An optimized method for setting Internal Quality Control targets (mean and limits) for multi-instrument Internal...

Internal Quality Control (IQC) procedures must evolve as laboratory practices change to meet the requirements of higher patient volumes and the greater need for comparability of results across laboratories. This study developed a strategy to detect significant errors in routine hematology analytical processes using a novel approach to setting IQC material targets (mean and limits) across multiple instruments. This has not been described for hematology laboratories before.

The approach described used a common IQC sample mean and limits determined from a manufacturer-based peer group using the same IQC material. The limits involved a new parameter, the random error of the peer group analyzers around their own mean (SD_intra90), to detect bias or imprecision. The model was assessed over five months using 17 analyzers that measured hemoglobin and red cell count. Using a common mean requires that the different analyzers exhibit no significant bias.

The model effectively controls analytical error in a network of laboratories using the same measurement system. This model aligns with the best theoretical principles for patient risk reduction and harmonizes practices for acceptance and rejection across the network.

The model presents an approach to IQC that meets the demands of real-world practice.