Comparison of four methods of establishing control limits for monitoring quality controls in infectious disease serology testing

    loading  Checking for direct PDF access through Ovid

Abstract

Background:

A general trend towards conducting infectious disease serology testing in centralized laboratories means that quality control (QC) principles used for clinical chemistry testing are applied to infectious disease testing. However, no systematic assessment of methods used to establish QC limits has been applied to infectious disease serology testing.

Methods:

A total of 103 QC data sets, obtained from six different infectious disease serology analytes, were parsed through standard methods for establishing statistical control limits, including guidelines from Public Health England, USA Clinical and Laboratory Standards Institute (CLSI), German Richtlinien der Bundesärztekammer (RiliBÄK) and Australian QConnect. The percentage of QC results failing each method was compared.

Results:

The percentage of data sets having more than 20% of QC results failing Westgard rules when the first 20 results were used to calculate the mean±2 standard deviation (SD) ranged from 3 (2.9%) for R4S to 66 (64.1%) for 10X rule, whereas the percentage ranged from 0 (0%) for R4S to 32 (40.5%) for 10X when the first 100 results were used to calculate the mean±2 SD. By contrast, the percentage of data sets with >20% failing the RiliBÄK control limits was 25 (24.3%). Only two data sets (1.9%) had more than 20% of results outside the QConnect Limits.

Conclusions:

The rate of failure of QCs using QConnect Limits was more applicable for monitoring infectious disease serology testing compared with UK Public Health, CLSI and RiliBÄK, as the alternatives to QConnect Limits reported an unacceptably high percentage of failures across the 103 data sets.

Related Topics

    loading  Loading Related Articles