Average of Patient Deltas: Patient-Based Quality Control Utilizing the Mean Within-Patient Analyte Variation

Because traditional QC is discontinuous, laboratories use additional strategies to detect systematic error. One strategy, the delta check, is best suited to detect large systematic error. The moving average (MA) monitors the mean patient analyte value but cannot equitably detect systematic error in skewed distributions. Our study combines delta check and MA to develop an average of deltas (AoD) strategy that monitors the mean delta of consecutive, intrapatient results.

Arrays of the differences (delta) between paired patient results collected within 20-28 h of each other were generated from historical data. AoD protocols were developed using a simulated annealing algorithm in MatLab (Mathworks) to select the number of patient delta values to average and truncation limits to eliminate large deltas. We simulated systematic error by adding bias to arrays for plasma albumin, alanine aminotransferase, alkaline phosphatase, amylase, aspartate aminotransferase, bicarbonate, bilirubin (total and direct), calcium, chloride, creatinine, lipase, sodium, phosphorus, potassium, total protein, and magnesium. The average number of deltas to detection (ANDED) was then calculated in response to induced systematic error.

ANDED varied by combination of assay and AoD protocol. Errors in albumin, lipase, and total protein were detected with a mean of 6 delta pairs. The highest ANDED was calcium, with a positive 0.6-mg/dL shift detected with an ANDED of 75. However, a negative 0.6-mg/dL calcium shift was detected with an ANDED of 25.

AoD detects systematic error with relatively few paired patient samples and is a patient-based QC technique that will enhance error detection.