Patient-Based Real-Time Quality Control for Semiquantitative Tests Based on Hierarchical Differences

With developments in artificial intelligence, patient-based real-time quality control (PBRTQC) has advanced. However, its application to semiquantitative tests remains unreported. Taking urine protein (URP), a semiquantitative parameter in routine urinalysis, as an example, we explored the application of PBRTQC in semiquantitative tests.

We assessed the correlation between URP and the quantitative parameter, urine total protein (UTP). Measurement results for both analytes were classified into five grades to calculate hierarchical differences (HDs). Three HD-based algorithms were established: the moving rate of inconsistency (MRI), moving average of HDs (MAHD), and moving average of absolute HDs (MAAD). Their performance was compared with that of the moving rate of positive results (MRP) using computer simulations to evaluate the detection of systematic errors (SEs) and random errors (REs). Anti-interference capability was evaluated by rearranging the sample sequence.

The MRP method detected SEs but failed to monitor REs. Compared with MRP, HD-based algorithms reduced the median number of patients affected before detection at critical errors by 83.1% for SEs and by 94.2% for REs. MRI, MAHD and MAAD showed significantly improved detection capabilities for SEs and, particularly, REs. Regarding anti-interference, rearranging the sample sequence significantly deteriorated MRP performance, whereas the HD-based algorithms remained unaffected and stable.

The novel HD-based algorithms demonstrate superior error detection and anti-interference capabilities compared with MRP. This study provides an effective PBRTQC strategy for semiquantitative tests by leveraging related quantitative data.