MSACL 2016 US Abstract

Automated Data Review Using Homebrew Software

Sheng-Ying Lo (Presenter)
University of Washington

Authorship: Sheng-Ying Lo
University of Washington, Seattle, WA

Short Abstract

The analysis of multiplexed LC-MS/MS data can be a complicated task. This session will describe the experience of a single institution with in-house software to analyze mass spectrometry data containing 828 data points per patient sample. Utilization of this software has reduced the time required for data analysis and has improved the consistency of the QA procedures as they are applied from day-to-day in our assay that measures 23 opioids and opioid metabolites in urine.

Long Abstract

Mass spectrometry is the confirmatory detection method of choice for chronic pain monitoring due to its superior sensitivity and specificity when compared with immunoassays. Even with mass spectrometric detection of analytes, inaccurate results are possible due to interferences. Because the clinical consequence of reporting inaccurate results can be extremely serious, a robust review process of the mass spectrometric data is required to ensure high quality results. At our medical institution, we have deployed a targeted, multiplexed LC-MS/MS assay on a triple quadrupole mass spectrometer that quantifies 23 opioids and opioid metabolites in urine. Data elements that can be used for quality assurance in each sample include analyte retention time, transition ion ratios, internal standard peak areas, and spike recovery results. To this end, our current workflow requires evaluation of 828 data points for each patient specimen. To facilitate the process of data analysis, a software package developed in-house is used to perform quality control and quality assurance calculations. This presentation will describe the experience of a single institution with the development and implementation of a custom software package to analyze complex mass spectrometry data. Introduction of this software has reduced the time required for analysis (saving 18 minutes of technologist time per specimen) and improved the overall consistency of QA procedures among the technologists running the assay.

Learning objectives of this session:

1. Outline a robust quality control strategy for urine drug testing by mass spectrometry.

2. List the goals for custom automated workflows.

3. Design an algorithm to analyze quality metrics in urine drug screens.

References & Acknowledgements:

Financial Disclosure

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