AACC 2019 | American Association for Clinical Chemistry

Visit Bio‑Rad at AACC

American Association for Clinical Chemistry

Booth #3239 | Anaheim, CA | August 4–8

August 7

WED


7:00 AM

Modern Thinking about Lingering QC Questions

Wednesday, August 7, 2019, 7:00–8:30 AM
Location: Anaheim Marriott, Platinum Ballroom 5
Breakfast reception: 6:30–7:00 AM
CEU information: Participants will receive 1.5 CEU units

Objectives:

At the completion of the presentation, the audience will be able to:

  1. Describe two QC strategies for multiple instruments and the advantage of each.

  2. Explain why instrument performance is not enough when determining QC frequency and identify a 2nd criterion.

  3. Identify the number of data points and criteria needed for a reduced crossover study based on the latest version of CLSI C24.

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Presenters:

Curtis Parvin, PhD

Curtis Parvin, PhD

Biostatistical Advisor

John Yundt‑Pacheco, MSCS

John Yundt‑Pacheco, MSCS

Scientific Fellow

Bio‑Rad Laboratories

Nico Vandepoele, BSc

Nico Vandepoele, BSc

Scientific and Professional Affairs Manager

Bio‑Rad Laboratories

Lecture Series Theater Presentation

August 6

TUES


2:00 PM

The utility of droplet digital PCR in molecular diagnostics: clinical validation of a FDA‑cleared BCR‑ABL assay for minimal residual disease monitoring

Tuesday, August 6, 2019, 2:00–2:40 PM
Location: Anaheim Convention Center, Exhibit Hall — Theater 3

Objectives:

At the completion of the presentation, the audience will be able to:

  1. Describe the clinical utility of BCR-ABL monitoring for minimal residual disease in chronic myelogenous leukemia.

  2. Realize the necessity of highly precise and accurate measurement of BCR-ABL transcripts in ongoing clinical research and trials.

  3. Understand how droplet digital PCR can be utilized in the molecular diagnostic lab for a variety of applications including oncology, newborn screening and infectious disease.

Presenters:

Dawne N. Shelton, PhD

Dawne N. Shelton, PhD

R&D Manager for IVD Development

Digital Biology Center

Bio‑Rad Laboratories

Jerald Radich, MD

Jerald Radich, MD

Medical Oncologist

Clinical Research Division

Fred Hutchinson Cancer Research Center

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Key Poster Presentations

Tuesday, AUGUST 6 | 9:30 AM–5:00 PM

Session: 6 / Factors Affecting Test Results

Poster #A‑268 – Field Trial Evaluation of Liquichek Serum Indices Quality Control for Pre‑Analytical Monitoring

Author Attendance Time: 12:30 PM –1:30 PM

Author: L. Wong, H. Onishi, M. Gonzales. Bio‑Rad Laboratories, Irvine, CA

Background: Pre‑analytical assessment of patient specimens is arguably one of the most important steps in ensuring quality test results. Specimens compromised by improper handling and the patient condition can interfere with assay performance. Three of the most common specimen interferences that contribute to pre‑analytical variation are Hemolysis (H), Icterus (I), and Lipemia (L). Recently, there has been an emergence of automated instrumentation that has replaced the visual assessment of specimen interferences. The Automated detection of interferences provides several advantages to the laboratory including, increased accuracy and streamlined workflow. As the need for automation increases, so does the need for reliable monitoring of instrument performance. Commercially produced, human‑derived sera replicating specimen interferences is now needed to achieve the workflow demands of today’s clinical laboratory. This study evaluates field trial results from four instrument platforms to demonstrate the functionality, commutability, and utility of the third‑party Liquichek Serum Indices product.

Methods: Testing was performed in a series of US and European clinical laboratories following the manufacturer’s instrument instruction. The product is available in individual 4 mL vials of H, I, L, or non-interfered.Labs pipetted each product into three sample cup and performed single replicate.

Conclusion: Results demonstrate the functionality, commutability and utility of the third‑party Liquichek Serum Indices to monitor an instrument’s ability to detect potential interferences through the HIL pre‑analytical test function, thereby increasing the reliability of test results, and ultimately improving patient care.


Wednesday, AUGUST 7 | 9:30 AM–5:00 PM

Session: 13 / Management Sciences and Patient Safety

Poster #B‑030 – Evaluating the Components of Risk of Patient Harm from Erroneous Results

Author Attendance Time: 12:30 PM –1:30 PM

Author: J. C. Yundt‑Pacheco, Bio‑Rad Laboratories, Plano, TX

Background: Using a risk-based approach to QC requires estimating the risk of patient harm from erroneous results (in this case using a Risk Management Index‑RMI), after which, a lab must determine what to do if there is an unacceptable level of risk (RMI>1)? To correct the situation, one must assess the components of risk. What part of the overall risk can be attributed to in-control imprecision, in‑control bias, or out‑of‑control conditions? If too many erroneous results are produced because of in‑control imprecision, there is no QC strategy that will fix it. A similar argument can be made for bias. While the production of erroneous results from out‑of‑control conditions is not independent of in‑control performance, it is still useful to assess how much risk is attributed to out‑of‑control conditions which may be mitigated by adjusting the QC strategy. The method below evaluates these risk components.

Methods: The sources of risk of patient harm from erroneous results are computed as:
PE(0)[probability of in-control erroneous results]=f(x)[1-Ф([x+TEa(x)-bias(x)]/ơ(x))+Ф([x-TEa(x)-bias(x)]/ơ(x))]
PEi(0)[probability of erroneous results from imprecision]=PE(0)-f(x)[1-Ф([x+TEa(x)]/ơ(x))+Ф([x-TEa(x)]/ơ(x))]
PEb(0)[probability of erroneous results from bias]=PE(0)-PEi(0)
PΔE[probability of out‑of‑control erroneous results] =∫[E(Nuf(SE))/[MPBF+ANPed(SE)] dSE over ±2 TEa range.
PE[probability of erroneous result]=PEi(0)+PEb(0)+PΔE
PPH[predicted probability of harm]=PE*Ph|u
Acceptable PH[acceptable probability of harm]is derived from Severity of Harm and risk acceptability matrix.
RMI (RMI<=1 indicates managed risk)=PPH/Acceptable PH
Formulas, terms and definitions are abbreviated for abstract.

Results: Computation Example Glucose:CV=2.5%,TEa=±10%,bias=+1. QC Strategy:2QC levels,1:3 Rule, every 50 results. MPBF=9,000. Ph|u=0.5.
Acceptable PH=1/10,000.

Conclusion: Breaking the RMI of a test method into the individual components of risk gives the laboratorian critical information for resolving risk issues – is the problem imprecision, bias or the quality control strategy, or some combination? Once the risk components have been evaluated, steps can be taken to mitigate risk by reducing the risk associated with bias, or imprecision, or the quality control strategy.


Wednesday, AUGUST 7 | 9:30 AM–5:00 PM

Session: 15 / Molecular Pathology

Poster #B‑123 – Analytical Validation of a Clinical ddPCR Assay for Graft‑derived Cell‑free DNA Determination

Author Attendance Time: 12:30 PM –1:30 PM

Author: J. Beck1, M. Oellerich2, A. Teubert3, R. Glaubitz3, E. Schütz1. 1Chronix Biomedical, Göttingen, Germany, 2University Medical Center Göttingen, Göttingen, Germany, 3amedes Genetics, Hannover, Germany

Background: Quantification of graft-derived cell‑free DNA (GcfDNA) has been widely investigated as new tool to monitor graft integrity. By this time a large body of evidence proves the clinical validity of GcfDNA as biomarker for rejection, whereby the two main testing methods are next-generation sequencing (NGS) or droplet digital PCR (ddPCR) assays. Before widespread application in clinical practice the analytical performance of the employed methods has to be tested. Here we report the analytical validation of an improved clinical ddPCR method for the quantification of GcfDNA.

Methods: We developed a workflow using 40 preselected SNPs, which are tested in a two-step screening using white-blood cells and plasma from the transplant recipient. The result of the screening are informative assays, of which at least four are used for the quantification of the GcfDNA fraction using ddPCR in all subsequent samples of the same patient. Prior to the quantitative ddPCR the informative assays are preamplified in a multiplex PCR. Analytical performance of the method was assessed in a total of 330 DNA‑samples by the following experiments: 1) all 40 assays were run in triplicate on DNA-mixtures with the respective minor allele frequency of 2%; 2) the limits of blank (LoB) were assessed using four different samples each with 10 replicates and evaluated for false‑positive allele counts; 3) the limit of detection (LoD) was calculated as LoB+1.645*STDEV of a 0.2% DNA mixture measured in twenty repetitions; 4) lower limit of quantification (LLoQ) was assessed by measuring five different DNA‑mixtures (0.1‑0.3% MAF) in five repetitions on four different days and three mixtures (0.4‑10% MAF) in ten repetitions on the same day; 5) minimum and maximum input amounts of total cfDNA were assessed.

Results: The LoB was 0.1% and the assay reliably quantifies GcfDNA fractions from 0.15% (interassay CV <20%=LoD) to 99%, with an interassay CV of 12% at 0.23% GcfDNA and 10% at 0.32% GcfDNA. At 9.4% GcfDNA the CV was 3%. Importantly, the sensitivity and accuracy of liquid biopsy assays strongly relies on the amount of total cfDNA used as input. Therefore, our method includes a ddPCR that precisely quantifies the amount of extracted total cfDNA and allows tight control of the input amounts in the diagnostic ddPCR that determines the GcfDNA fraction. The minimum amount of total amplifyable cfDNA is set at 4545 haploid genomic copies. Furthermore, the upfront quantification of the total cfDNA per mL plasma can be used to calculate the absolute GcfDNA levels in cp/mL plasma from the fractional abundance. This combined measurement also allows the compensation of fluctuations in the recipient cfDNA levels which would otherwise falsify the fractional GcfDNA values.

Conclusion: Clinical diagnostic assays employing cell‑free DNA must be strictly controlled and must be able to reliably quantify even low fractions of the analyte in low total input quantities. The improved ddPCR meets all criteria for a clinical assay for monitoring graft integrity in solid organ transplantation.

Exhibited Products

Quality Control

A1c Testing

Infectious Disease Testing

Blood Bank Testing

Droplet Digital PCR for Molecular Diagnostics