Though more often associated with persistent diarrhea, protozoal gastroenteritis may also present as an acute illness. Suspicion for gastrointestinal protozoal infection is historically based on travel or recreational water exposure and duration of illness, which can delay diagnosis and effective treatment. The use of multiplex PCR panels have the potential to improve diagnosis of protozoal illness. To better understand when testing for protozoa is warranted, we examined predictors of protozoal gastroenteritis within a prospective multicenter study of US children presenting to emergency departments with acute gastroenteritis (AGE).
The analysis utilized data from the IMPACT study, a prospective trial of the clinical impact of a multiplex PCR panel for detecting GI pathogens in AGE in 5 pediatric hospitals across the US. From this data, we evaluated 72 potential predictors, including patient demographics, medical histories, exposures, symptoms, and vital signs. Using Random Forest Algorithm, 10 variables were selected based on variable importance measure. These were then entered into a Stepwise logistic regression model.
Out of 962 patients there were 41 (4.3%) patients with protozoal detections, including 18 for Giardia and 24 for Cryptosporidium. Of these, 21 (51%) were male and the median age was 4.3 years old. Detection rates varied by site with 76% (31) of cases at 2 Midwestern sites. In 23 (56%) of cases protozoa was the sole pathogen detected. Logistic regression modeling of the top 10 variables identified by Random Forest showed the strongest predictor was living in a household with a child 5 years old or younger, followed by study site and younger age (Table). Notably, travel, recreational water exposure, and duration of diarrhea were not in the top 10 variables identified.
Our analysis suggests that age and household exposure predict higher likelihood of protozoal infection in children with AGE. Classic epidemiologic exposures including travel and recreational water exposure were not predictive. These data could improve appropriate test selection. Future studies are still needed for external validation of this model.
B. Haaland, None
A. Pavia, BioFire Dx: Investigator , Research support .
D. Leung, None