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Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

  • Gabriel L. Streun , Andrea E. Steuer , Lars C. Ebert , Akos Dobay and Thomas Kraemer ORCID logo EMAIL logo
Published/Copyright: March 22, 2021
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 59 Issue 8

Abstract

Objectives

Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model.

Methods

Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach.

Results

Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted.

Conclusions

With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Keywords: artificial neural networks; high-resolution mass spectrometry; machine learning; progenesis QI; sample manipulation; urine adulteration
Corresponding author: Prof. Dr. Thomas Kraemer, Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstr. 190/52, 8057Zurich, Switzerland, Phone: +41 44 635 56 41, E-mail:
Akos Dobay and Thomas Kraemer shared last authorship.

Funding source: Emma Louise Kessler Foundation

Acknowledgments

The authors express their gratitude to Emma Louise Kessler, MD for her generous legacy she donated to the Institute of Forensic Medicine at the University of Zurich, Switzerland for research purposes.

  1. Research funding: The Emma Louise Kessler Foundation directly funded Akos Dobay.

  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: According to Swissethics (Humanforschungsgesetz), no further ethical approval from the cantonal ethics commission was necessary as the research was not aiming to investigate diseases or functions of the human body. A statement of the Cantonal Ethics Board of the Canton of Zurich (document BASEC-No. Req-2017-00946 and E-14/2009) was obtained.

  5. Code availability:https://github.com/gbril/urine-adulteration.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2021-0010).

Received: 2021-01-04
Accepted: 2021-03-05
Published Online: 2021-03-22
Published in Print: 2021-07-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

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https://doi.org/10.1515/cclm-2021-0010
Keywords for this article
artificial neural networks; high-resolution mass spectrometry; machine learning; progenesis QI; sample manipulation; urine adulteration

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Kinetics and biological characteristics of humoral response developing after SARS-CoV-2 infection: implications for vaccination
  4. Reviews
  5. The impact of interventions applied in primary care to optimize the use of laboratory tests: a systematic review
  6. Circulating tumor DNA (ctDNA) as a pan-cancer screening test: is it finally on the horizon?
  7. Opinion Paper
  8. Performance specifications for measurement uncertainty of common biochemical measurands according to Milan models
  9. General Clinical Chemistry and Laboratory Medicine
  10. Activity-based cost analysis of laboratory tests in clinical chemistry
  11. Audit of sweat chloride testing reveals analytical errors
  12. Comparison of different algorithms in laboratory diagnosis of alpha1-antitrypsin deficiency
  13. Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry
  14. Prognostic role of Krebs von den Lungen-6 (KL-6) measurement in idiopathic pulmonary fibrosis: a systematic review and meta-analysis
  15. Evaluating chronic kidney disease in rural South Africa: comparing estimated glomerular filtration rate using point-of-care creatinine to iohexol measured GFR
  16. Reference Values and Biological Variations
  17. Pediatric reference intervals for endocrine markers and fertility hormones in healthy children and adolescents on the Siemens Healthineers Atellica immunoassay system
  18. Cardiovascular Diseases
  19. Independent and combined effects of biotin and hemolysis on high-sensitivity cardiac troponin assays
  20. Infectious Diseases
  21. Analytical and clinical performances of a SARS-CoV-2 S-RBD IgG assay: comparison with neutralization titers
  22. Clinical validation of the Siemens quantitative SARS-CoV-2 spike IgG assay (sCOVG) reveals improved sensitivity and a good correlation with virus neutralization titers
  23. Evaluation of the automated LIAISON® SARS-CoV-2 TrimericS IgG assay for the detection of circulating antibodies
  24. Lumipulse G SARS-CoV-2 Ag assay evaluation using clinical samples from different testing groups
  25. Letters to the Editors
  26. The impact of measurement uncertainty on the uncertainty of ordinal medical scores based on continuous quantitative laboratory results
  27. Effect of five different pneumatic tube carrier inserts on mechanical sample stress: a multicentre evaluation
  28. Critical role of pre-analytical aspects for the measurement of circulating calprotectin in serum or plasma as a biomarker for neutrophil-related inflammation
  29. Prospective serological evaluation of anti SARS-CoV-2 IgG and anti S1-RBD antibodies in a community outbreak
  30. Common laboratory tests as indicators of COVID-19 severity on admission at high altitude: a single-center retrospective study in Quito (ECUADOR)
  31. Protease-antiprotease imbalance in patients with severe COVID-19
  32. Iodine containing contrast media and urinary flow cytometry: an unknown interference in automated urine sediment analysis
  33. Prolymphocytic or Richter’s transformation of chronic lymphocytic leukemia?
  34. OGTT reproducibility in adults with impaired fasting glucose is nearly 65% with adoption of Italian SIBioC-SIPMeL recommendations
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