RÉSUMÉ
OBJECTIVE: To establish an applicable and highly sensitive patient-based real-time quality control (PBRTQC) program based on a data model constructed with patients' results of a procalcitonin point-of-care testing (POCT) analyzer. METHODS: Patients' results were retrospectively collected within one year. The Excel software was used to establish quality control (QC) programs of the moving average (MA) and the moving rate of positive results (MR). A Monte Carlo simulation was used to introduce positive and negative biases between 0.01 and 1 ng/ml at random points of the testing data set. Different parameters were used to detect the biases, and the detection efficiency was expressed using the median number of patient samples affected until error detection (MNPed). After comparing the MNPeds of different programs, MA and MR programs with appropriate parameters were selected, and validation plots were generated using MNPeds and maximum number of the patient samples affected (MAX). ß curves were generated using the power function of the programs, the performances were compared with that of the conventional QC program. RESULTS: Neither the conventional QC nor MA program was sensitive to small bias, While MR program can detect the minimum positive bias of 0.06 ng/ml and negative of 0.4 ng/ml at an average daily run size of 10 specimens, with FRs < 1.0%, ßs < 1%. CONCLUSION: The MR program, which is more sensitive to small biases than conventional QC and MA programs, with low FR and ß. As such, it can be used as a PBRTQC program with high performance.
