Development and Validation of a Model to Predict Growth of Potentially Antibiotic-Resistant Gram-Negative Bacilli in Critically Ill Children With Suspected Infection

Abstract

BackgroundRisk-based guidelines aid empiric antibiotic selection for critically ill adults with suspected infection with Gram-negative bacilli with high potential for antibiotic resistance (termed high-risk GNRs). Neither evidence-based guidelines for empiric antibiotic selection nor validated risk factors predicting high-risk GNR growth exist for critically ill children. We developed and validated a model for predicting high-risk GNR growth in critically ill children with suspected infection.MethodsThis is a retrospective cohort study involving 2 pediatric cohorts admitted to a pediatric intensive care unit (ICU) with suspected infection. We developed a risk model predicting growth of high-risk GNRs using multivariable regression analysis in 1 cohort and validated it in a separate cohort.ResultsIn our derivation cohort (556 infectious episodes involving 489 patients), we identified the following independent predictors of high-risk GNR growth: hospitalization >48 hours before suspected infection, hospitalization within the past 4 weeks, recent systemic antibiotics, chronic lung disease, residence in a chronic care facility, and prior high-risk GNR growth. The model sensitivity was 96%, the specificity was 48%, performance using the Brier score was good, and the area under the receiver operator characteristic curve (AUROC) was 0.722, indicating good model performance. In our validation cohort (525 episodes in 447 patients), model performance was similar (AUROC, 0.733), indicating stable model performance.ConclusionsOur model predicting high-risk GNR growth in critically ill children demonstrates the high sensitivity needed for ICU antibiotic decisions, good overall predictive capability, and stable performance in 2 separate cohorts. This model could be used to develop risk-based empiric antibiotic guidelines for the pediatric ICU.

Antimicrobial stewardship in transplant patients

“…Higher rates of multidrugresistant organisms (MDRO) recovery have been observed in transplant unit-specific antibiograms and urine-specific antibiograms in kidney transplant recipients and may vary significantly from overall hospital rates [28]. Newer methods of prediction using modeling and EMR data are under investigation and should be coupled with the characterization of dynamic patient-level changes in the MDRO colonization-infection-clearance continuum posttransplantation [29][30][31].…”