Application of Patient Data–Based Quality Control in the Laboratory Serum Potassium and Magnesium Tests

Objective: The study aims to retrospectively analyze the results of serum potassium (K) and serum magnesium (Mg) tests conducted in the laboratory and evaluate the application of the patient data–based quality control (PDB‐QC) in the laboratory. Methods: All serum K and serum Mg testing data and quality control (QC) results from February 2022 to January 2023 were collected. After patient outliers were excluded and the data were sorted by test time, the data of the first half‐year period (from February 2022 to July 2022) constituted the training group, and the data of the second half‐year period (from August 2022 to January 2023) constituted the testing group. Average values and standard deviations (SDs) were calculated, and Z ‐score charts were plotted for PDB‐QC and internal quality control (IQC). The Sigma metrics (SMs) of K and Mg were calculated by using the Westgard online frequency calculator, and QC rules were determined according to the SM and the number of patient samples. The PDB‐QC and IQC were compared according to the determined QC rules. Results: The laboratory used the 2 2S and 1 3S rules to judge the systematic and random errors, respectively. PDB‐QC detected systematic errors 5 times and random errors 0 times for K, along with systematic errors 19 times and random errors 17 times for Mg. In comparison, IQC detected systematic errors 4 times and random errors 2 times for K, and systematic errors 1 time and random errors 2 times for Mg. Besides, PDB‐QC was faster than IQC in systematic and random error identification. Conclusion: Compared with IQC, PDB‐QC was capable of identifying more errors (except for random errors in K) with faster identification of both systematic and random errors for K and Mg. So, PDB‐QC is an effective means of monitoring the measurement status and improving laboratory quality management and can serve as an effective supplement to IQC.