Impact of Rapid Molecular Multiplex Gastrointestinal Pathogen Testing in Management of Children during a Shigella Outbreak

Fecal culture for isolation and identification of Shigella may take days. The BioFire FilmArray Gastrointestinal (GI) panel (bioMérieux, France) is a PCR-based assay that detects enteric pathogens including Shigella/enteroinvasive Escherichia coli (EIEC) in about an hour. The aim of this study was to evaluate the impact of GI panel detection of Shigella in a pediatric emergency department (ED) during an outbreak. Stool samples from children with acute gastroenteritis were tested by the GI panel. Test results were either withheld in preintervention (PRE) or reported to clinicians/families in the postintervention (POST) period. The impact of the GI panel testing on patient management and outcomes was measured. Shigella/EIEC was identified by the GI panel in the PRE (n = 30) and POST (n = 21) phase. The GI panel detected more Shigella infections than did culture; six of 31 (19.4%) Shigella GI panel-positive patients who also had stool cultures were missed by culture. Azithromycin therapy was prescribed for 20% of subjects in the PRE phase and 71.4% of subjects in the POST phase (P < 0.001). Time from the clinical encounter until starting azithromycin therapy was shorter in the POST phase (n = 9), 8.25 h (range, 6.37 to 52.37 h), than in the PRE phase (n = 1), 72 h. Six subjects in the PRE phase visited additional providers compared with one in the POST phase. Prompt diagnosis of shigellosis with the GI panel may provide the opportunity for prompt antimicrobial therapy and avoid additional visits to providers due to early definitive diagnosis. Prompt diagnosis of Shigella at an ED visit may optimize patient management and reduce transmission.

Toward snowpack runoff decision support

Rain-on-snow (ROS) events are commonly linked to large historic floods in the United States. Projected increases in the frequency and magnitude of ROS multiply existing uncertainties and risks in operational decision making. Here, we introduce a framework for quality-controlling hourly snow water content, snow depth, precipitation, and temperature data to guide the development of an empirically based snowpack runoff decision support framework at the Central Sierra Snow Laboratory for water years 2006-2019. This framework considers the potential for terrestrial water input from the snowpack through decision tree classification of rain-on-snow and warm day melt events to aid in pattern recognition of prominent weather and antecedent snowpack conditions capable of producing snowpack runoff. Our work demonstrates how (1) present weather and (2) antecedent snowpack risk can be "learned" from hourly data to support eventual development of basin-specific snowpack runoff decision support systems aimed at providing real-time guidance for water resource management.