Abstract
BACKGROUNDGastroenteritis (GE) is a nonspecific term for various pathologic states of the gastrointestinal tract. Infectious agents usually cause acute gastroenteritis. At present, there are no robust decision-making rules that predict bacterial GE and hence dictate when to start antibiotics in patients presenting with acute GE to the ED. We aim to define a clinical prediction rule to diagnose bacterial gastroenteritis requiring empirical antibiotics in an emergency department setting. METHODSA 2-year retrospective case review was performed on all cases from July 2015 to June 2017 that presented acutely with infectious GE symptoms to the Emergency Department and then had stool cultures performed. The clinical parameters analysed included patient co-morbid conditions, physical examination findings, historical markers, point of care tests and other laboratory work. We then used multivariate logistic regression analysis on each group (Bacterial culture-positive GE and Bacterial culture-negative GE) to elucidate clinical criteria with the highest yield for predicting BGE. RESULTS756 patients with a mean age of 52 years, 52% of whom were female, and 48% male, were recruited into the study. Based on the data from these patients, we suggest using a scoring system to delineate the need for empirical antibiotics in patients with suspected bacterial GE based on six clinical and laboratory variables. A score 0-3 points on the suggests low risk (5.8%) of bacterial GE. A score of 4-5 points confers an intermediate risk of 28.5% and a score of 6-8 points confers a high risk of 66.7%. A cut-off of >5 points may be used to predict culture positive BGE with a 75% sensitivity and 75% specificity. The AUROC for the scoring system (range 0-8) is 0.812+0.016 (95% CI: 0.780-0.843) p-value <0.001. CONCLUSIONWhile this is a pilot study which will require further validation with a larger sample size, our proposed decision-making rule will potentially serve to improve diagnosis of BGE, reduce unnecessary prescribing of antibiotics which will in turn reduce antibiotic associated adverse events and save costs worldwide.