Utilizing technology to enhance laboratory-client interaction while encouraging best behavior.

As client interactions with veterinary diagnostic laboratories have evolved, so have client expectations: faster results, enhanced accessibility to cases, and more seamless data transfer from the laboratory database; all of these factors have encouraged the evolution of diagnostic laboratory systems. This evolution started with 24-h access to laboratory results via the web, yet data quality remained at the mercy of the person filling out the form. If bad (incomplete) information was flowing in, then the data coming out was equally bad (incomplete or inconsistent). By designing a web-based system integrated into our existing reporting platform, the Iowa State University Veterinary Diagnostic Laboratory (ISU-VDL) set out to improve the quality of submission data by including the premises identification number (PIN) and obtaining consistent location data, all while presenting to the client an easy-to-use interface. Efforts continued by incentivizing the use of this tool and client submission practices. As clients transitioned, data have become more complete, resulting in easier queries and an improved ability to leverage the diagnostic data. To further enhance the client experience, a streamlined daily reporting summary was designed to communicate laboratory results succinctly. The use of these web-based tools had a positive impact on the quality and consistency of the diagnostic data. As new ideas develop, the ISU-VDL strives to foster continuous improvement and positively impact the clients' experience.

Data standardization implementation and applications within and among diagnostic laboratories: integrating and monitoring enteric coronaviruses.

Every day, thousands of samples from diverse populations of animals are submitted to veterinary diagnostic laboratories (VDLs) for testing. Each VDL has its own laboratory information management system (LIMS), with processes and procedures to capture submission information, perform laboratory tests, define the boundaries of test results (i.e., positive or negative), and report results, in addition to internal business and accounting applications. Enormous quantities of data are accumulated and stored within VDL LIMSs. There is a need for platforms that allow VDLs to exchange and share portions of laboratory data using standardized, reliable, and sustainable information technology processes. Here we report concepts and applications for standardization and aggregation of data from swine submissions to multiple VDLs to detect and monitor porcine enteric coronaviruses by RT-PCR. Oral fluids, feces, and fecal swabs were the specimens submitted most frequently for enteric coronavirus testing. Statistical algorithms were used successfully to scan and monitor the overall and state-specific percentage of positive submissions. Major findings revealed a consistently recurrent seasonal pattern, with the highest percentage of positive submissions detected during December-February for porcine epidemic diarrhea virus, porcine deltacoronavirus, and transmissible gastroenteritis virus (TGEV). After 2014, very few submissions tested positive for TGEV. Monitoring VDL data proactively has the potential to signal and alert stakeholders early of significant changes from expected detection. We demonstrate the importance of, and applications for, data organized and aggregated by using LOINC and SNOMED CTs, as well as the use of customized messaging to allow inter-VDL exchange of information.

Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry.

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year's weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015-2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14-25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14-25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

Macroepidemiological aspects of porcine reproductive and respiratory syndrome virus detection by major United States veterinary diagnostic laboratories over time, age group, and specimen.

This project investigates the macroepidemiological aspects of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by veterinary diagnostic laboratories (VDLs) for the period 2007 through 2018. Standardized submission data and PRRSV real-time reverse-transcriptase polymerase chain reaction (RT-qPCR) test results from porcine samples were retrieved from four VDLs representing 95% of all swine samples tested in NAHLN laboratories in the US. Anonymized data were retrieved and organized at the case level using SAS (SAS® Version 9.4, SAS® Institute, Inc., Cary, NC) with the use of PROC DATA, PROC MERGE, and PROC SQL scripts. The final aggregated and anonymized dataset comprised of 547,873 unique cases was uploaded to Power Business Intelligence-Power BI® (Microsoft Corporation, Redmond, Washington) to construct dynamic charts. The number of cases tested for PRRSV doubled from 2010 to 2018, with that increase mainly driven by samples typically used for monitoring purposes rather than diagnosis of disease. Apparent seasonal trends for the frequency of PRRSV detection were consistently observed with a higher percentage of positive cases occurring during fall or winter months and lower during summer months, perhaps due to increased testing associated with well-known seasonal occurrence of swine respiratory disease. PRRSV type 2, also known as North American genotype, accounted for 94.76% of all positive cases and was distributed across the US. PRRSV type 1, also known as European genotype, was geographically restricted and accounted for 2.15% of all positive cases. Co-detection of both strains accounted for 3.09% of the positive cases. Both oral fluid and processing fluid samples, had a rapid increase in the number of submissions soon after they were described in 2008 and 2017, respectively, suggesting rapid adoption of these specimens by the US swine industry for PRRSV monitoring in swine populations. As part of this project, a bio-informatics tool defined as Swine Disease Reporting System (SDRS) was developed. This tool has real-time capability to inform the US swine industry on the macroepidemiological aspects of PRRSV detection, and is easily adaptable for other analytes relevant to the swine industry.