Journal:Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory

From LIMSWiki
Revision as of 16:25, 1 October 2019 by Shawndouglas (talk | contribs) (Saving and adding more.)
Jump to navigationJump to search
Full article title Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory
Journal BMC Medical Informatics and Decision Making
Author(s) Wang, Zhongqing; Peng, Cheng; Kang, Hui; Fan, Xia; Mu, Runqing; Zhou, Liping, He, Miao; Qu, Bo
Author affiliation(s) China Medical University, Affiliated Hospitals of China Medical University
Primary contact Online contact form
Year published 2019
Volume and issue 19(1)
Page(s) 123
DOI 10.1186/s12911-019-0848-2
ISSN 1472-6947
Distribution license Creative Commons Attribution 4.0 International
Website https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-0848-2
Download https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-019-0848-2 (PDF)
Emblem-important-yellow.svg This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.

Abstract

Background: An autoverification system for coagulation consists of a series of rules that allows normal data to be released without manual verification. With new advances in medical informatics, the laboratory information system (LIS) has growing potential for the use of autoverification, allowing rapid and accurate verification of clinical laboratory tests. The purpose of the study is to develop and evaluate a LIS-based autoverification system for validation and efficiency.

Methods: Autoverification decision rules—including quality control, analytical error flag, critical value, limited range check, delta check, and logical check rules, as well as patient’s historical information—were integrated into the LIS. Autoverification limit ranges was constructed based on 5 and 95% percentiles. The four most commonly used coagulation assays—prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG)—were followed by the autoverification protocols. The validation was assessed using characteristics such as autoverification passing rate, the true-positive cases, the true-negative cases, the false-positive cases, the false-negative cases, the sensitivity and the specificity. Efficiency was evaluated by turnaround time (TAT).

Results: A total of 157,079 historical test results of coagulation profiles from January 2016 to December 2016 were collected to determine the distribution intervals. The autoverification passing rate was 77.11% (29,165 / 37,821) based on historical patient data. In the initial test of the autoverification version in June 2017, the overall autoverification passing rate for the whole sample was 78.75% (11,257 / 14,295), with 892 true-positive cases, 11,257 true-negative cases, 2,146 false-positive cases, no false-negative cases, sensitivity of 100% ,and specificity of 83.99%. After formal implementation of the autoverification system for six months, 83,699 samples were assessed. The average overall autoverification passing rate for the whole sample was 78.86%, and the 95% confidence interval (CI) of the passing rate was [78.25, 79.59%]. TAT was reduced from 126 minutes to 101 minutes, which was statistically significant (P < 0.001, Mann-Whitney U test).

Conclusions: The LIS-based autoverification system for coagulation assays can halt the samples with abnormal values for manual verification, guarantee medical safety, minimize the requirements for manual work, shorten TAT, and raise working efficiency.

Keywords: laboratory information systems, medical safety, autoverification, coagulation, turnaround time

Background

Following the analytical phase, a large number of manual verifications are performed in clinical laboratories to detect possible errors before results are released to electronic health records (EHRs), which is time-consuming.[1] A significant solution for this issue may be autoverification, a process which uses a set of well-designed rules to identify and flag samples with abnormal values for manual verification, at the same time permitting those with normal values to be released without manual intervention.[2] Previous reports have demonstrated that autoverification can ensure medical safety[3], shorten turnaround time (TAT)[2][3][4][5], reduce labor requirements[2][3][4], improve operational efficiency[2][4][5][6] and minimize error rate[2], as well as enable laboratory technologists to devote more attention to test results that have greater potential for error.[2]

Until recently, those autoverification systems were commonly developed via third-party commercial software or middleware, which are costly, and the autoverification decision rules were proprietary, such that no revision could be made according to user requirements.[7][8][9][10] In addition, they could not connect with a hospital information system (HIS) and obtain comprehensive clinical data, such as a patient’s history and clinical diagnosis. With the rapid progress of laboratory automation today, it is challenging to achieve interconnection and intercommunication between analytical instruments and the laboratory information system (LIS) with the goal of designing laboratory-focused autoverification systems independent of any commercial software.

Coagulation assays are essential for the assessment of patients requiring acute care[11], patients undergoing anticoagulant therapy[12], thrombolytic therapy[13], and pregnancy[14], as well as for the monitoring of disseminated intravascular coagulation.[15] In many laboratories, coagulation assays are currently still released by manual review, verification, and release, and reports about autoverification in coagulation are scarce.[16] The four most routinely used coagulation assays in our laboratory—namely, prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG)—are commonly prescribed together. As such, an urgent need to automate these assays exists, yet it is still a challenge to establish autoverification decision rules in coagulation that take advantage of quality control (QC) checks, instrument error flags, critical value warnings, limited range checks, delta checks, logical rule requirements, and patients' historical data, all while referencing the Clinical Laboratory Standards Institute (CLSI) guidelines for Autoverification of Clinical Laboratory Test Results (AUTO 10-A).[17] This research extends our knowledge to establish a laboratory-specific autoverification system for coagulation assays based on LIS to ensure medical safety and shorten turnaround time (TAT). Moreover, it is the first study based on clinical large-scale data to evaluate the validation and efficiency of autoverification in terms of specificity and efficiency.


References

  1. ↑ Panteghini, M. (2004). "The future of laboratory medicine: understanding the new pressures". The Clinical Biochemist Reviews 25 (4): 207–15. PMC PMC1934959. PMID 18458714. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1934959. 
  2. ↑ 2.0 2.1 2.2 2.3 2.4 2.5 Torke, N.; Boral, L.; Nguyen, T. et al. (2005). "Process improvement and operational efficiency through test result autoverification". Clinical Chemistry 51 (12): 2406–8. doi:10.1373/clinchem.2005.054395. PMID 16306113. 
  3. ↑ 3.0 3.1 3.2 Wu, J.; Pan, M.; Ouyang, H. et al. (2018). "Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory". SLAS Technology 23 (6): 631–40. doi:10.1177/2472630318775311. PMID 29787327. 
  4. ↑ 4.0 4.1 4.2 Li, J.; Cheng, B.; Ouyang, H. et al. (2018). "Designing and evaluating autoverification rules for thyroid function profiles and sex hormone tests". Annals of Clinical Biochemistry 55 (2): 254-263. doi:10.1177/0004563217712291. PMID 28490181. 
  5. ↑ 5.0 5.1 Sediq, A.M.; Abdel-Azeez, A.G. (2014). "Designing an autoverification system in Zagazig University Hospitals Laboratories: Preliminary evaluation on thyroid function profile". Annals of Saudi Medicine 34 (5): 427–32. doi:10.5144/0256-4947.2014.427. PMC PMC6074554. PMID 25827700. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6074554. 
  6. ↑ Randell, E.W.; Short, G.; Lee, N. et al. (2018). "Strategy for 90% autoverification of clinical chemistry and immunoassay test results using six sigma process improvement". Data in Brief 18: 1740-1749. doi:10.1016/j.dib.2018.04.080. PMC PMC5998219. PMID 29904674. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998219. 
  7. ↑ Jones, J.B. (2013). "A strategic informatics approach to autoverification". Clinics in Laboratory Medicine 33 (1): 161–81. doi:10.1016/j.cll.2012.11.004. PMID 23331736. 
  8. ↑ Randell, E.W.; Short, G.; Lee, N. et al. (2018). "Autoverification process improvement by Six Sigma approach: Clinical chemistry & immunoassay". Clinical Biochemistry 55: 42–48. doi:10.1016/j.clinbiochem.2018.03.002. PMID 29518383. 
  9. ↑ Gómez-Rioja, R.; Alvarez, V.; Ventura, M. et al. (2013). "Current status of verification practices in clinical biochemistry in Spain". Clinical Chemistry and Laboratory Medicine 51: 1739-46. doi:10.1515/cclm-2012-0659. PMID 23612663. 
  10. ↑ Li, J.; Cheng, B.; Yang, L. et al. (2016). "Development and Implementation of Autoverification Rules for ELISA Results of HBV Serological Markers". Journal of Laboratory Automation 21: 642-51. doi:10.1177/2211068215601612. PMID 26311059. 
  11. ↑ Marsden, N.J.; Van, M.; Dean, S. et al. (2017). "Measuring coagulation in burns: An evidence-based systematic review". Scars, Burns & Healing 3: 2059513117728201. doi:10.1177/2059513117728201. PMC PMC5965330. PMID 29799542. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5965330. 
  12. ↑ Barr, D.; Epps, Q.J. (2019). "Direct oral anticoagulants: A review of common medication errors". Journal of Thrombosis and Thrombolysis 47 (1): 146-154. doi:10.1007/s11239-018-1752-9. PMID 30298305. 
  13. ↑ Favaloro, E.J. (2017). "Optimizing the Verification of Mean Normal Prothrombin Time (MNPT) and International Sensitivity Index (ISI) for Accurate Conversion of Prothrombin Time (PT) to International Normalized Ratio (INR)". Methods in Molecular Biology 1646: 59–74. doi:10.1007/978-1-4939-7196-1_4. PMID 28804818. 
  14. ↑ Gutiérrez García, I.; Pérez Cañadas, P.; Martínez Uriarte, J. et al. (2018). "D-dimer during pregnancy: establishing trimester-specific reference intervals". Scandinavian Journal of Clinical and Laboratory Investigation 78 (6): 439–42. doi:10.1080/00365513.2018.1488177. PMID 29975107. 
  15. ↑ Papageorgiou, C.; Jourdi, G.; Adjambri, E. et al. (2018). "Disseminated Intravascular Coagulation: An Update on Pathogenesis, Diagnosis, and Therapeutic Strategies". Clinical and Applied Thromosis/Hemostasis 24 (9): 8S–28S. doi:10.1177/1076029618806424. PMC PMC6710154. PMID 30296833. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6710154. 
  16. ↑ Zhao, Y.; Yang, L.; Zheng, G. et al. (2014). "Building and evaluating the autoverification of coagulation items in the laboratory information system". Clinical Laboratory 60 (1): 143–50. doi:10.1177/1076029618806424. PMID 24600989. 
  17. ↑ "Autoverification of Clinical Laboratory Test Results, 1st Edition". AUTO10. Clinical Laboratory Standards Institute. 31 October 2006. https://clsi.org/standards/products/automation-and-informatics/documents/auto10/. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation and grammar, for clarity. In some cases important information was missing from the references, and that information was added.

Retrieved from "https://www.limswiki.org/index.php?title=Journal:Design_and_evaluation_of_a_LIS-based_autoverification_system_for_coagulation_assays_in_a_core_clinical_laboratory&oldid=36487"