Evaluating the utility of syndromic surveillance algorithms for screening to detect potentially clonal hospital infection outbreaks.

OBJECTIVE: The authors evaluated algorithms commonly used in syndromic surveillance for use as screening tools to detect potentially clonal outbreaks for review by infection control practitioners.
DESIGN: Study phase 1 applied four aberrancy detection algorithms (CUSUM, EWMA, space-time scan statistic, and WSARE) to retrospective microbiologic culture data, producing a list of past candidate outbreak clusters. In phase 2, four infectious disease physicians categorized the phase 1 algorithm-identified clusters to ascertain algorithm performance. In phase 3, project members combined the algorithms to create a unified screening system and conducted a retrospective pilot evaluation. MEASUREMENTS: The study calculated recall and precision for each algorithm, and created precision-recall curves for various methods of combining the algorithms into a unified screening tool.
RESULTS: Individual algorithm recall and precision ranged from 0.21 to 0.31 and from 0.053 to 0.29, respectively. Few candidate outbreak clusters were identified by more than one algorithm. The best method of combining the algorithms yielded an area under the precision-recall curve of 0.553. The phase 3 combined system detected all infection control-confirmed outbreaks during the retrospective evaluation period. LIMITATIONS: Lack of phase 2 reviewers' agreement indicates that subjective expert review was an imperfect gold standard. Less conservative filtering of culture results and alternate parameter selection for each algorithm might have improved algorithm performance.
CONCLUSION: Hospital outbreak detection presents different challenges than traditional syndromic surveillance. Nevertheless, algorithms developed for syndromic surveillance have potential to form the basis of a combined system that might perform clinically useful hospital outbreak screening.