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Using machine learning to identify clotted specimens in coagulation testing

  • Kui Fang , Zheqing Dong EMAIL logo , Xiling Chen , Ji Zhu , Bing Zhang , Jinbiao You , Yingjun Xiao and Wenjin Xia
Published/Copyright: March 3, 2021
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 59 Issue 7

Abstract

Objectives

A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens.

Methods

The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset.

Results

Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958–0.974) and 0.971 (95% CI, 0.9641–0.9784), respectively.

Conclusions

Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.

Keywords: blood clot; coagulation assays; laboratory automation; machine learning; neural network; quality control
Corresponding author: Zheqing Dong, Director, Clinical Laboratory, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou310000, P.R. China, E-mail:

Funding source: Department of Health of Zhejiang Province

Award Identifier / Grant number: 2021KY845

  1. Research funding: This work was supported by the Science Fund of the Health Department of Zhejiang Province. Project ID: 2021KY845.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Ethical approval: The study was approved by the institutional research ethics committee of The Third Affiliated Hospital of Zhejiang Chinese Medical University. Approval ID: ZSLL-KY-2021-001-01.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2021-0081).

Received: 2021-01-18
Accepted: 2021-02-15
Published Online: 2021-03-03
Published in Print: 2021-06-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2021-0081
Keywords for this article
blood clot; coagulation assays; laboratory automation; machine learning; neural network; quality control

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Machine learning and coagulation testing: the next big thing in hemostasis investigations?
  4. Reviews
  5. Updates on liquid biopsy: current trends and future perspectives for clinical application in solid tumors
  6. The underestimated issue of non-reproducible cardiac troponin I and T results: case series and systematic review of the literature
  7. Opinion Paper
  8. Benefits, limitations and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice
  9. Genetics and Molecular Diagnostics
  10. ctDNA from body fluids is an adequate source for EGFR biomarker testing in advanced lung adenocarcinoma
  11. General Clinical Chemistry and Laboratory Medicine
  12. Incidence, characteristics and outcomes among inpatient, outpatient and emergency department with reported high critical serum potassium values
  13. Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey
  14. Integrating quality assurance in autoimmunity: the changing face of the automated ANA IIF test
  15. Plasma thiol/disulphide homeostasis changes in patients with restless legs syndrome
  16. Reference Values and Biological Variations
  17. High-resolution pediatric reference intervals for 15 biochemical analytes described using fractional polynomials
  18. Continuous reference intervals for leukocyte telomere length in children: the method matters
  19. Hematology and Coagulation
  20. Using machine learning to identify clotted specimens in coagulation testing
  21. Cardiovascular Diseases
  22. Long term pronostic value of suPAR in chronic heart failure: reclassification of patients with low MAGGIC score
  23. Infectious Diseases
  24. Monocyte distribution width (MDW) parameter as a sepsis indicator in intensive care units
  25. A low level of CD16pos monocytes in SARS-CoV-2 infected patients is a marker of severity
  26. Thrombin generation in patients with COVID-19 with and without thromboprophylaxis
  27. Corrigendum
  28. Applying the concept of uncertainty to the sFlt-1/PlGF cut-offs for diagnosis and prognosis of preeclampsia
  29. Letters to the Editors
  30. Additional approaches for identifying non-reproducible cardiac troponin results
  31. Paediatric reference intervals for ionised calcium – a data mining approach
  32. A case of interference in testosterone, DHEA-S and progesterone measurements by second generation immunoassays
  33. Lack of cross-reactivity between anti-A IgG isoagglutinins and anti-SARS-CoV-2 IgG antibodies
  34. Artefactual bands on urine protein immunofixation gels
  35. A case of methaemoglobinaemia interference on the WDF channel on Sysmex XN-Series analysers
  36. Soluble fms-like tyrosine kinase-1: a potential early predictor of respiratory failure in COVID-19 patients
  37. Serendipitous detection of α1-antitrypsin deficiency: a single institution’s experience over a 32 month period
  38. The activated partial thromboplastin time may not reveal even severe fibrinogen deficiency
  39. Influence of C-reactive protein on thrombin generation assay
  40. Inappropriate extrapolations abound in fecal microbiota research
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