Ann Lab Med.  2024 Sep;44(5):385-391. 10.3343/alm.2024.0053.

Next-Generation Patient-Based Real-Time Quality Control Models

• Duan X
• Zhang M
• Liu Y
• Zheng W
• Lim CY
• Kim S
• Loh TP
• Guo W
• Zhou R
• Badrick T
• On behalf of the Patient-Based Real-Time Quality Control Working Group of the Asia Pacific Federation of Clinical Biochemistry and Laboratory Medicine

• KMID: 2559144
• DOI: http://doi.org/10.3343/alm.2024.0053

Abstract

Patient-based real-time QC (PBRTQC) uses patient-derived data to assess assay performance. PBRTQC algorithms have advanced in parallel with developments in computer science and the increased availability of more powerful computers. The uptake of Artificial Intelligence in PBRTQC has been rapid, with many stated advantages over conventional approaches. However, until this review, there has been no critical comparison of these. The PBRTQC algorithms based on moving averages, regression-adjusted real-time QC, neural networks and anomaly detection are described and contrasted. As Artificial Intelligence tools become more available to laboratories, user-friendly and computationally efficient, the major disadvantages, such as complexity and the need for high computing resources, are reduced and become attractive to implement in PBRTQC applications.

Keyword

Artificial intelligence; Machine learning; Patient-based real-time QC; QC

Figure

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  Fig. 1 Summary of three next-generation methods to improve PBRTQC performance. Abbreviations: PBRTQC, patient-based real-time QC; RARTQC, regression-adjusted real-time QC; NN, neural network; SVM, support vector machine; RF, random forest.

Reference

References

1. Badrick T, Cervinski M, Loh TP. 2019; A primer on patient-based quality control techniques. Clin Biochem. 64:1–5. DOI: 10.1016/j.clinbiochem.2018.12.004. PMID: 30550876.

2. 2022. Medical laboratories: requirements for quality and competence. ISO 15189:2022. International Organization for Standardization;DOI: 10.3403/02769708u.

3. Loh TP, Cervinski MA, Katayev A, Bietenbeck A, van Rossum H, Badrick T, et al. 2019; Recommendations for laboratory informatics specifications needed for the application of patient-based real time quality control. Clin Chim Acta. 495:625–9. DOI: 10.1016/j.cca.2019.06.009. PMID: 31194967.

4. Badrick T, Loh TP. 2023; Knowledge, attitude, and practice of patient-based real-time quality control in Australasia. J Lab Precis Med. 8:23. DOI: 10.21037/jlpm-23-14.

5. Badrick T, Bietenbeck A, Cervinski MA, Katayev A, van Rossum HH, Loh TP, et al. 2019; Patient-based real-time quality control: review and recommendations. Clin Chem. 65:962–71. DOI: 10.1373/clinchem.2019.305482. PMID: 31263035.

6. Jones GRD. 2016; Average of delta: a new quality control tool for clinical laboratories. Ann Clin Biochem. 53:133–40. DOI: 10.1177/0004563215581400. PMID: 25800407.

7. Lim CY, Badrick T, Loh TP. 2020; Patient-based quality control for glucometers: using the moving sum of positive patient results and moving average. Biochem Med (Zagreb). 30:020709. DOI: 10.11613/BM.2020.020709. PMID: 32550817. PMCID: PMC7271757.

8. Liu J, Tan CH, Badrick T, Loh TP. 2018; Moving standard deviation and moving sum of outliers as quality tools for monitoring analytical precision. Clin Biochem. 52:112–6. DOI: 10.1016/j.clinbiochem.2017.10.009. PMID: 29107011.

9. Bietenbeck A, Cervinski MA, Katayev A, Loh TP, van Rossum HH, Badrick T. 2020; Understanding patient-based real-time quality control using simulation modeling. Clin Chem. 66:1072–83. DOI: 10.1093/clinchem/hvaa094. PMID: 32637994.

10. Zhou Q, Loh TP, Badrick T, Lim CY. 2021; Impact of combining data from multiple instruments on performance of patient-based real-time quality control. Biochem Med (Zagreb). 31:020705. DOI: 10.11613/BM.2021.020705. PMID: 33927555. PMCID: PMC8047783.

11. Loh TP, Bietenbeck A, Cervinski MA, van Rossum HH, Katayev A, Badrick T, et al. 2020; Recommendation for performance verification of patient-based real-time quality control. Clin Chem Lab Med. 58:1205–13. DOI: 10.1515/cclm-2019-1024. PMID: 32049646.

12. Zhou R, Wang W, Padoan A, Wang Z, Feng X, Han Z, et al. 2022; Traceable machine learning real-time quality control based on patient data. Clin Chem Lab Med. 60:1998–2004. DOI: 10.1515/cclm-2022-0548. PMID: 35852126.

13. Zhou R, Liang YF, Cheng HL, Padoan A, Wang Z, Feng X, et al. 2022; A multi-model fusion algorithm as a real-time quality control tool for small shift detection. Comput Biol Med. 148:105866. DOI: 10.1016/j.compbiomed.2022.105866. PMID: 35849951.

14. Man D, Mu R, Zhang K, Zhou Z, Kang H. 2023; Patient-based pre-classified real-time quality control (PCRTQC). Clin Chim Acta. 549:117562. DOI: 10.1016/j.cca.2023.117562. PMID: 37722577.

15. Liang YF, Padoan A, Wang Z, Chen C, Wang QT, Plebani M, et al. 2023; Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study. Clin Chem Lab Med. 62:635–45. DOI: 10.1515/cclm-2023-0964. PMID: 37982680.

16. Cervinski MA. 2021; Pushing patient-based quality control forward through regression. Clin Chem. 67:1299–300. DOI: 10.1093/clinchem/hvab155. PMID: 34487154.

17. Qiu P. 2013. Introduction to statistical process control. 1st ed. Chapman and Hall/CRC;New York: DOI: 10.1201/b15016.

18. Ng D, Polito FA, Cervinski MA. 2016; Optimization of a moving averages program using a simulated annealing algorithm: the goal is to monitor the process not the patients. Clin Chem. 62:1361–71. DOI: 10.1373/clinchem.2016.257055. PMID: 27540031.

19. Duan X, Wang B, Zhu J, Zhang C, Jiang W, Zhou J, et al. 2021; Regression-adjusted real-time quality control. Clin Chem. 67:1342–50. DOI: 10.1093/clinchem/hvab115. PMID: 34355737.

20. Alwan LC, Bissell MG. 1988; Time series modeling for quality control in clinical chemistry. Clin Chem. 34:1396–406. DOI: 10.1093/clinchem/34.7.1396. PMID: 3390910.

21. Hamilton JD. 2020. Time series analysis. Princeton University Press;New Jersey:

22. Somnay YR, Craven M, McCoy KL, Carty SE, Wang TS, Greenberg CC, et al. 2017; Improving diagnostic recognition of primary hyperparathyroidism with machine learning. Surgery. 161:1113–21. DOI: 10.1016/j.surg.2016.09.044. PMID: 27989606. PMCID: PMC5367958.

23. Weiß CH, Lu Z. 2015; SPC methods for time-dependent processes of counts-A literature review. Cogent Math. 2:111116. DOI: 10.1080/23311835.2015.1111116.

24. Tran PH, Ahmadi Nadi A, Nguyen TH, Tran KD, Tran KP. Tran KP, editor. 2022. Application of machine learning in statistical process control charts: a survey and perspective. Control charts and machine learning for anomaly detection in manufacturing. Springer;Cham: p. 7–42. DOI: 10.1007/978-3-030-83819-5_2. PMCID: PMC9633741.

25. Alwan LC, Roberts HV. 1988; Time-series modeling for statistical process control. J Bus Econ Stat. 6:87–95. DOI: 10.1080/07350015.1988.10509640.

26. Makridakis S, Spiliotis E, Assimakopoulos V. 2018; Statistical and machine learning forecasting methods: concerns and ways forward. PLOS ONE. 13:e0194889. DOI: 10.1371/journal.pone.0194889. PMID: 29584784. PMCID: PMC5870978. PMID: d8c8193e8a234c1a826a807560407178.

27. Han Z, Zhao J, Leung H, Ma KF, Wang W. 2021; A review of deep learning models for time series prediction. IEEE Sens J. 21:7833–48. DOI: 10.1109/JSEN.2019.2923982.

28. Li XR, Ban XJ, Yuan ZL, Qiao HR. 2022; Review on deep learning models time series forecasting in industry. Chin J Eng. 44:757–66.

29. Pirani M, Thakkar P, Jivrani P, Bohara MH, Garg D. 2022. A comparative analysis of Arima, GRU, LSTM and BiLSTM on financial time series forecasting. In : 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE); Ballari, India. p. 1–6. DOI: 10.1109/ICDCECE53908.2022.9793213.

30. Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, et al. 2021; Informer: beyond efficient transformer for long sequence time-series forecasting. AAAI. 35:11106–15. DOI: 10.1609/aaai.v35i12.17325.

31. Pang G, Shen C, Cao L, Van Den Hengel A. 2022; Deep learning for anomaly detection: a review. ACM Comput Surv. 54:1–38. DOI: 10.1145/3439950.

32. Kong J, Kowalczyk W, Menzel S, Bäck T. 2020. Improving imbalanced classification by anomaly detection. Parallel Problem Solving from Nature-PPSN XVI. Lecture Note in Computer Science, 12269. Springer;Cham: DOI: 10.1007/978-3-030-58112-1_35.

33. Akcay S, Atapour-Abarghouei A, Breckon TP. GANomaly: semi-supervised anomaly detection via adversarial training. In : Computer Vision-ACCV 2018: 14th Asian Conference on Computer Vision; December 2-6, 2018; Perth, Australia. p. Revised Selected Papers, part III: 622–37. DOI: 10.1007/978-3-030-20893-6_39.

34. Kim JM, Ha ID. 2022; Deep learning-based residual control chart for count data. Qual Eng. 34:370–81. DOI: 10.1080/08982112.2022.2044049.

35. Badrick T, Brown AS. 2023; Identifying human factors as a source of error in laboratory quality control. J Lab Precis Med. 8:16. DOI: 10.21037/jlpm-23-7.

36. Duan X, Wang B, Zhu J, Shao W, Wang H, Shen J, et al. 2020; Assessment of patient-based real-time quality control algorithm performance on different types of analytical error. Clin Chim Acta. 511:329–35. DOI: 10.1016/j.cca.2020.10.006. PMID: 33127347.