Automated collection of pathogen-specific diagnostic data for real-time syndromic epidemiological studies

Lindsay Meyers; Christine C. Ginocchio; Aimie N. Faucett; Frederick S. Nolte; Per H. Gesteland; Amy Leber; Diane Janowiak; Virginia Donovan; Jennifer Dien Bard; Silvia Spitzer; Kathleen A. Stellrecht; Hossein Salimnia; Rangaraj Selvarangan; Stefan Juretschko; Judy A. Daly; Jeremy C. Wallentine; Kristy Lindsey; Franklin Moore; Sharon L. Reed; Maria Aguero-Rosenfeld; Paul D. Fey; Gregory A. Storch; Steve J. Melnick; Christine C. Robinson; Jennifer F. Meredith; Camille V. Cook; Robert K. Nelson; Jay D. Jones; Samuel V. Scarpino; Benjamin M. Althouse; Kirk M. Ririe; Bradley A. Malin; Mark A. Poritz

The data in Figure 2 are similar to previous demonstrations of the seasonality associated with different respiratory viruses [63] [64] [65] [66] . What is novel is that this data is generated automatically, on site, and in close to real-time compared to other surveillance systems. Greater than 98% of the test results are exported to the Trend database within 24 hours of being generated. As part of the de-identification protocol, sequential FilmArray RP tests of the same type are put into the same 330 time bin. This has the effect that test results are exported faster during periods of peak use such as during the peak of the respiratory season or during an outbreak. Trend should be instrumental at a local level to determine the start of respiratory season; many hospitals make significant changes to their operations based on this event however, at present, data collection to track the respiratory season is often slow and manual, or semi-automated at best. 335 The key to implementing (Figure 3) , supporting the validity and utility of the Trend data. 380 A second source of concern in the Trend data set is a consequence of removal of sample identification such that we cannot directly determine whether the sample was from a patient or was a non-clinical sample (verification test, QC or PT) and should be removed from further epidemiological analysis. We estimate that non-patient testing makes up approximately 1.8% of the total FilmArray RP tests. Automated detection algorithms remove 3.5% of the total RP tests, 385 including approximately half of the non-clinical samples. With the exception of the four positive tests, the clinical samples removed by filtering should be a random sampling of all patient tests.