Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis

PLoS Comput Biol. 2023 Sep 6;19(9):e1011424. doi: 10.1371/journal.pcbi.1011424. eCollection 2023 Sep.

Abstract

Chronic Pseudomonas aeruginosa (Pa) lung infections are the leading cause of mortality among cystic fibrosis (CF) patients; therefore, the eradication of new-onset Pa lung infections is an important therapeutic goal that can have long-term health benefits. The use of early antibiotic eradication therapy (AET) has been shown to clear the majority of new-onset Pa infections, and it is hoped that identifying the underlying basis for AET failure will further improve treatment outcomes. Here we generated machine learning models to predict AET outcomes based on pathogen genomic data. We used a nested cross validation design, population structure control, and recursive feature selection to improve model performance and showed that incorporating population structure control was crucial for improving model interpretation and generalizability. Our best model, controlling for population structure and using only 30 recursively selected features, had an area under the curve of 0.87 for a holdout test dataset. The top-ranked features were generally associated with motility, adhesion, and biofilm formation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-Bacterial Agents / therapeutic use
  • Cell Aggregation
  • Child
  • Cystic Fibrosis* / complications
  • Cystic Fibrosis* / drug therapy
  • Humans
  • Lung
  • Pseudomonas Infections* / complications
  • Pseudomonas Infections* / drug therapy
  • Pseudomonas aeruginosa

Substances

  • Anti-Bacterial Agents

Grants and funding

The study was supported by a Collaborative Health Research Project grant from the Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada (CP-151952) to DSG, YJW, YCWY, and DMH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.