HantaNet: A New MicrobeTrace Application for Hantavirus Classification, Genomic Surveillance, Epidemiology and Outbreak Investigations.

Hantaviruses zoonotically infect humans worldwide with pathogenic consequences and are mainly spread by rodents that shed aerosolized virus particles in urine and feces. Bioinformatics methods for hantavirus diagnostics, genomic surveillance and epidemiology are currently lacking a comprehensive approach for data sharing, integration, visualization, analytics and reporting. With the possibility of hantavirus cases going undetected and spreading over international borders, a significant reporting delay can miss linked transmission events and impedes timely, targeted public health interventions. To overcome these challenges, we built HantaNet, a standalone visualization engine for hantavirus genomes that facilitates viral surveillance and classification for early outbreak detection and response. HantaNet is powered by MicrobeTrace, a browser-based multitool originally developed at the Centers for Disease Control and Prevention (CDC) to visualize HIV clusters and transmission networks. HantaNet integrates coding gene sequences and standardized metadata from hantavirus reference genomes into three separate gene modules for dashboard visualization of phylogenetic trees, viral strain clusters for classification, epidemiological networks and spatiotemporal analysis. We used 85 hantavirus reference datasets from GenBank to validate HantaNet as a classification and enhanced visualization tool, and as a public repository to download standardized sequence data and metadata for building analytic datasets. HantaNet is a model on how to deploy MicrobeTrace-specific tools to advance pathogen surveillance, epidemiology and public health globally.

Optimizing drug inventory management with a web-based information system: The TBTC Study 31/ACTG A5349 experience.

INTRODUCTION: Efficient management of study drug inventory shipments is critical to keep research sites enrolling into multisite clinical treatment trials. A standard manual drug-management process used by the Tuberculosis Trials Consortium (TBTC), did not accommodate import permit approval timelines, shipment transit-times and time-zone differences. We compared a new web-based solution with the manual process, during an international 34-site clinical trial conducted by the TBTC and the AIDS Clinical Trials Group (ACTG); TBTC Study 31/ACTG A5349. MATERIAL AND METHODS: We developed and implemented a technological solution by integrating logistical and regulatory requirements for drug importation with statistical simulations that estimated stock-out times in an online Drug Management Module (DMM). We measured the average shipment-related drug stock-outs and time to drug availability, to assess the efficiency of the DMM compared to the manual approach. RESULTS: An Interrupted Time-Series (ITS) analysis showed a 15.4% [p-value = 0.03; 95% C.I. (-28.8%, -2.0%)] reduction in average shipment-related study drug stock-out after DMM implementation. The DMM streamlined the restocking process at study sites, reducing median transit-time for sites associated with a depot by 2 days [95% C.I. (-3.0, -1.0)]. Under the DMM, study drugs were available for treatment assignment on the day received, compared to one day after receipt under the manual process. DISCUSSION: The DMM provided TBTC's Data and Coordinating Center and site staff with more efficient procedures to manage and consistently maintain study drug inventory at enrolling sites. This DMM framework can improve efficiency in future multicenter clinical trials. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (Identifier: NCT02410772) on April 8, 2015.

Central monitoring in a randomized, open-label, controlled phase 3 clinical trial for a treatment-shortening regimen for pulmonary tuberculosis.

INTRODUCTION: With the growing use of online study management systems and rapid availability of data, timely data review and quality assessments are necessary to ensure proper clinical trial implementation. In this report we describe central monitoring used to ensure protocol compliance and accurate data reporting, implemented during a large phase 3 clinical trial. MATERIAL AND METHODS: The Tuberculosis Trials Consortium (TBTC) Study 31/AIDS Clinical Trials Group (ACTG) study A5349 (S31) is an international, multi-site, randomized, open-label, controlled, non-inferiority phase 3 clinical trial comparing two 4-month regimens to a standard 6 month regimen for treatment of drug-susceptible tuberculosis (TB) among adolescents and adults with a sample size of 2500 participants. RESULTS: Central monitoring utilized primary study data in a five-tiered approach, including (1) real-time data checks & topic-specific intervention reports, (2) missing forms reports, (3) quality assurance metrics, (4) critical data reports and (5) protocol deviation identification, aimed to detect and resolve quality challenges. Over the course of the study, 240 data checks and reports were programed across the five tiers used. DISCUSSION: This use of primary study data to identify issues rapidly allowed the study sponsor to focus quality assurance and data cleaning activities on prioritized data, related to protocol compliance and accurate reporting of study results. Our approach enabled us to become more efficient and effective as we informed sites about deviations, resolved missing or inconsistent data, provided targeted guidance, and gained a deeper understanding of challenges experienced at clinical trial sites. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (Identifier: NCT02410772) on April 8, 2015.