BACKGROUND: Clinicians and travelers often have limited tools to differentiate bacterial from non-bacterial causes of travelers' diarrhea (TD). Development of a clinical prediction rule assessing the etiology of TD may help identify episodes of bacterial diarrhea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhea among clinical, demographic, and weather variables, as well as to develop and cross-validate a parsimonious predictive model. METHODS: We collected de-identified clinical data from 457 international travelers with acute diarrhea presenting to two healthcare centers in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal etiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhea. RESULTS: We identified 195 cases of bacterial etiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite, and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected etiologies were average location-specific environmental temperature and RBC on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an AUC of 0.73 using 3 variables with calibration intercept -0.01 (SD 0.31) and slope 0.95 (SD 0.36). CONCLUSIONS: We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of etiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.
Weather variables as important clinical predictors of bacterial diarrhea among international travelers