Performance of OC-Auto Micro 80 Fecal Immunochemical Test in an Integrated Academic-Community Health System.

GOALS: We aimed to determine the performance of the OC-Auto Micro 80 fecal immunochemical test (FIT) in an average-risk population receiving care in an integrated, academic-community health system. BACKGROUND: The FIT is the most used colorectal cancer (CRC) screening test worldwide. However, many Food and Drug Administration-cleared FIT products have not been evaluated in clinical settings. STUDY: We performed a retrospective cohort study of patients (50 to 75 y old) in the University of Washington Medicine health care system who were screened for CRC by OC-Auto Micro 80 FIT between March 2016 and September 2021. We used electronic health records to extract patient-level and clinic-level factors, FIT use, colonoscopy, and pathology findings. The primary outcomes were the FIT positivity rate and neoplasms detected at colonoscopy. Secondary outcomes were FIT positivity by sex and safety-net versus non-safety-net clinical settings. RESULTS: We identified 39,984 FITs completed by 26,384 patients; 2411 (6.0%) had a positive FIT result (>100 ng/mL of hemoglobin in buffer), and 1246 (51.7%) completed a follow-up colonoscopy. The FIT positive rate was 7.0% in men and 5.2% in women (P <0.01). Among those who completed a colonoscopy after an abnormal FIT result, the positive predictive value for CRC, advanced adenoma, and advanced neoplasia was 3.0%, 20.9%, and 23.9%, respectively. CONCLUSIONS: In a retrospective analysis of a large heterogeneous population, the OC-Auto Micro 80 FIT for CRC screening demonstrated a positivity rate of 6.0% and a positive predictive value for CRC of 3.0%.

A web application to support the coordination of reflexive, interpretative toxicology testing.

Background: Reflexive laboratory testing workflows can improve the assessment of patients receiving pain medications chronically, but complex workflows requiring pathologist input and interpretation may not be well-supported by traditional laboratory information systems. In this work, we describe the development of a web application that improves the efficiency of pathologists and laboratory staff in delivering actionable toxicology results. Method: Before designing the application, we set out to understand the entire workflow including the laboratory workflow and pathologist review. Additionally, we gathered requirements and specifications from stakeholders. Finally, to assess the performance of the implementation of the application, we surveyed stakeholders and documented the approximate amount of time that is required in each step of the workflow. Results: A web-based application was chosen for the ease of access for users. Relevant clinical data was routinely received and displayed in the application. The workflows in the laboratory and during the interpretation process served as the basis of the user interface. With the addition of auto-filing software, the return on investment was significant. The laboratory saved the equivalent of one full-time employee in time by automating file management and result entry. Discussion: Implementation of a purpose-built application to support reflex and interpretation workflows in a clinical pathology practice has led to a significant improvement in laboratory efficiency. Custom- and purpose-built applications can help reduce staff burnout, reduce transcription errors, and allow staff to focus on more critical issues around quality.

Host-pathogen dynamics in longitudinal clinical specimens from patients with COVID-19.

Rapid dissemination of SARS-CoV-2 sequencing data to public repositories has enabled widespread study of viral genomes, but studies of longitudinal specimens from infected persons are relatively limited. Analysis of longitudinal specimens enables understanding of how host immune pressures drive viral evolution in vivo. Here we performed sequencing of 49 longitudinal SARS-CoV-2-positive samples from 20 patients in Washington State collected between March and September of 2020. Viral loads declined over time with an average increase in RT-QPCR cycle threshold of 0.87 per day. We found that there was negligible change in SARS-CoV-2 consensus sequences over time, but identified a number of nonsynonymous variants at low frequencies across the genome. We observed enrichment for a relatively small number of these variants, all of which are now seen in consensus genomes across the globe at low prevalence. In one patient, we saw rapid emergence of various low-level deletion variants at the N-terminal domain of the spike glycoprotein, some of which have previously been shown to be associated with reduced neutralization potency from sera. In a subset of samples that were sequenced using metagenomic methods, differential gene expression analysis showed a downregulation of cytoskeletal genes that was consistent with a loss of ciliated epithelium during infection and recovery. We also identified co-occurrence of bacterial species in samples from multiple hospitalized individuals. These results demonstrate that the intrahost genetic composition of SARS-CoV-2 is dynamic during the course of COVID-19, and highlight the need for continued surveillance and deep sequencing of minor variants.

Phylogenetic estimates of SARS-CoV-2 introductions into Washington State.

BACKGROUND: The first confirmed case of SARS-CoV-2 in North America was identified in Washington state on January 21, 2020. We aimed to quantify the number and temporal trends of out-of-state introductions of SARS-CoV-2 into Washington. METHODS: We conducted a phylogenetic analysis of 11,422 publicly available whole genome SARS-CoV-2 sequences from GISAID sampled between December 2019 and September 2020. We used maximum parsimony ancestral state reconstruction methods on time-calibrated phylogenies to enumerate introductions/exports, their likely geographic source (e.g. US, non-US, and between eastern and western Washington), and estimated date of introduction. To incorporate phylogenetic uncertainty into our estimates, we conducted 5,000 replicate analyses by generating 25 random time-stratified samples of non-Washington reference sequences, 20 random polytomy resolutions, and 10 random resolutions of the reconstructed ancestral state. RESULTS: We estimated a minimum 287 separate introductions (median, range 244-320) into Washington and 204 exported lineages (range 188-227) of SARS-CoV-2 out of Washington. Introductions began in mid-January and peaked on March 29, 2020. Lineages with the Spike D614G variant accounted for the majority (88%) of introductions. Overall, 61% (range 55-65%) of introductions into Washington likely originated from a source elsewhere within the US, while the remaining 39% (range 35-45%) likely originated from outside of the US. Intra-state transmission accounted for 65% and 28% of introductions into eastern and western Washington, respectively. CONCLUSIONS: There is phylogenetic evidence that the SARS-CoV-2 epidemic in Washington is continually seeded by a large number of introductions, and that there was significant inter- and intra-state transmission. Due to incomplete sampling our data underestimate the true number of introductions.

Phylogenetic estimates of SARS-CoV-2 introductions into Washington State.

BACKGROUND: The first confirmed case of SARS-CoV-2 in North America was identified in Washington state on January 21, 2020. We aimed to quantify the number and temporal trends of out-of-state introductions of SARS-CoV-2 into Washington. METHODS: We conducted a molecular epidemiologic analysis of 11,422 publicly available whole genome SARS-CoV-2 sequences from GISAID sampled between December 2019 and September 2020. We used maximum parsimony ancestral state reconstruction methods on time-calibrated phylogenies to enumerate introductions/exports, their likely geographic source (US, non-US, and between eastern and western Washington), and estimated date of introduction. To incorporate phylogenetic uncertainty into our estimates, we conducted 5,000 replicate analyses by generating 25 random time-stratified samples of non-Washington reference sequences, 20 random polytomy resolutions, and 10 random resolutions of the reconstructed ancestral state. FINDINGS: We estimated a minimum 287 introductions (range 244-320) into Washington and 204 exported lineages (range 188-227) of SARS-CoV-2 out of Washington. Introductions began in mid-January and peaked on March 29, 2020. Lineages with the Spike D614G variant accounted for the majority (88%) of introductions. Overall, 61% (range 55-65%) of introductions into Washington likely originated from a source elsewhere within the US, while the remaining 39% (range 35-45%) likely originated from outside of the US. Intra-state transmission accounted for 65% and 28% of introductions into eastern and western Washington, respectively. INTERPRETATION: The SARS-CoV-2 epidemic in Washington was continually seeded by a large number of introductions. Our findings highlight the importance of genomic surveillance to monitor for emerging variants due to high levels of inter- and intra-state transmission of SARS-CoV-2. FUNDING SOURCE: None.

Pooling of SARS-CoV-2 samples to increase molecular testing throughput.

BACKGROUND: SARS-CoV-2 testing demand has outpaced its supply. Pooling samples for lower risk populations has the potential to accommodate increased demand for SARS-CoV-2 molecular testing. OBJECTIVE: To evaluate the sensitivity, specificity, and reproducibility of 4-way pooling of SARS-CoV-2 specimens for high-throughput RT-PCR. STUDY DESIGN: Individual samples were pooled 1:4 through automated liquid handling, extracted, and assayed by our emergency use authorized CDC-based RT-PCR laboratory developed test. Positive samples were serially diluted and theoretical and empirical PCR cycle thresholds were evaluated. Thirty-two distinct positive samples were pooled into negative specimens and individual CTs were compared to pooled CTs. Low positive samples were repeated for reproducibility and 32 four-way pools of negative specimens were assayed to determine specificity. RESULTS: Four-way pooling was associated with a loss of sensitivity of 1.7 and 2.0 CTs for our N1 and N2 targets, respectively. Pooling correctly identified SARS-CoV-2 in 94 % (n = 30/32) of samples tested. The two low positive specimens (neat CT > 35) not detected by pooling were individually repeated and detected 75 % (n=6/8) and 37.5 % (n = 3/8) of the time, respectively. All specimens individually determined negative were also negative by pooling. CONCLUSION: We report that 1:4 pooling of samples is specific and associated with an expected 2 CT loss in analytical sensitivity. Instead of running each sample individually, pooling of four samples will allow for a greater throughput and conserve scarce reagents.

Global hotspots and correlates of emerging zoonotic diseases.

Zoonoses originating from wildlife represent a significant threat to global health, security and economic growth, and combatting their emergence is a public health priority. However, our understanding of the mechanisms underlying their emergence remains rudimentary. Here we update a global database of emerging infectious disease (EID) events, create a novel measure of reporting effort, and fit boosted regression tree models to analyze the demographic, environmental and biological correlates of their occurrence. After accounting for reporting effort, we show that zoonotic EID risk is elevated in forested tropical regions experiencing land-use changes and where wildlife biodiversity (mammal species richness) is high. We present a new global hotspot map of spatial variation in our zoonotic EID risk index, and partial dependence plots illustrating relationships between events and predictors. Our results may help to improve surveillance and long-term EID monitoring programs, and design field experiments to test underlying mechanisms of zoonotic disease emergence.

FLIRT-ing with Zika: A Web Application to Predict the Movement of Infected Travelers Validated Against the Current Zika Virus Epidemic.

INTRODUCTION:  Beginning in 2015, Zika virus rapidly spread throughout the Americas and has been linked to neurological and autoimmune diseases in adults and babies. Developing accurate tools to anticipate Zika spread is one of the first steps to mitigate further spread of the disease. When combined, air traffic data and network simulations can be used to create tools to predict where infectious disease may spread to and aid in the prevention of infectious diseases. Specific goals were to: 1) predict where travelers infected with the Zika Virus would arrive in the U.S.; and, 2) analyze and validate the open access web application's (i.e., FLIRT) predictions using data collected after the prediction was made. METHOD: FLIRT was built to predict the flow and likely destinations of infected travelers through the air travel network. FLIRT uses a database of flight schedules from over 800 airlines, and can display direct flight traffic and perform passenger simulations between selected airports. FLIRT was used to analyze flights departing from five selected airports in locations where sustained Zika Virus transmission was occurring. FLIRT's predictions were validated against Zika cases arriving in the U.S. from selected airports during the selected time periods.  Kendall's τ and Generalized Linear Models were computed for all permutations of FLIRT and case data to test the accuracy of FLIRT's predictions. RESULTS: FLIRT was found to be predictive of the final destinations of infected travelers in the U.S. from areas with ongoing transmission of Zika in the Americas from 01 February 2016 - 01 to April 2016, and 11 January 2016 to 11 March 2016 time periods. MIA-FLL, JFK-EWR-LGA, and IAH were top ranked at-risk metro areas, and Florida, Texas and New York were top ranked states at-risk for the future time period analyzed (11 March 2016 - 11 June 2016). For the 11 January 2016 to 11 March 2016 time period, the region-aggregated model indicated 7.24 (95% CI 6.85 - 7.62) imported Zika cases per 100,000 passengers, and the state-aggregated model suggested 11.33 (95% CI 10.80 - 11.90) imported Zika cases per 100,000 passengers. DISCUSSION: The results from 01 February 2016 to 01 April 2016 and 11 January 2016 to 11 March 2016 time periods support that modeling air travel and passenger movement can be a powerful tool in predicting where infectious diseases will spread next. As FLIRT was shown to significantly predict distribution of Zika Virus cases in the past, there should be heightened biosurveillance and educational campaigns to medical service providers and the general public in these states, especially in the large metropolitan areas.

Evaluation and Verification of the Global Rapid Identification of Threats System for Infectious Diseases in Textual Data Sources.

The Global Rapid Identification of Threats System (GRITS) is a biosurveillance application that enables infectious disease analysts to monitor nontraditional information sources (e.g., social media, online news outlets, ProMED-mail reports, and blogs) for infectious disease threats. GRITS analyzes these textual data sources by identifying, extracting, and succinctly visualizing epidemiologic information and suggests potentially associated infectious diseases. This manuscript evaluates and verifies the diagnoses that GRITS performs and discusses novel aspects of the software package. Via GRITS' web interface, infectious disease analysts can examine dynamic visualizations of GRITS' analyses and explore historical infectious disease emergence events. The GRITS API can be used to continuously analyze information feeds, and the API enables GRITS technology to be easily incorporated into other biosurveillance systems. GRITS is a flexible tool that can be modified to conduct sophisticated medical report triaging, expanded to include customized alert systems, and tailored to address other biosurveillance needs.