APHL 2022 POSTER ABSTRACTS
Infectious Diseases / Informatics
( mCIM / eCIM or CARBA-NP , respectively ) and / or molecular tests ( Xpert Carba-R , lab developed PCR ). With the increased adoption of whole genome sequencing ( WGS ) in clinical and public health labs , we evaluated the use of WGS to identify the presence of antimicrobial resistance genes in bacterial isolates . To accommodate this effort , we developed a new bioinformatics pipeline , Spriggan , to assemble bacterial WGS data and identify antimicrobial resistance genes . Using a dataset of 220 bacterial isolates sequenced at the Wisconsin State Laboratory of Hygiene ( WSLH ) between 2019 and 2021 , we evaluated the performance of this pipeline by comparing the wet lab and genomic results . A carbapenemase gene was detected by CARBA-R and / or custom PCR panel in 210 of the 220 isolates analyzed ( 95 %). WGS results were in agreement for 208 of these isolates ( 99 %). Of the two discrepant isolates , one was the result of an improperly labeled sample , which was resolved through repeat testing . The other discrepant isolate tested positive for two carbapenemase genes by molecular methods , but WGS only identified one of them . An additional 10 isolates that were mCIM +/ PCR- during lab testing were determined by WGS to have carbapenemase genes not covered by the molecular assays available at WSLH . This highlights the ability of WGS to detect uncommon or novel resistance mechanisms . Herein , our results demonstrate the feasibility of using WGS to detect antimicrobial resistance genes in bacterial isolates that could complement , or potentially replace , traditional methods .
Presenter : Logan Patterson , Wisconsin State Laboratory of Hygiene , pattersonlx0 @ gmail . com
INFORMATICS
Rapid Expansion of Bioinformatics Capabilities at the Texas Department of State Health Services
S Marcellus , K Bobier , J Lu and R Lee , Texas Department of State Health Services
Through the addition of dedicated bioinformaticians and the acquisition and establishment of bioinformatics infrastructure , the integration and usage of genetic and genomic data has grown rapidly . The Bioinformatics Group at the Texas Department of State Health Services ( BIGTex ) grew from one APHL – CDC Bioinformatics Fellow and one APHL Covid-19 Associate to four full-time bioinformaticians over the course of six months . The BigTex staff have launched projects across infectious disease and newborn screening . Infectious disease efforts to analyze SARS-CoV-2 DNA sequences , automate sequence submission to public data repositories such as NCBI-SRA and GISAID , sequence SARS-CoV-2 in community wastewater and establish an antibiotic resistance laboratory network pipeline have provided much needed genetic and genomic information for epidemiologic surveillance and decision making . The newborn screening group is in development of a variant interpretation pipeline and whole-exome sequencing data analysis pipeline in addition to utilizing the bioinformatics infrastructure for epidemiologically based projects such as cut-off value evaluation for screening of metabolic conditions . To facilitate the rapid expansion and staff development , connecting and communicating with other bioinformaticians and state laboratories was vital . Configuration of existing pipelines and projects in use at other states expedited the implementation of genomic analysis , specifically for SARS-CoV-2 and newborn screening . The vast web of bioinformatics growth allows our public health laboratory to better serve Texans through genetic and genomic analysis and provides a foundation for future expansion of genomic surveillance and testing in Texas .
Presenter : Samantha Marcellus , Texas Department of State Health Services Laboratory , smarcellus @ live . com
Laboratory Reporting Technical Assistance : A Model for Improving Electronic Laboratory Reporting Systems in State Public Health Agencies
M Schmitz 1 , S Reddy 1 , C Davison 2 , L Trujillo 1 , K Rondini 1 , C Hall 2 ;
1
Booz Allen Hamilton , 2 Centers for Disease Control and Prevention
Background : Given the progression and urgency of the COVID-19 pandemic , the Centers for Disease Control and Prevention ( CDC ) has worked to improve the timeliness , completeness and accuracy of laboratory COVID-19 data reported to CDC via the provision of technical assistance ( TA ) to public health agencies ( PHAs ). The TA aims to apply subject matter expertise in onboarding processing and reporting data when PHAs face reporting challenges and may need technical help . TA expertise encompasses electronic laboratory reporting ( ELR ), PHA electronic information exchange , and interoperability of surveillance systems , with an end goal of providing CDC with national surveillance data and enabling a datadriven response to COVID-19 .
Methods : The Booz Allen Hamilton Laboratory Reporting Technical Assistance ( LRTA ) project has supported the CDC ’ s Laboratory Reporting Working Group , within the CDC COVID-19 emergency response , since July 2020 . States can submit requests for TA to the Association for Public Health Laboratories ( APHL ) via a Smartsheet form . The team composition , tools and processes developed vary by PHA needs .
Results : The LRTA team has worked with 10 PHAs : Arizona , Florida , Louisiana , Oklahoma , Oregon , South Carolina , Texas , Vermont , Virginia and Washington . The LRTA contract team has participated in various ELR-related tasks , such as onboarding facilities which provide ELR records into state health surveillance systems ; improving HL7 message handling in Rhapsody , BizTalk and Mirth ; creating automatic data validation / output tools for facilities , using Python and VBA , to manually audit and QA spreadsheet data ; configuring the National HL7 Generator tool , created by APHL , for PHAs to convert flat file data into HL7 messages ; and creating automated process improvement tools and dashboards in R Studio and PowerBI to identify reporting anomalies . Recently , the team has developed a data-scraping tool from faxed PDF files using optical character recognition and creating RestAPI calls to automate data transformations from point-of-care forms into state surveillance system standards .
Conclusion : The LRTA project has addressed various challenges in laboratory data reporting at the PHA-level : providing solutions for a lack of qualified HL7 resources and informaticians to onboard and implement large number of facilities for ELR ; manual and time-consuming validation processes ; manual data entry due to paper reporting ; data quality issues due to CSV / flat file reporting ; and inefficient message processing and manual error handling for PHA systems . Each process has improved the timeliness , accuracy