Clinical Registries Could Improve Influenza Like Illness and COVID-19 Surveillance

Capacity for tracking COVID-19 prevalence patterns is hampered by insufficient data, particularly from rural and small communities. The PRIME Registry holds data for 5.4 million patients in 47 states who made 638,983 Influenza-Like Illness (ILI) visits in 2019, mirroring CDC’s ILINet temporal patterns but with higher volume and greater rural penetration. Clinical data registries are viable partners that could fill gaps for epidemic sentinel functions and have rich patient data which may identify factors predictive of COVID-19 morbidity and mortality.https://deepblue.lib.umich.edu/bitstream/2027.42/154853/1/ILI-PRIME_AnnalsFamMed_FINAL.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154853/2/RehkopfILIFig1.docxhttps://deepblue.lib.umich.edu/bitstream/2027.42/154853/3/RehkopfILIFig2.docxDescription of ILI-PRIME_AnnalsFamMed_FINAL.pdf : Main ArticleDescription of RehkopfILIFig1.docx : Figure 1Description of RehkopfILIFig2.docx : Figure

American Family Cohort, a data resource description

This manuscript is a research resource description and presents a large and novel Electronic Health Records (EHR) data resource, American Family Cohort (AFC). The AFC data is derived from Centers for Medicare and Medicaid Services (CMS) certified American Board of Family Medicine (ABFM) PRIME registry. The PRIME registry is the largest national Qualified Clinical Data Registry (QCDR) for Primary Care. The data is converted to a popular common data model, the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The resource presents approximately 90 million encounters for 7.5 million patients. All 100% of the patients present age, gender, and address information, and 73% report race. Nealy 93% of patients have lab data in LOINC, 86% have medication data in RxNorm, 93% have diagnosis in SNOWMED and ICD, 81% have procedures in HCPCS or CPT, and 61% have insurance information. The richness, breadth, and diversity of this research accessible and research ready data is expected to accelerate observational studies in many diverse areas. We expect this resource to facilitate research in many years to come

The gut microbiome-Does stool represent right?

: Many stool-based gut microbiome studies have highlighted the importance of the microbiome. However, we hypothesized that stool is a poor proxy for the inner-colonic microbiome and that studying stool samples may be inadequate to capture the true inner-colonic microbiome. To test this hypothesis, we conducted prospective clinical studies with up to 20 patients undergoing an FDA-cleared gravity-fed colonic lavage without oral purgative pre-consumption. The objective of this study was to present the analysis of inner-colonic microbiota obtained non-invasively during the lavage and how these results differ from stool samples. The inner-colonic samples represented the descending, transverse, and ascending colon. All samples were analyzed for 16S rRNA and shotgun metagenomic sequences. The taxonomic, phylogenetic, and biosynthetic gene cluster analyses showed a distinctive biogeographic gradient and revealed differences between the sample types, especially in the proximal colon. The high percentage of unique information found only in the inner-colonic effluent highlights the importance of these samples and likewise the importance of collecting them using a method that can preserve these distinctive signatures. We proposed that these samples are imperative for developing future biomarkers, targeted therapeutics, and personalized medicine