Keywords
Manual verification rules
Intelligent verification
TAT
Submitted : 2024-03-13
Accepted : 2024-03-28
Published : 2024-04-12
Abstract
Objective: To apply and verify the application of intelligent audit rules for urine analysis by Cui et al. Method: A total of 1139 urine samples of hospitalized patients in Tai’an Central Hospital from September 2021 to November 2021 were randomly selected, and all samples were manually microscopic examined after the detection of the UN9000 urine analysis line. The intelligent audit rules (including the microscopic review rules and manual verification rules) were validated based on the manual microscopic examination and manual audit, and the rules were adjusted to apply to our laboratory. The laboratory turnaround time (TAT) before and after the application of intelligent audit rules was compared. Result: The microscopic review rate of intelligent rules was 25.63% (292/1139), the true positive rate, false positive rate, true negative rate, and false negative rate were 27.66% (315/1139), 6.49% (74/1139), 62.34% (710/1139) and 3.51% (40/1139), respectively. The approval consistency rate of manual verification rules was 84.92% (727/856), the approval inconsistency rate was 0% (0/856), the interception consistency rate was 12.61% (108/856), and the interception inconsistency rate was 0% (0/856). Conclusion: The intelligence audit rules for urine analysis by Cui et al. have good clinical applicability in our laboratory.
References
Previtali G, Ravasio R, Seghezzi M, et al., 2017, Performance Evaluation of The New Fully Automated Urine Particle Analyser UF-5000 Compared to The Reference Method of the Fuchs-Rosenthal chamber. Clin Chim Acta, 472: 123–130.
Khejonnit V, Pratumvinit B, Reesukumal K, et al., 2015, Optimal Criteria for Microscopic Review of Urinalysis Following Use of Automated Urine Analyzer. Clin Chim Acta, 439: 1–4.
Du J, Xu J, Wang F, et al., 2015, Establishment and Development of The Personalized Criteria for Microscopic Review Following Multiple Automated Routine Urinalysis Systems. Clin Chim Acta, 444: 221–228.
Wang L, Hao XK, Yang Dagan, et al., 2020, A Multicenter Research on Validation and Improvement of The Intelligent Verification Criteria for Routine Urinalysis. Chin J Lab Med, 43(8): 794–801.
Shang H, Wang YS, Shen ZY, 2015, National Operating Procedures for Clinical Laboratory. People’s Medical Publishing House, 2015: 275–276.
Liu Y, Dan G, Jiang ZY, et al., 2015, Exploring the Value of the Combining with RBC Laser Parameters and UF- 1000i in Diagnosing of Glomerular Hematuria. Sichuan Med, 36(8): 1153–1156.
Wang L, Guo Y, Han J, et al., 2019, Establishment of The Intelligent Verification Criteria for A Routine Urinalysis Analyzer in A Multi-Center Study. Clin Chem Lab Med, 57(12): 1923–1932.
Duan M, Zhao HJ, Wang W, et al., 2018, Suggestions on Validation of Reference Interval of Clinical Test Items. Chinese Journal of Clinical Laboratory Science, 36(3): 204–206.
Wen DM, Zhang XM, Wang WJ, et al., 2018, Establishment and Application of The Auto-verification System in Laboratory Clinical Chemistry and Immunology Laboratory. Chin J Lab Med, 41(2): 141–148.
Li XB, Pu Zhifei, Tao Chunlin, et al., 2018, Establishment and Application of Auto-verification Procedure for Clinical Chemistry Test Results. Chin J Lab Med, 41 (7): 547–553.
Randell EW, Yenice S, Khine Wamono AA, et al., 2019, Auto-verification of Test Results in The Core Clinical Laboratory. Clin Biochem, 73: 11–25.
Palmieri R, Falbo R, Cappellini F, et al., 2018, The Development of Auto-Verification Rules Applied to Urinalysis Performed on the AUTIONMAX-SEDIMAX Platform. Clin Chim Acta, 485: 275–281.
Wongkrajang P, Reesukumal K, Pratumvinit B, 2020, Increased Effectiveness of Urinalysis Testing via The Integration of Automated Instrumentation, The Lean Management Approach, and Auto-verification. J Clin Lab Anal. 34(1): e23029.
Zheng SL, Hao XK, 2011, Design and Application of Report Audit Module for Clinical Urine Analysis. Chin J Lab Med, 34(6): 507–510.
National Health Commission of the People’s Republic of China, 2018, Automatic Verification of Quantitative Test Results in Clinical Laboratories, viewed 25 September, 2018, http://www.nhc.gov.cn/ewebeditor/uploadfile/2018/09/20180925121506686.pdf