Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
Cembrowski Cembrowski Quality Control Consulting, Edmonton, AB, Canada.
Clin Chem. 2021 Jul 6;67(7):1019-1029. doi: 10.1093/clinchem/hvab057.
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.
由于传统的 QC 是不连续的，实验室使用额外的策略来检测系统误差。一种策略是 delta 检查，最适合检测大的系统误差。移动平均值（MA）监测患者分析物的平均值，但不能公平地检测偏态分布中的系统误差。我们的研究结合了 delta 检查和 MA，开发了一种平均 delta（AoD）策略，该策略监测连续的、个体患者结果的平均 delta。
从历史数据中生成了成对患者结果之间在 20-28 小时内收集的差异（delta）数组。AoD 方案是使用 MatLab（Mathworks）中的模拟退火算法开发的，以选择要平均的患者 delta 值的数量和消除大 delta 的截断限制。我们通过向血浆白蛋白、丙氨酸氨基转移酶、碱性磷酸酶、淀粉酶、天冬氨酸氨基转移酶、碳酸氢盐、胆红素（总胆红素和直接胆红素）、钙、氯、肌酐、脂肪酶、钠、磷、钾、总蛋白和镁的数组中添加偏差来模拟系统误差。然后计算出对诱导系统误差的平均 delta 数（ANDED）。
ANDED 因分析物和 AoD 方案的组合而异。白蛋白、脂肪酶和总蛋白的误差可以通过平均 6 对 delta 对来检测。钙的 ANDED 最高，正 0.6-mg/dL 偏移检测的 ANDED 为 75。然而，负 0.6-mg/dL 钙偏移检测的 ANDED 为 25。
AoD 使用相对较少的配对患者样本检测系统误差，是一种基于患者的 QC 技术，将增强误差检测。
