Abstract
Background
Although laboratory information system (LIS) is widely used nowadays, the results of routine urinalysis still need 100% manual verification. We established intelligent verification criteria to perform the automated verification process and reduce manual labor.
Methods
A total of 4610 urine specimens were obtained from the patients of three hospitals in Beijing, China. Firstly, 895 specimens were measured to establish the reference intervals of formed-element parameters in UF5000. Secondly, 2803 specimens were analyzed for setting up the intelligent verification criteria (including the microscopic review rules and manual verification rules). Lastly, 912 specimens were used to verify the efficacy and accuracy of the intelligent verification criteria. Phase-contrast microscopes were used for the microscopic review.
Results
Employing a results level corresponding relationship in specific parameters including hemoglobin (red blood cell [RBC]), leukocyte esterase (white blood cell [WBC]) and protein (cast) between the dry-chemistry analysis and formed-element analysis, as well as instrument flags, we established seven WBC verification rules, eight RBC verification rules and four cast verification rules. Based on the microscopy results, through analyzing the pre-set rules mentioned earlier, we finally determined seven microscopic review rules, nine manual verification rules and three auto-verification rules. The microscopic review rate was 21.98% (616/2803), the false-negative rate was 4.32% (121/2803), the total manual verification rate was 35.71% (1001/2803) and the auto-verification rate was 64.29% (1802/2803). The validation results were consistent.
Conclusions
The intelligent verification criteria for urinary dry-chemistry and urinary formed-element analysis can improve the efficiency of the results verification process and ensure the reliability of the test results.
Funding source: CAMS Innovation Fund for Medical Sciences (CIFMS)
Award Identifier / Grant number: 2017-I2M-3-005
Funding statement: This work was supported by the CAMS Innovation Fund for Medical Sciences (CIFMS) (grant no. 2017-I2M-3-005).
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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