Home Medicine Enhanced patient-based real-time quality control using the graph-based anomaly detection
Article
Licensed
Unlicensed Requires Authentication

Enhanced patient-based real-time quality control using the graph-based anomaly detection

  • , , , , ORCID logo , EMAIL logo , EMAIL logo and EMAIL logo
Published/Copyright: May 16, 2024
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 12

Abstract

Objectives

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control.

Methods

The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison.

Results

The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example.

Conclusions

The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Keywords: patient-based real-time quality control; anomaly detection; graph neural network; statistical process control
Corresponding authors: Yanwei Hu, Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China, E-mail: ; and Qingtao Wang and Rui Zhou, Department of Laboratory Medicine, Beijing Chao-yang Hospital, Capital Medical University, Beijing, P.R. China; and Beijing Center for Clinical Laboratories, No. 8 Gongti South Street, Chaoyang District, Beijing, P.R. China, E-mail: (Q. Wang), (R. Zhou)

Funding source: National Natural Science Foundation of China

Award Identifier / Grant number: 72374145

Acknowledgments

We thank the participating colleagues in the department and Laboratory Information System engineers for helping with data collection.

  1. Research ethics: This study was approved by the Medical Ethical Committee of Beijing Chaoyang Hospital (2021-D-51).

  2. Informed consent: Not applicable.

  3. Author contributions: Xueling Shang: methodology, investigation, formal analysis, writing – original draft, visualization. Minglong Zhang: methodology, investigation, formal analysis, software. Dehui Sun: methodology, software, resources. Yufang Liang: data curation, resources. Tong Badrick: writing – review & editing. Yanwei Hu: conceptualization, writing – review & editing, resources, supervision. Qingtao Wang: conceptualization, methodology, supervision, funding acquisition. Rui Zhou: conceptualization, methodology, writing – review & editing, supervision, funding acquisition. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors declare no conflict of interest.

  5. Research funding: This work was supported by the National Natural Science Foundation of China (72374145).

  6. Data availability: The data are not publicly available.

References

1. Loh, TP, Bietenbeck, A, Cervinski, MA, van Rossum, HH, Katayev, A, Badrick, T, et al.. Recommendation for performance verification of patient-based real-time quality control. Clin Chem Lab Med 2020;58:1205–13. https://doi.org/10.1515/cclm-2019-1024.Search in Google Scholar PubMed

2. Anhoj, J, Wentzel-Larsen, T. Sense and sensibility: on the diagnostic value of control chart rules for detection of shifts in time series data. BMC Med Res Methodol 2018;18:100. https://doi.org/10.1186/s12874-018-0564-0.Search in Google Scholar PubMed PubMed Central

3. Abbasi, SA, Yeganeh, A, Shongwe, SC. Monitoring non-parametric profiles using adaptive EWMA control chart. Sci Rep 2022;12:14336. https://doi.org/10.1038/s41598-022-18381-8.Search in Google Scholar PubMed PubMed Central

4. Westgard, JO. Internal quality control: planning and implementation strategies. Ann Clin Biochem 2003;40:593–611. https://doi.org/10.1258/000456303770367199.Search in Google Scholar PubMed

5. Ng, D, Polito, FA, Cervinski, MA. Optimization of a moving averages program using a simulated annealing algorithm: the goal is to monitor the process not the patients. Clin Chem 2016;62:1361–71. https://doi.org/10.1373/clinchem.2016.257055.Search in Google Scholar PubMed

6. Badrick, T, Bietenbeck, A, Cervinski, MA, Katayev, A, van Rossum, HH, Loh, TP, et al.. Patient-based real-time quality control: review and recommendations. Clin Chem 2019;65:962–71. https://doi.org/10.1373/clinchem.2019.305482.Search in Google Scholar PubMed

7. Loh, TP, Cervinski, MA, Katayev, A, Bietenbeck, A, van Rossum, H, Badrick, T. Recommendations for laboratory informatics specifications needed for the application of patient-based real time quality control. Clin Chim Acta 2019;495:625–9. https://doi.org/10.1016/j.cca.2019.06.009.Search in Google Scholar PubMed

8. Badrick, T, Bietenbeck, A, Katayev, A, van Rossum, HH, Loh, TP, Cervinski, MA, et al.. Implementation of patient-based real-time quality control. Crit Rev Clin Lab Sci 2020;57:532–47. https://doi.org/10.1080/10408363.2020.1765731.Search in Google Scholar PubMed

9. Liu, J, Tan, CH, Badrick, T, Loh, TP. Moving sum of number of positive patient result as a quality control tool. Clin Chem Lab Med 2017;55:1709–14. https://doi.org/10.1515/cclm-2016-0950.Search in Google Scholar PubMed

10. Howanitz, PJ, Tetrault, GA, Steindel, SJ. Clinical laboratory quality control: a costly process now out of control. Clin Chim Acta 1997;260:163–74. https://doi.org/10.1016/s0009-8981(96)06494-7.Search in Google Scholar PubMed

11. Miller, WG, Erek, A, Cunningham, TD, Oladipo, O, Scott, MG, Johnson, RE. Commutability limitations influence quality control results with different reagent lots. Clin Chem 2011;57:76–83. https://doi.org/10.1373/clinchem.2010.148106.Search in Google Scholar PubMed

12. Bietenbeck, A, Cervinski, MA, Katayev, A, Loh, TP, van Rossum, HH, Badrick, T. Understanding patient-based real-time quality control using simulation modeling. Clin Chem 2020;66:1072–83. https://doi.org/10.1093/clinchem/hvaa094.Search in Google Scholar PubMed

13. Duan, X, Wang, B, Zhu, J, Zhang, C, Jiang, W, Zhou, J, et al.. Regression-adjusted real-time quality control. Clin Chem 2021;67:1342–50. https://doi.org/10.1093/clinchem/hvab115.Search in Google Scholar PubMed

14. Man, D, Mu, R, Zhang, K, Zhou, Z, Kang, H. Patient-based pre-classified real-time quality control (PCRTQC). Clin Chim Acta 2023;549:117562. https://doi.org/10.1016/j.cca.2023.117562.Search in Google Scholar PubMed

15. Li, Y, Chen, X, Zhao, Y. The effect of ratios upon improving patient-based real-time quality control (PBRTQC) performance. Clin Chem Lab Med 2023;62:646–56. https://doi.org/10.1515/cclm-2023-0865.Search in Google Scholar PubMed

16. Zhou, R, Wang, W, Padoan, A, Wang, Z, Feng, X, Han, Z, et al.. Traceable machine learning real-time quality control based on patient data. Clin Chem Lab Med 2022;60:1998–2004. https://doi.org/10.1515/cclm-2022-0548.Search in Google Scholar PubMed

17. van Rossum, HH. Moving average quality control: principles, practical application and future perspectives. Clin Chem Lab Med 2019;57:773–82. https://doi.org/10.1515/cclm-2018-0795.Search in Google Scholar PubMed

18. Cembrowski, GS, Chandler, EP, Westgard, JO. Assessment of “Average of Normals” quality control procedures and guidelines for implementation. Am J Clin Pathol 1984;81:492–9. https://doi.org/10.1093/ajcp/81.4.492.Search in Google Scholar PubMed

19. Johnson, JM, Khoshgoftaar, TM. Survey on deep learning with class imbalance. Journal of Big Data 2019;6:27. https://doi.org/10.1186/s40537-019-0192-5.Search in Google Scholar

20. Chalapathy, R, Chawla, S. Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 2019. https://doi.org/10.48550/arXiv.1901.03407.Search in Google Scholar

21. Chandola, V, Banerjee, A, Kumar, V. Anomaly detection: a survey. ACM computing surveys (CSUR) 2009;41:1–58. https://doi.org/10.1145/1541880.1541882.Search in Google Scholar

22. Xie, X, Wang, C, Chen, S, Shi, G, Zhao, Z. Real-time illegal parking detection System based on deep learning. Proceedings of the 2017 international conference on deep learning technologies 2017;23–7. https://doi.org/10.1145/3094243.3094261.Search in Google Scholar

23. Schlegl, T, Seeböck, P, Waldstein, SM, Schmidt-Erfurth, U, Langs, G. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. arXiv preprint arXiv:1703.05921 2017. https://doi.org/10.48550/arXiv.1703.05921.Search in Google Scholar

24. Mohammadi, M, Al-Fuqaha, A, Sorour, S, Guizani, M. Deep learning for IoT big data and streaming analytics: a survey. IEEE Commun. Surv. Tutor. 2018;20:2923–60. https://doi.org/10.1109/comst.2018.2844341.Search in Google Scholar

25. Badrick, T, Loh, TP. Developing an evidence-based approach to quality control. Clin Biochem 2023;114:39–42. https://doi.org/10.1016/j.clinbiochem.2023.01.011.Search in Google Scholar PubMed

26. Akcay, S, Atapour-Abarghouei, A, Breckon, TP. Ganomaly: semi-supervised anomaly detection via adversarial training. arXiv preprint arXiv:1805.06725 2018. https://doi.org/10.48550/arXiv.1805.06725.Search in Google Scholar

27. Liang, YF, Padoan, A, Wang, Z, Chen, C, Wang, QT, Plebani, M, et al.. Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study. Clin Chem Lab Med 2023;62:635–45. https://doi.org/10.1515/cclm-2023-0964.Search in Google Scholar PubMed

28. Zhou, R, Liang, YF, Cheng, HL, Padoan, A, Wang, Z, Feng, X, et al.. A multi-model fusion algorithm as a real-time quality control tool for small shift detection. Comput Biol Med 2022;148:105866. https://doi.org/10.1016/j.compbiomed.2022.105866.Search in Google Scholar PubMed

29. Zhou, R, Liang, YF, Cheng, HL, Wang, W, Huang, DW, Wang, Z, et al.. A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory. Clin Chem Lab Med 2022;60:1984–92. https://doi.org/10.1515/cclm-2021-1171.Search in Google Scholar PubMed

30. Veličković, P, Cucurull, G, Casanova, A, Romero, A, Liò, P, Bengio, Y. Graph attention networks. arXiv preprint arXiv:1710.10903 2018. https://doi.org/10.48550/arXiv.1710.10903.Search in Google Scholar

31. Badrick, T. Biological variation: understanding why it is so important? Pract Lab Med 2021;23:e00199. https://doi.org/10.1016/j.plabm.2020.e00199.Search in Google Scholar PubMed PubMed Central

32. Chen, W, Shang, H, Shen, Z, Wang, Z, Zhang, C, Li, X, et al.. WS/T 403-2012 Analytical quality specifications for routine analytes in clinical biochemistry. National Health Commission of the PRC 2012.Search in Google Scholar

33. Wu, Z, Pan, S, Chen, F, Long, G, Zhang, C, Yu, PS. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 2021;32:4–24. https://doi.org/10.1109/tnnls.2020.2978386.Search in Google Scholar

34. Kim, H, Lee, BS, Shin, W-Y, Lim, S. Graph anomaly detection with graph neural networks: current status and challenges. IEEE Access 2022;10:111820–9. https://doi.org/10.1109/ACCESS.2022.3211306.Search in Google Scholar

35. Deng, A, Hooi, B. Graph neural network-based anomaly detection in multivariate time series. arXiv preprint arXiv:2106.06947 2021. https://doi.org/10.48550/arXiv.2106.06947.10.1609/aaai.v35i5.16523Search in Google Scholar

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2024-0124).

Received: 2024-01-26
Accepted: 2024-05-04
Published Online: 2024-05-16
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. External quality assurance (EQA): navigating between quality and sustainability
  4. Reviews
  5. Molecular allergology: a clinical laboratory tool for precision diagnosis, stratification and follow-up of allergic patients
  6. Nitrous oxide abuse direct measurement for diagnosis and follow-up: update on kinetics and impact on metabolic pathways
  7. Opinion Papers
  8. A vision to the future: value-based laboratory medicine
  9. Point-of-care testing, near-patient testing and patient self-testing: warning points
  10. Navigating the path of reproducibility in microRNA-based biomarker research with ring trials
  11. Point/Counterpoint
  12. Six Sigma – is it time to re-evaluate its value in laboratory medicine?
  13. The value of Sigma-metrics in laboratory medicine
  14. Genetics and Molecular Diagnostics
  15. Analytical validation of the amplification refractory mutation system polymerase chain reaction-capillary electrophoresis assay to diagnose spinal muscular atrophy
  16. Can we identify patients carrying targeted deleterious DPYD variants with plasma uracil and dihydrouracil? A GPCO-RNPGx retrospective analysis
  17. General Clinical Chemistry and Laboratory Medicine
  18. Comparison of ChatGPT, Gemini, and Le Chat with physician interpretations of medical laboratory questions from an online health forum
  19. External quality assessment performance in ten countries: an IFCC global laboratory quality project
  20. Multivariate anomaly detection models enhance identification of errors in routine clinical chemistry testing
  21. Enhanced patient-based real-time quality control using the graph-based anomaly detection
  22. Performance evaluation and user experience of BT-50 transportation unit with automated and scheduled quality control measurements
  23. Stability of steroid hormones in dried blood spots (DBS)
  24. Quantification of C1 inhibitor activity using a chromogenic automated assay: analytical and clinical performances
  25. Reference Values and Biological Variations
  26. Time-dependent characteristics of analytical measurands
  27. Cancer Diagnostics
  28. Expert-level detection of M-proteins in serum protein electrophoresis using machine learning
  29. An automated workflow based on data independent acquisition for practical and high-throughput personalized assay development and minimal residual disease monitoring in multiple myeloma patients
  30. Cardiovascular Diseases
  31. Analytical validation of the Mindray CL1200i analyzer high sensitivity cardiac troponin I assay: MERITnI study
  32. Diabetes
  33. Limitations of glycated albumin standardization when applied to the assessment of diabetes patients
  34. Patient result monitoring of HbA1c shows small seasonal variations and steady decrease over more than 10 years
  35. Letters to the Editor
  36. Inaccurate definition of Bence Jones proteinuria in the EFLM Urinalysis Guideline 2023
  37. Use of the term “Bence-Jones proteinuria” in the EFLM European Urinalysis Guideline 2023
  38. Is uracil enough for effective pre-emptive DPD testing?
  39. Reply to: “Is uracil enough for effective pre-emptive DPD testing?”
  40. Accurate predictory role of monocyte distribution width on short-term outcome in sepsis patients
  41. Reply to: “Accurate predictory role of monocyte distribution width on short-term outcome in sepsis patients”
  42. Spurious parathyroid hormone (PTH) elevation caused by macro-PTH
  43. Setting analytical performance specifications for copeptin-based testing
  44. Serum vitamin B12 levels during chemotherapy against diffuse large B-cell lymphoma: a case report and review of the literature
  45. Evolution of acquired haemoglobin H disease monitored by capillary electrophoresis: a case of a myelofibrotic patient with a novel ATRX mutation
https://doi.org/10.1515/cclm-2024-0124
Keywords for this article
patient-based real-time quality control; anomaly detection; graph neural network; statistical process control

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. External quality assurance (EQA): navigating between quality and sustainability
  4. Reviews
  5. Molecular allergology: a clinical laboratory tool for precision diagnosis, stratification and follow-up of allergic patients
  6. Nitrous oxide abuse direct measurement for diagnosis and follow-up: update on kinetics and impact on metabolic pathways
  7. Opinion Papers
  8. A vision to the future: value-based laboratory medicine
  9. Point-of-care testing, near-patient testing and patient self-testing: warning points
  10. Navigating the path of reproducibility in microRNA-based biomarker research with ring trials
  11. Point/Counterpoint
  12. Six Sigma – is it time to re-evaluate its value in laboratory medicine?
  13. The value of Sigma-metrics in laboratory medicine
  14. Genetics and Molecular Diagnostics
  15. Analytical validation of the amplification refractory mutation system polymerase chain reaction-capillary electrophoresis assay to diagnose spinal muscular atrophy
  16. Can we identify patients carrying targeted deleterious DPYD variants with plasma uracil and dihydrouracil? A GPCO-RNPGx retrospective analysis
  17. General Clinical Chemistry and Laboratory Medicine
  18. Comparison of ChatGPT, Gemini, and Le Chat with physician interpretations of medical laboratory questions from an online health forum
  19. External quality assessment performance in ten countries: an IFCC global laboratory quality project
  20. Multivariate anomaly detection models enhance identification of errors in routine clinical chemistry testing
  21. Enhanced patient-based real-time quality control using the graph-based anomaly detection
  22. Performance evaluation and user experience of BT-50 transportation unit with automated and scheduled quality control measurements
  23. Stability of steroid hormones in dried blood spots (DBS)
  24. Quantification of C1 inhibitor activity using a chromogenic automated assay: analytical and clinical performances
  25. Reference Values and Biological Variations
  26. Time-dependent characteristics of analytical measurands
  27. Cancer Diagnostics
  28. Expert-level detection of M-proteins in serum protein electrophoresis using machine learning
  29. An automated workflow based on data independent acquisition for practical and high-throughput personalized assay development and minimal residual disease monitoring in multiple myeloma patients
  30. Cardiovascular Diseases
  31. Analytical validation of the Mindray CL1200i analyzer high sensitivity cardiac troponin I assay: MERITnI study
  32. Diabetes
  33. Limitations of glycated albumin standardization when applied to the assessment of diabetes patients
  34. Patient result monitoring of HbA1c shows small seasonal variations and steady decrease over more than 10 years
  35. Letters to the Editor
  36. Inaccurate definition of Bence Jones proteinuria in the EFLM Urinalysis Guideline 2023
  37. Use of the term “Bence-Jones proteinuria” in the EFLM European Urinalysis Guideline 2023
  38. Is uracil enough for effective pre-emptive DPD testing?
  39. Reply to: “Is uracil enough for effective pre-emptive DPD testing?”
  40. Accurate predictory role of monocyte distribution width on short-term outcome in sepsis patients
  41. Reply to: “Accurate predictory role of monocyte distribution width on short-term outcome in sepsis patients”
  42. Spurious parathyroid hormone (PTH) elevation caused by macro-PTH
  43. Setting analytical performance specifications for copeptin-based testing
  44. Serum vitamin B12 levels during chemotherapy against diffuse large B-cell lymphoma: a case report and review of the literature
  45. Evolution of acquired haemoglobin H disease monitored by capillary electrophoresis: a case of a myelofibrotic patient with a novel ATRX mutation
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Winner of the OpenAthens UX Award 2026
Winner of the OpenAthens UX Award 2026
Have an idea on how to improve our website?
Please write us.
© 2026 De Gruyter Brill
Downloaded on 1.4.2026 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2024-0124/html
Scroll to top button