Lab Matters Summer 2019 - Page 81

APHL 2019 POSTER ABSTRACTS Results: This intermediate-level multimedia online course discusses a stepwise plan for setting up and validating a new LC-MS/MS based assay for application in a biochemical genetics laboratory. A specific example of quantifying methylmalonic acid in human plasma is used to describe each of the steps required to develop and validate the method. An outline of the validation report is provided that includes how to review, interpret and document validation tests, quality control results, and proficiency tests. Continuing education credits are available from this course free of charge, including 1.5 hours of the ASCLS P.A.C.E. credit. The course is expected to be publicly available on CDC TRAIN website at https://www.train.org/cdctrain/home in Spring 2019. 23 submitters accounted for 50.1% of errors. More than half (52%) of specimens with submission errors were associated with 13/152 NYC PHL submitters. The most common error types were: specimen identifiers not matching those on the submission form (CDC 56%, NYC PHL 26%), no submission form (CDC 16%, NYC PHL 39%), specimen labeling errors (CDC 2%, NYC PHL 30%). Additionally, CDC had a specific error of >1 specimen per submission form (9%). Existing QA procedures resolved most submission errors, often through correcting CDC ELIMS and NYC LIMS entries. CDC ELIMS data showed 35 CDC lab units corrected identifier- related submission errors in ELIMS (682 corrections per 100,000 specimens with errors). Conclusions: This online training module is intended to help laboratory professionals understand the regulatory environment of a biochemical genetics laboratory, define the steps required to develop and validate an assay based on LC-MS/MS, and describe the contents of the validation report. Course evaluation results and feedback from the participants will be closely monitored to assess the utility and learning outcomes. Conclusions: Overall, the percentage of specimens submitted to CDC and NYC PHL with errors was low. This analysis shows the power of LIMS to inform evidence-based improvement of pre- analytic processes. Accessioning QA data revealed that most errors associated with specimen submissions were related to incomplete or inaccurate submission forms. This analysis identifies specific areas for quality improvement and provides a reference to implement future mitigations (e.g., job aids and LIMS- integrated analytic tools) that would allow labs to conduct internal assessments. Presenter: Bertina Su, Association of Public Health Laboratories, Silver Spring, MD, bertina.su@aphl.org Improving Specimen Submission Through Data Analysis of Mislabeled Specimens Received by the Centers for Disease Control and Prevention and the New York City Public Health Laboratory R. Fowler 1 , D. Lowe 1 , C. Kretz 1 , R. Stinnett 1 , M. Lawrence 1 , A. Marinova-Petkova 1 , M. Hardy 1 , M. Petway 1 , E. Wilson 2 , P. Mandel 1 , S. Soroka 1 , J. Rakeman 3 , A. Muehlenbachs 1 ; 1 Centers for Disease Control and Prevention, 2 New York City Department of Health and Mental Hygiene, 3 New York City Public Health Laboratory Background: Accurate specimen identification and test requisitioning are cornerstones of quality assurance (QA) for laboratory testing. Specialized groups that receive and accession specimens, such as CDC’s Specimen Triage and Transportation Team (STATT) and the New York City Public Health Laboratory (NYC PHL) requisition team, provide QA measures to identify specimen submission errors. Laboratory information management systems (LIMS) data can be used to evaluate the source of these errors and to identify opportunities for improvement. Our objective was to identify the frequency and types of submission errors at CDC and NYC PHL through analysis of LIMS data and QA records. Methods: CDC’s Enterprise LIMS (ELIMS) was queried to identify submission errors among the 856, 120 specimens received by CDC from various PHLs during October 2014–January 2018. Data for submitter, patient, and CDC lab unit identifiers were blinded. CDC lab units (n=108) were queried for changes to patient identifiers. CDC STATT QA records for 265, 397 specimens received by CDC during July 2014–June 2018 were reviewed to identify additional specimen submission errors that were resolved before ELIMS accessioning. NYC PHL LIMS was queried for submission errors that could be corrected before testing and errors that resulted in specimen rejection during May 2017–May 2018. Proportion of specimens with submission errors was estimated by type, submitter and laboratory unit. Results: NYC PHL, CDC ELIMS, and CDC STATT identified errors in 1.5%, 0.68% and 0.1% of specimen submissions, respectively. CDC received specimens with submission errors from 144 submitters; PublicHealthLabs @APHL APHL.org Presenter: Marlon Lawrence, Centers for Disease Control and Prevention, Atlanta, GA, nrk4@cdc.gov Analyzing the Performance of Different Types of Coolers and Coolants to Improve Cold Chain Transportation D. Lowe , G. Pellegrini, A. Carter, E. LeMasters and A. Weiner, Centers for Disease Control and Prevention Background: Clinical and research specimens often must remain cold during transportation to the laboratory. Long distances, poor infrastructure, and limited coolants frequently lead to samples arriving too warm and possibly unusable. Yet, there is little empirical evidence addressing how different coolers or coolants perform. Our objective was to analyze different types of coolers and coolants, and to develop a model to estimate the amount of coolant needed based on variable transit times. Methods: We compared 16 different coolers and 10 different coolants by the time needed to reach 6°C. Two categories of coolers were evaluated: currently utilized polystyrene foam and injection molded coolers, and newer rotation-molded coolers. A standard amount of coolant filled each cooler and a data logger recorded the temperature. The coolant categories evaluated were cold packs and frozen filled water bottles. We measured how long coolants kept temperatures below a target temperature of 6°C versus cost. Finally, we varied the amount of coolant to generate a predictive model for transit times. Results: Polystyrene foam and rotation-molded coolers remained below the target temperature longer than injection-molded coolers (average: 61.4 hrs vs 39.7 hrs). This duration varied with rotation- molded coolers (range: 36.2-109.5 hrs). The coolant sources also varied in their time below 6°C (range: 11.1–27.4 hrs) and their cost (range: $0.11-$12.95/pound). Frozen water bottles remained below 6°C for a similar time as other cold packs, but were the most economical. Finally, we varied the amount of water bottles to build a predictive model that predicted the time to 6°C to within 3hr (R-squared = 0.994) for rotation-molded coolers. Summer 2019 LAB MATTERS 79