Internationally Acquired Severe Systemic Infections in Febrile Pediatric Travelers Presenting to the Emergency Department.

OBJECTIVES: Most children in the United States who visit the emergency department (ED) with fever have minor illnesses not requiring treatment or hospitalization. However, when a child has recently immigrated or traveled abroad, internationally acquired severe systemic infections (ISSIs) must be considered. We sought to describe children who have traveled internationally and present to the ED with a complaint of fever and to determine risk factors associated with ISSIs in these patients. METHODS: We conducted a retrospective study of children younger than 18 years who presented to 2 pediatric EDs in Bronx, NY (June 2007 to May 2017). Patients were included if they had both fever within 24 hours and international travel within 30 days. We compared groups using bivariate analyses and created a prediction model for ISSIs using multivariable logistic regression. RESULTS: Of the 353 children included, 44 (12%) had ISSI: 25 (57%), malaria; 6 (14%), dengue; and 13 (30%), bacteremia. Eight (18%) of those with ISSI presented with fever to another medical provider in the week prior but did not receive bloodwork. Four variables were independently associated with ISSIs: headache (odds ratio [OR], 21.7; 95% confidence interval [CI], 6.8-69.3), travel to Africa or Asia (OR, 18.8; 95% CI, 4.8-73.2), platelets of 150,000/µL or less (OR, 15.1; 95% CI, 4.7-48.6), and alanine aminotransferase level of 30 IU/L or greater (OR, 8.9; 95% CI, 3.1-25.3). CONCLUSIONS: Children who travel internationally and present with fever upon return are at substantial risk for developing ISSIs. The diagnosis of ISSIs is often overlooked, but certain risk factors have the potential to aid clinicians.

GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking): A methodology to rapidly leverage whole genome sequencing of bacterial isolates for clinical identification.

Whole genome sequencing (WGS) of clinical bacterial isolates has the potential to transform the fields of diagnostics and public health. To realize this potential, bioinformatic software that reports identification results needs to be developed that meets the quality standards of a diagnostic test. We developed GAMBIT (Genomic Approximation Method for Bacterial Identification and Tracking) using k-mer based strategies for identification of bacteria based on WGS reads. GAMBIT incorporates this algorithm with a highly curated searchable database of 48,224 genomes. Herein, we describe validation of the scoring methodology, parameter robustness, establishment of confidence thresholds and the curation of the reference database. We assessed GAMBIT by way of validation studies when it was deployed as a laboratory-developed test in two public health laboratories. This method greatly reduces or eliminates false identifications which are often detrimental in a clinical setting.