A simple matrix of analytical performance to identify assays that risk patients using External Quality Assurance Program data

Objectives: We propose a simple way to reliably rank assays for improvement according to patient risk, based solely on EQA imprecision and biological variation data. Because the underlying technique aligns the imprecision class of an assay from EQA data, peer performance can be used to assess achievable imprecision and the risk ranking can not only prioritise improvement but also highlight laboratory QC operating parameters that are easy to manage and provide reliable, acceptable performance. Design and methods: A modified Failure Modes Effects Analysis (FMEA) is applied to produce an analyte risk rating based on three factors, each of which is graded: 1) the ease of detecting analytical errors based on the ratio of allowable limits of performance to imprecision (Assay Capability) compared to absolute standards and to peers, 2) the predicted frequency of errors in patient monitoring based on the ratio of within-individual biological variation to laboratory imprecision, and 3) the clinical importance of the assay as a surrogate marker for harm arising from an error. Results: We provide laboratory examples to illustrate these models. Conclusion: The proposed models using only EQA data can objectively identify assays at risk of failing against biological variation goals for monitoring patients and suggest parameters for reliable performance.