Advertisement

Establishing and evaluating an auto-verification system of thalassemia gene detection results

  • Xiaozhe Lin
  • Bizhen Cheng
  • Yingmu Cai
  • Xiaoyang Jiao
  • Xinran Yang
  • Qiaoxin ZhangEmail author
  • Yongni Wang
Original Article
  • 22 Downloads

Abstract

The manual verification of gene tests is time-consuming and error prone. In this study, we try to explore a high-efficiency, clinically useful auto-verification system for gene detection of thalassemia. A series of verification elements were rooted in the auto-verification system. Consistency check was applied initially as one of the essential elements in our study. One hundred twenty-four archived cases were used to choose the consistency-check rules’ indices from routine blood examination and hemoglobin electrophoresis by the receiver operating characteristic curves. Rule 1 and rule 2 established by the chosen indices were compared by their passing rate, consistency with manual validation, and error rate. Finally, 748 cases were used for verifying the system’s feasibility by evaluating the passing rate, turn-around time (TAT), and error rate. The rule 2 had a higher passing rate (67.7% vs. 50.8%) and consistency (0.623 vs. 0.364) than the rule 1 with an error rate of zero. In a “live” valuation, the auto-verification system can reduce the TAT and error rate of verification by 51.5% and 0.13%, respectively, with a high passing rate of 82.8%. The auto-verification system for gene detection of thalassemia in this study can shorten the validation time, reduce errors, and enhance efficiency.

Keywords

Auto-verification system Thalassemia Consistency check Gene detection 

Notes

Acknowledgements

We are grateful to Yongxin Qiu, from Beckman Coulter Inc. and Gaozhe Zheng, the senior technicians in the Clinical Chemistry Core Laboratory, for computer and DM2 technical support. Additionally, we thank Wei Li and Nuan Chen, the senior clinical laboratory physicians in the Clinical Gene Laboratory, for result verification.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics statement

This study was performed under the Institutional Review Board approvals from The First Affiliated Hospital of Shantou University Medical College and conducted in accordance with the Declaration of Helsinki. Written informed consents had been obtained from all patients and controls.

References

  1. 1.
    Yang Y, Zhang J (2017) Research progress on thalassemia in southern China—review. Zhongguo shi yan xue ye xue za zhi 25(1):276–280Google Scholar
  2. 2.
    Li B, Zhang XZ, Yin AH, Zhao QG, Wu L, Ma YZ, Luo MY, Yu SY (2014) High prevalence of thalassemia in migrant populations in Guangdong Province, China. BMC Public Health 14:905CrossRefGoogle Scholar
  3. 3.
    Muncie HL Jr, Campbell J (2009) Alpha and beta thalassemia. Am Fam Physician 80(4):339–344Google Scholar
  4. 4.
    Galanello R, Origa R (2010) Beta-thalassemia. Orphanet J Rare Dis 5:11CrossRefGoogle Scholar
  5. 5.
    Helmi N, Bashir M, Shireen A, Ahmed IM (2017) Thalassemia review: features, dental considerations and management. Electronic Phys 9(3):4003–4008CrossRefGoogle Scholar
  6. 6.
    Sediq AM, Abdel-Azeez AG (2014) Designing an autoverification system in Zagazig University Hospitals Laboratories: preliminary evaluation on thyroid function profile. Ann Saudi Med 34(5):427–432CrossRefGoogle Scholar
  7. 7.
    Krasowski MD, Davis SR, Drees D et al (2014) Autoverification in a core clinical chemistry laboratory at an academic medical center. J Pathol Inform 5(1):13CrossRefGoogle Scholar
  8. 8.
    Onelov L, Gustafsson E, Gronlund E et al (2016) Autoverification of routine coagulation assays in a multi-center laboratory. Scand J Clin Lab Invest 76(6):500–502CrossRefGoogle Scholar
  9. 9.
    Guidi GC, Poli G, Bassi A, Giobelli L, Benetollo PP, Lippi G (2009) Development and implementation of an automatic system for verification, validation and delivery of laboratory test results. Clin Chem Lab Med 47(11):1355–1360CrossRefGoogle Scholar
  10. 10.
    Zhao Y, Yang L, Zheng G, Cai Y (2014) Building and evaluating the autoverification of coagulation items in the laboratory information system. Clin Lab 60(1):143–150Google Scholar
  11. 11.
    Li J, Cheng B, Yang L, Zhao Y, Pan M, Zheng G, Xu X, Hu J, Xiao T, Cai Y (2016) Development and implementation of autoverification rules for ELISA results of HBV serological markers. J Lab Autom 21(5):642–651CrossRefGoogle Scholar
  12. 12.
    Crolla LJ, Westgard JO (2003) Evaluation of rule-based autoverification protocols. Clin Leadersh Manag Rev: the journal of CLMA 17(5):268–272Google Scholar
  13. 13.
    Yin A, Li B, Luo M, Xu L, Wu L, Zhang L, Ma Y, Chen T, Gao S, Liang J, Guo H, Qin D, Wang J, Yuan T, Wang Y, Huang WW, He WF, Zhang Y, Liu C, Xia S, Chen Q, Zhao Q, Zhang X (2014) The prevalence and molecular spectrum of alpha- and beta-globin gene mutations in 14,332 families of Guangdong Province, China. PLoS One 9(2):e89855CrossRefGoogle Scholar
  14. 14.
    Xu XM (2004) The prevalence and spectrum of and thalassaemia in Guangdong Province: implications for the future health burden and population screening. J Clin Pathol 57(5):517–522CrossRefGoogle Scholar
  15. 15.
    Cappellini MD. The thalassemias. Goldman-Cecil Medicine 1089–1095Google Scholar
  16. 16.
    Ovens K, Naugler C (2012) How useful are delta checks in the 21 century? A stochastic-dynamic model of specimen mix-up and detection. J Pathol Inform 3:5CrossRefGoogle Scholar
  17. 17.
    Iizuka Y, Kume H, Kitamura M (1982) Multivariate delta check method for detecting specimen mix-up. Clin Chem 28(11):2244–2248Google Scholar
  18. 18.
    Brancaleoni V, Di Pierro E, Motta I, Cappellini MD (2016) Laboratory diagnosis of thalassemia. Int J Lab Hematol 38(Suppl 1):32–40CrossRefGoogle Scholar
  19. 19.
    Langlois S, Ford JC, Chitayat D, Chitayat D, Désilets VA, Farrell SA, Geraghty M, Langlois S, Nelson T, Nikkel SM, Shugar A, Skidmore D, Allen VM, Audibert F, Blight C, Désilets VA, Gagnon A, Johnson JA, Langlois S, Douglas Wilson R, Wyatt P (2008) Carrier screening for thalassemia and hemoglobinopathies in Canada. J Obstet Gynaecol Canada: JOGC = Journal d'obstetrique et gynecologie du Canada : JOGC 30(10):950–971CrossRefGoogle Scholar
  20. 20.
    Hoffmann JJ, Urrechaga E, Aguirre U (2015) Discriminant indices for distinguishing thalassemia and iron deficiency in patients with microcytic anemia: a meta-analysis. Clin Chem Lab Med 53(12):1883–1894Google Scholar
  21. 21.
    Tong L, Kauer J, Wachsmann-Hogiu S, Chu K, Dou H, Smith ZJ (2017) A new red cell index and portable RBC analyzer for screening of iron deficiency and Thalassemia minor in a Chinese population. Sci Rep 7(1):10510CrossRefGoogle Scholar
  22. 22.
    Shen C, Jiang YM, Shi H, Liu JH, Zhou WJ, Dai QK, Yang H (2010) Evaluation of indices in differentiation between iron deficiency anemia and beta-thalassemia trait for Chinese children. J Pediatr Hematol Oncol 32(6):e218–e222CrossRefGoogle Scholar
  23. 23.
    Sabath DE (2017) Molecular diagnosis of thalassemias and hemoglobinopathies: an ACLPS critical review. Am J Clin Pathol 148(1):6–15CrossRefGoogle Scholar
  24. 24.
    Miri-Moghaddam E, Sargolzaie N (2014) Cut off determination of discrimination indices in differential diagnosis between Iron deficiency anemia and beta- thalassemia minor. Int J Hematol-Oncol Stem Cell Res 8(2):27–32Google Scholar
  25. 25.
    Jameel T, Baig M, Ahmed I, Hussain MB, Alkhamaly MBD (2017) Differentiation of beta thalassemia trait from iron deficiency anemia by hematological indices. Pakistan J Med Sci 33(3):665–669Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Department of Clinical LaboratoryThe First Affiliated Hospital of Shantou University Medical College, Shantou UniversityShantouChina
  2. 2.Cell Biology and Genetics Department of Shantou University Medical CollegeShantouChina

Personalised recommendations