Automated system to identify Clostridium difficile infection among hospitalised patients.

The purpose of this study was to assess whether data on stool frequency collected electronically could identify patients at high risk for Clostridium difficile infection (CDI). All patients with reports of diarrhoea were assessed prospectively for number of stools per day and number of diarrhoea days. C. difficile testing was requested independently from study investigators. Number of days with diarrhoea and maximum number of unformed stools was assessed as a CDI predictor. A total of 605 patients were identified with active diarrhoea of whom 64 (10.6%) were diagnosed with CDI. In univariate analysis, the maximum number of stools and number of diarrhoea days was associated with increased risk of CDI. Compared to patients with three diarrhoea stools per day (CDI incidence: 6.3%), CDI increased to 13.4% in patients with four or more diarrhoea stools per day [odds ratio (OR): 2.3; 95% confidence interval (CI): 1.3-4.2; P=0.0054]. Compared to patients with one day of diarrhoea (CDI incidence: 6.3%), CDI increased to 17.4% in patients with two diarrhoea days (OR: 3.1; 95% CI: 1.7-5.6) and to 27.1% in patients with three or more diarrhoea days (OR: 5.5; 95% CI: 2.6-11.7). These results were validated using logistic regression with number of days with diarrhoea identified as the most important predictor. Using an electronic data capture technique, number of days of diarrhoea and maximum number of diarrhoea stools in a 24h time period were able to identify a patient population at high risk for CDI.