A Decision Tree Using Patient Characteristics to Predict Resistance to Commonly Used Broad-Spectrum Antibiotics in Children With Gram-Negative Bloodstream Infections

Abstract

Background As rates of multidrug-resistant gram-negative infections rise, it is critical to recognize children at high risk of bloodstream infections with organisms resistant to commonly used empiric broad-spectrum antibiotics. The objective of the current study was to develop a user-friendly clinical decision aid to predict the risk of resistance to commonly prescribed broad-spectrum empiric antibiotics for children with gram-negative bloodstream infections. Methods This was a longitudinal retrospective cohort study of children with gram-negative bacteria cared for at a tertiary care pediatric hospital from June 2009 to June 2015. The primary outcome was a bloodstream infection due to bacteria resistant to broad-spectrum antibiotics (ie, cefepime, piperacillin-tazobactam, meropenem, or imipenem-cilastatin). Recursive partitioning was used to develop the decision tree. Results Of 689 episodes of gram-negative bloodstream infections included, 31% were resistant to broad-spectrum antibiotics. The decision tree stratified patients into high- or low-risk groups based on prior carbapenem treatment, a previous culture with a broad-spectrum antibiotic resistant gram-negative organism in the preceding 6 months, intestinal transplantation, age ≥3 years, and ≥7 prior episodes of gram-negative bloodstream infections. The sensitivity for classifying high-risk patients was 46%, and the specificity was 91%. Conclusion A decision tree offers a novel approach to individualize patients’ risk of gram-negative bloodstream infections resistant to broad-spectrum antibiotics, distinguishing children who may warrant even broader antibiotic therapy (eg, combination therapy, newer β-lactam agents) from those for whom standard empiric antibiotic therapy is appropriate. The constructed tree needs to be validated more widely before incorporation into clinical practice.

Outcomes in Pediatric Central Line–associated Bloodstream Infections Treated With Antimicrobial Locks: A 14-Year Retrospective Analysis

“…This has previously been identified as an important risk factor in developing antimicrobial resistance in Gram-negative BSIs. 18 This study is subject to important limitations. As a retrospective, observational, single center study, children met inclusion criteria and were allocated to treatment types nonrandomly.…”

Using Machine Learning to Predict Antimicrobial Resistance―A Literature Review

“…Moreover, previous antibiotic susceptibility results provide potent information to predict resistance to existing infections [ 51 ]. Sick-Samuels et al constructed a decision tree by using recursive partitioning to predict the risk of broad-spectrum antibiotic (BSA) resistance in a cohort of septic pediatric patients based on five distinctive risk factors [ 52 ]. Nearly half of high-risk BSA-resistant episodes were incorrectly categorized as low-risk episodes, and 9% were incorrectly categorized as high-risk episodes.…”

<p>Optimal Management of Complicated Infections in the Pediatric Patient: The Role and Utility of Ceftazidime/Avibactam</p>

“…9,13 After the development of an algorithm in adult population, 14 a decision tree illustrating the risk of antimicrobial resistance among children with GNB BSI based on individual patient risk factors has been recently proposed for pediatric population, but it needs to be validated more widely before incorporation into clinical practice. 15…”