Curtis A. Parvin

Collective opinion paper on findings of the 2009 convocation of experts on quality control.

Abstract On May 28–29, 2009, a number of medical laboratory opinion leaders, pathologists and biochemists met in Sitges, Spain to discuss issues of interest to medical laboratory professionals. The meeting was sponsored by Bio-Rad Laboratories Inc. (Hercules, CA). Over 40 persons representing Austria, Belgium, Czech Republic, Finland, Germany, Great Britain, Israel, Italy, Netherlands, Portugal, South Africa, Spain, Sweden and the US participated in the 1.5 days meeting. The intended purpose of the convocation was to give medical laboratory professionals from different countries and backgrounds an opportunity to share ideas, concerns and experiences in five areas of interest of the sponsor. These areas of interest included: • a requirement for medical laboratory accreditation across Europe • uncertainty of measurement in a clinical laboratory setting • application of Six Sigma values to characterize laboratory quality • effects of analytical errors on patient care and outcomes • harmonization of allowable total error (TEa) specifications The convocation began with a keynote speech by Dr. James Westgard on “Managing quality vs. measuring uncertainty in the medical laboratory”. Dr. Westgards presentation was thought provoking and called into question the utility and practicality of using uncertainty in a medical laboratory setting. This journal contains a companion article written by Dr. Westgard on this topic. After the keynote speech, the meeting adjourned into five discussion groups and reconvened the next day to hear the outcomes of the discussions by each of the working groups. This article provides a synopsis of the reports from each working group. Clin Chem Lab Med 2010;48:41–52.

Validating the Performance of QC Procedures

A methodology for computing the maximum expected number of unreliable patient results produced because of an out-of-control condition for a given quality control strategy is presented along with strategies for changing the expected number of unreliable results produced and reported. The expected number of unreliable patient results reported before and after the last accepted quality control evaluation before the detection of an out-of-control condition are discussed and used as design criteria for quality control strategies that meet a laboratorys risk criteria.

Comparing analytic performance criteria: evaluation of HbA1c certification criteria as an example.

BACKGROUND Direct comparison of analytical performance criteria that utilize different statistical approaches can be problematic. We describe a mathematical approach to compare performance criteria for hemoglobin A1c (HbA1c) analysis used by the NGSP standardization program and the College of American Pathologists (CAP) to enhance consistency between the schemes. METHODS The imprecision (CV) and bias combinations required to pass each criterion at probabilities of 0.95, 0.99 and 0.999 were calculated and used to construct contour plots to compare them. The CV/bias requirements were calculated mathematically for the 2011-2012 CAP (3/3 results within ±7% of the target) and different proposed NGSP (33/40 to 40/40 results within ±7% of the target) criteria, and using computer simulations for the existing NGSP criterion (95% confidence interval of the differences between the method and NGSP within ±0.75% HbA1c). RESULTS Requiring 37 of 40 results to be within ±7% of the NGSP target best matched the CAP criterion at zero bias (95% chance of passing). CONCLUSIONS The NGSP Steering Committee recommended a certification criterion of 37 of 40 results within ±7% of the NGSP (reduced to ±6% in 2014). The described evaluation approach may be useful in other situations where comparison of different performance criteria is desired.

Assessing Quality Control Strategies for HbA1c Measurements From a Patient Risk Perspective

Background: Current laboratory risk management principles emphasize the importance of assessing laboratory quality control (QC) practices in terms of the risk of patient harm. Limited practical guidance or examples on how to do this are available. Methods: The patient risk model described in a published laboratory risk management guideline was combined with a recently reported approach to computing the predicted probability of patient harm to produce a risk management index (RMI) that compares the predicted probability of patient harm for a QC strategy to the acceptable probability of patient harm based on the expected severity of harm caused by an erroneously reported patient result. Results: Measurement procedure capability and quality control performance for two instruments measuring HbA1c in a laboratory were assessed by computing the RMI for each instrument individually and for the laboratory as a whole. Conclusions: This assessment provides a concrete example of how laboratory QC practices can be directly correlated to the risk of patient harm from erroneously reported patient results.

Planning Statistical Quality Control to Minimize Patient Risk: It's About Time

The purpose of statistical quality control (SQC)2 in the clinical laboratory is to assure that reported patient results are fit for their intended use, not only when a measurement procedure is operating in its stable incontrol state, but also when out-of-control conditions occur. The value of quality-control principles and practices in the laboratory has been well recognized and appreciated for many decades. The Clinical and Laboratory Standards Institute (CLSI; then known as the NCCLS) published its first approved guideline on statistical quality-control principles and definitions for quantitative measurement procedures in 1991 (1). The fourth edition of the guideline appeared last year (2). For many years SQC design primarily involved choosing how many quality control (QC) samples to measure and what QC rules to apply to the QC results. This approach originated in an era when batch testing was common. QC samples were placed in the batch along with patient specimens. The QC sample results were used to decide if the patient results in the batch were acceptable. The goal was for the QC rule to have a low probability of rejection when the batch was in control (probability of false rejection, Pfr) and a high probability of rejection when the batch was out-of-control (probability of error detection, Ped) (3). When continuous-production analyzers became prevalent in the laboratory, a new QC planning question arose: When should QC samples be measured? In batch testing, the answer was to measure QC samples with each batch. However, with continuous-production analyzers a link between QC results and patient results within a batch no longer exists. Instead, QC results simply reflect the current state of the measurement procedure at the time they are measured. Unfortunately, the traditional QC performance measures, …