Department of Clinical Laboratory, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, P.R. China.
Department of Laboratory Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, P.R. China.
Clin Chem Lab Med. 2019 Nov 26;57(12):1923-1932. doi: 10.1515/cclm-2019-0344.
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.
虽然实验室信息系统(LIS)现在已经得到广泛应用,但常规尿液分析的结果仍需要 100%进行人工验证。我们建立了智能验证标准来执行自动化验证过程,减少人工劳动。
共收集来自中国北京三家医院的 4610 份尿液标本。首先,测量 895 份标本以建立 UF5000 中形成元素参数的参考区间。其次,分析 2803 份标本以建立智能验证标准(包括显微镜复查规则和手动验证规则)。最后,使用 912 份标本验证智能验证标准的有效性和准确性。使用相差显微镜进行显微镜复查。
在血红蛋白(红细胞 [RBC])、白细胞酯酶(白细胞 [WBC])和蛋白质(管型)等特定参数的干化学分析与形成元素分析之间以及仪器标志之间采用相应的结果水平对应关系,我们建立了七个 WBC 验证规则、八个 RBC 验证规则和四个管型验证规则。基于显微镜结果,通过分析前面提到的预设规则,最终确定了七个显微镜复查规则、九个手动验证规则和三个自动验证规则。显微镜复查率为 21.98%(616/2803),假阴性率为 4.32%(121/2803),总手动验证率为 35.71%(1001/2803),自动验证率为 64.29%(1802/2803)。验证结果一致。
尿液干化学和尿液形成元素分析的智能验证标准可以提高结果验证过程的效率,并确保测试结果的可靠性。