Patient-based feedback control for erythroid variables obtained using in-house automated hematology analyzers in veterinary medicine

Background Automated in‐house diagnostic analyzers, most commonly used for hematologic and biochemical analysis, are typically calibrated, and then control materials are used to confirm the quality of results. Although this approach provides indirect knowledge that the system is performing correctly, it does not provide direct knowledge of system performance between control runs. Objectives The objectives of this study were to apply analysis of weighted moving averages to assess performance of hematology analyzers using animal patient samples from dogs, cats, and horses as they were analyzed and apply correction factors to mitigate instrument‐driven biases when they developed. Methods A set of algorithms was developed and applied to sequential batches of 20 samples. Repeated samples within a batch and large populations of samples with similar abnormalities were excluded. Data for 6 hematologic variables were grouped into batches of weighted moving averages; data were analyzed with control chart rules, a gradient descent algorithm, and fuzzy logic to define and apply adjustments. Results A total of 102 hematology analyzers that had developed biases in RBC count, HCT, hemoglobin (HGB) concentration, MCV, MCH, and MCHC were evaluated. Following analysis, all variables except HGB concentration required adjustment, with RBC counts requiring only slight change and MCV requiring the greatest change. Adjustments were validated by comparing PCVs with the original and adjusted HCT values. Conclusions The proposed system provides feedback control to minimize system bias for RBC count, HCT, HGB concentration, MCV, MCH, and MCHC. Fundamental assumptions must be met for the approach to assure proper functionality.