susceptibility testing (AST) data. In research presented at IDWeek 2019
, the research team details the creation of machine learning prediction models.
To test it, investigators sequenced a validation cohort of contemporary P aeruginosa
isolates and performed AST. They then calculated an accuracy score around predicting AMR for various P aeruginosa-
VAMPr comprised a total of 3393 bacterial isolates (83 P aeruginosa
isolates included) from 9 species, which contained AST data for 29 antibiotics.
“14,615 variant genotypes were identified within the dataset and 93 association and prediction models were built,” investigators explained in the abstract. “120 [P aeruginosa
] bloodstream isolates from cancer patients were included for analysis in the validation cohort. ~15% of isolates were carbapenem resistant and ~20% were quinolone resistant.”
Machine learning prediction accuracies ranged from 75.6% (P aeruginosa
and ceftazidime; 90/119 correctly predicted) to 98.1% (P aeruginosa
and amikacin; 105/107 correctly predicted) for drug-isolate combinations where > 100 isolates were available. Machine learning also correctly identified known variants that strongly predicted resistance to various classes of antibiotics.
“Machine learning predicted AMR in P aeruginosa
across a number of antibiotics with high accuracy,” investigators concluded. “Given the genomic heterogeneity of [P aeruginosa
], increased genomic data for this pathogen will aid in further improving prediction accuracy across all antibiotic classes.”
The study, Machine Learning Approaches to Predicting Resistance in Pseudomonas aeruginosa
, was presented in an oral abstract session on Friday, October 4, 2019, at IDWeek in Washington, DC.
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