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Background: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. Objective: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. Methods: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. Results: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. Conclusions: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Abstract 1471 Introduction: A variety of prognostic molecular markers have been proposed for risk stratification of intermediate-risk AML, which includes patients with normal cytogenetics (CN) and patients with cytogenetic abnormalities other than the recurrent prognostically informative cytogenetic aberrations (CA). Acquired mutations of FLT3 ITD, NPM1, and CEBPA have well-established prognostic significance, and have been incorporated into consensus classification schemes. In addition, several gene expression markers (WT1, ERG, BAALC, and MN1) have been proposed as prognostic indicators, but with less extensive validation. Whether these new gene expression markers should be incorporated into proposed “molecular risk scores” for AML has not been well refined. In this study, we have determined the transcript levels of WT1, ERG, BAALC, and MN1 in AML patients, and correlated these quantitative levels with other validated clinical and laboratory prognostic indicators. Methods: Diagnostic blood or bone marrow samples were analyzed from 56 pre-treatment AML patients from our institution (48% male; median age 59). Metaphase cytogenetics showed 4 patients with inv(16), 4 with a monosomal karyotype, 25 with a CN karyotype and 23 with a CA karyotype (intermediate-risk cytogenetics). The expression levels of WT1, ERG, BAALC, and MN1 were quantified with TaqMan real-time PCR and normalized to ABL. The FLT3 ITD, NPM1, DNMT3A R882, CEBPA, and MLL PTD mutations were detected with either HRM, direct sequencing, and/or PCR fragment analysis. The WT1 SNP rs16754 was determined with a TaqMan genotyping assay. Results: In the 56 AML patients, the prevalence of the FLT3 ITD, NPM1, MLL-PTD, and DNMT3A R882 mutations were similar to previously reported values at 37%, 27%, 6%, and 13%, respectively. The frequency of patients carrying the minor allele of the WT1 SNP (combined heterozygous and homozygous) was 30%. Compared with their expression level in 29 normal control individuals, all four genes were significantly over-expressed in the AML patients, with mean RNA level differences of 1.98-log for WT1 (P0.9). Consistent with its role as a prognostic risk marker, ERG transcript levels were significantly lower in CN-AML patients with the low-risk NPM+/FLT3- genotype than in those with other genotypes (0.42 log expression difference; P Disclosures: No relevant conflicts of interest to declare.