Home Design of an algorithm for the detection of intravenous fluid contamination in clinical laboratory samples
Article
Licensed
Unlicensed Requires Authentication

Design of an algorithm for the detection of intravenous fluid contamination in clinical laboratory samples

  • Cristian Rios Campillo , Maria Sanz de Pedro , Jose Manuel Iturzaeta , Ana Laila Qasem ORCID logo , Maria Jose Alcaide , Belen Fernandez-Puntero and Rubén Gómez Rioja ORCID logo
Published/Copyright: June 5, 2023

Abstract

Objectives

Contamination of blood samples from patients receiving intravenous fluids is a common error with potential risk to the patient. Algorithms based on the presence of aberrant results have been described but have the limitation that not all infusion fluids have the same composition. Our objective is to develop an algorithm based on the detection of the dilution observed on the analytes not usually included in infusion fluids.

Methods

A group of 89 cases was selected from samples flagged as contaminated. Contamination was confirmed by reviewing the clinical history and comparing the results with previous and subsequent samples. A control group with similar characteristics was selected. Eleven common biochemical parameters not usually included in infusion fluids and with low intraindividual variability were selected. The dilution in relation to the immediate previous results was calculated for each analyte and a global indicator, defined as the percentage of analytes with significant dilution, was calculated. ROC curves were used to define the cut-off points.

Results

A cut-off point of 20 % of dilutional effect requiring also a 60 % dilutional ratio achieved a high specificity (95 % CI 91–98 %) with an adequate sensitivity (64 % CI 54–74 %). The Area Under Curve obtained was 0.867 (95 % CI 0.819–0.915).

Conclusions

Our algorithm based on the global dilutional effect presents a similar sensitivity but greater specificity than the systems based on alarming results. The implementation of this algorithm in the laboratory information systems may facilitate the automated detection of contaminated samples.


Corresponding author: Cristian Rios Campillo, Laboratory Medicine, La Paz – Cantoblanco – Carlos III University Hospital, Madrid, Spain, E-mail:

Acknowledgments

Thanks to Alejandro Gómez from Siemens Healthineers for contribution on LIS implementation of the algorithm.

  1. Research funding: None declared.

  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. Informed consent: Not applicable.

  5. Ethical approval: Not applicable.

References

1. Najat, D. Prevalence of pre-analytical errors in clinical chemistry diagnostic labs in Sulaimani city of Iraqi Kurdistan. PLoS One 2017;12:e0170211. https://doi.org/10.1371/journal.pone.0170211.Search in Google Scholar PubMed PubMed Central

2. Mukhopadhyay, T, Subramanian, A, Pandey, S, Madaan, N, Trikha, A, Malhotra, R. The rise in preanalytical errors during COVID-19 pandemic. Biochem Med 2021;31:318–24. https://doi.org/10.11613/bm.2021.020710.Search in Google Scholar

3. Cornes, MP, Atherton, J, Pourmahram, G, Borthwick, H, Kyle, B, West, J, et al.. Monitoring and reporting of preanalytical errors in laboratory medicine: the UK situation. Ann Clin Biochem Int J Lab Med 2016;53:279–84. https://doi.org/10.1177/0004563215599561.Search in Google Scholar PubMed

4. Lippi, G, Betsou, F, Cadamuro, J, Cornes, M, Fleischhacker, M, Fruekilde, P, et al.. Preanalytical challenges – time for solutions. Clin Chem Lab Med 2019;57:974–81. https://doi.org/10.1515/cclm-2018-1334.Search in Google Scholar PubMed

5. Lippi, G, Becan-McBride, K, Behúlová, D, Bowen, RA, Church, S, Delanghe, J, et al.. Preanalytical quality improvement: in quality we trust. Clin Chem Lab Med 2013;51:229–41. https://doi.org/10.1515/cclm-2012-0597.Search in Google Scholar PubMed

6. Cornes, MP. Exogenous sample contamination. Sources and interference. Clin Biochem 2016;49:1340–5. https://doi.org/10.1016/j.clinbiochem.2016.09.014.Search in Google Scholar PubMed

7. Taghizadeganzadeh, M, Yazdankhahfard, M, Farzaneh, M, Mirzaei, K. Blood samples of peripheral venous catheter or the usual way: do infusion fluid alters the biochemical test results? Glob J Health Sci 2015;8:93. https://doi.org/10.5539/gjhs.v8n7p93.Search in Google Scholar PubMed PubMed Central

8. Coventry, LL, Jacob, AM, Davies, HT, Stoneman, L, Keogh, S, Jacob, ER. Drawing blood from peripheral intravenous cannula compared with venepuncture: a systematic review and meta‐analysis. J Adv Nurs 2019;75:2313–39. https://doi.org/10.1111/jan.14078.Search in Google Scholar PubMed

9. Bowen, RAR, Hortin, GL, Csako, G, Otañez, OH, Remaley, AT. Impact of blood collection devices on clinical chemistry assays. Clin Biochem 2010;43:4–25. https://doi.org/10.1016/j.clinbiochem.2009.10.001.Search in Google Scholar PubMed

10. Blonshine, S. Procedures for the collection of arterial blood specimens: approved standard, 4th ed. Wayne, PA: NCCLS; 2004.Search in Google Scholar

11. Ernst, DJ, Martel, AM, Arbique, JC, Johnson, S, McCall, RE, McLean, M, et al.. Collection of diagnostic venous blood specimens, 7th ed Wayne, PA: Clinical and Laboratory Standards Institute; 2017.Search in Google Scholar

12. Baker, RB, Summer, SS, Lawrence, M, Shova, A, McGraw, CA, Khoury, J. Determining optimal waste volume from an intravenous catheter. J Infusion Nurs 2013;36:92–6. https://doi.org/10.1097/nan.0b013e318282a4c2.Search in Google Scholar

13. Boteanu, C, Martín, MJA, Rioja, RG, Sánchez, JMI, Valdés, CE, Ortega, RÁ. Efecto en pruebas de coagulación del procedimiento de extracción desde catéter reservorio vascular subcutáneo. Rev Lab Clínico 2011;4:196–200. https://doi.org/10.1016/j.labcli.2011.06.004.Search in Google Scholar

14. Bauça, JM, Boned, B, Bullich, S, Caballero, A, Cortés, M. VIII Programa de Garantía Externa de la Calidad Preanalítica de la Sociedad Española de Medicina de Laboratorio; 2021 [Online]. Available from: https://www.contcal.org/qcweb/Documents/90%20Avaluacio%20anual/140%20Programas%202021/03%20Preanal%C3%ADtica%202021.pdf [Accessed 19 Feb 2023].Search in Google Scholar

15. Sciacovelli, L, Lippi, G, Sumarac, Z, del Pino Castro, IG, Ivanov, A, De Guire, V, et al.. Pre-analytical quality indicators in laboratory medicine: performance of laboratories participating in the IFCC working group “laboratory errors and patient safety” project. Clin Chim Acta 2019;497:35–40. https://doi.org/10.1016/j.cca.2019.07.007.Search in Google Scholar PubMed

16. Sinha, S, Jayaram, R, Hargreaves, CG. Fatal neuroglycopaenia after accidental use of a glucose 5% solution in a peripheral arterial cannula flush system. Anaesthesia 2007;62:615–20. https://doi.org/10.1111/j.1365-2044.2007.04989.x.Search in Google Scholar PubMed

17. Randell, EW, Yenice, S, Wamono, AAK, Orth, M. Autoverification of test results in the core clinical laboratory. Clin Biochem 2019;73:11–25. https://doi.org/10.1016/j.clinbiochem.2019.08.002.Search in Google Scholar PubMed

18. Zhu, J, Wang, H, Wang, B, Hao, X, Cui, W, Duan, Y, et al.. Combined strategy of knowledge‐based rule selection and historical data percentile‐based range determination to improve an autoverification system for clinical chemistry test results. J Clin Lab Anal 2022;36:e24233. https://doi.org/10.1002/jcla.24233.Search in Google Scholar PubMed PubMed Central

19. Jin, D, Wang, Q, Peng, D, Wang, J, Li, B, Cheng, Y, et al.. Development and implementation of an LIS-based validation system for autoverification toward zero defects in the automated reporting of laboratory test results. BMC Med Inf Decis Making 2021;21:174. https://doi.org/10.1186/s12911-021-01545-3.Search in Google Scholar PubMed PubMed Central

20. Topcu, DI, Gulbahar, O. A model to establish autoverification in the clinical laboratory. Clin Biochem 2021;93:90–8. https://doi.org/10.1016/j.clinbiochem.2021.03.018.Search in Google Scholar PubMed

21. Randell, EW, Yenice, S. Delta Checks in the clinical laboratory. Crit Rev Clin Lab Sci 2019;56:75–97. https://doi.org/10.1080/10408363.2018.1540536.Search in Google Scholar PubMed

22. Schifman, RB, Talbert, M, Souers, RJ. Delta check practices and outcomes: a Q-probes study involving 49 health care facilities and 6541 delta check alerts. Arch Pathol Lab Med 2017;141:813–23. https://doi.org/10.5858/arpa.2016-0161-cp.Search in Google Scholar

23. Baron, JM, Mermel, CH, Lewandrowski, KB, Dighe, AS. Detection of preanalytic laboratory testing errors using a statistically guided protocol. Am J Clin Pathol 2012;138:406–13. https://doi.org/10.1309/ajcpqirib3ct1ejv.Search in Google Scholar PubMed

24. Jara-Aguirre, JC, Smeets, SW, Wockenfus, AM, Karon, BS. Blood gas sample spiking with total parenteral nutrition, lipid emulsion, and concentrated dextrose solutions as a model for predicting sample contamination based on glucose result. Clin Biochem 2018;55:93–5. https://doi.org/10.1016/j.clinbiochem.2018.03.011.Search in Google Scholar PubMed

25. Lippi, G, Avanzini, P, Sandei, F, Aloe, R, Cervellin, G. Blood sample contamination by glucose-containing solutions: effects and identification. Br J Biomed Sci 2013;70:176–9. https://doi.org/10.1080/09674845.2013.11978286.Search in Google Scholar PubMed

26. Demirci, F, Akan, P, Kume, T, Sisman, AR, Erbayraktar, Z, Sevinc, S. Artificial neural network approach in laboratory test reporting: learning algorithms. Am J Clin Pathol 2016;146:227–37. https://doi.org/10.1093/ajcp/aqw104.Search in Google Scholar PubMed

27. Hernandez, J. The paradox of learning from errors. Why laboratories should embrace errors as learning opportunities. Clin Lab News 2011;37:15.Search in Google Scholar

28. Aarsand, A, Fernandez-Calle, P, Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, et al.. The EFLM biological variation database [Online]. Available from: https://biologicalvariation.eu [Accessed 19 Feb 2023].Search in Google Scholar

29. Rizoli, S. Plasmalyte. J Trauma 2011;70:S17–18. https://doi.org/10.1097/ta.0b013e31821a4d89.Search in Google Scholar PubMed

30. Lobo, D, Stanga, Z, Simpson, J, Anderson, JA, Rowlands, BJ, Allison, SP. Dilution and redistribution effects of rapid 2-litre infusions of 0.9 % (w/v) saline and 5 % (w/v) dextrose on haematological parameters and serum biochemistry in normal subjects: a double-blind crossover study. Clin Sci 2001;101:173–9. https://doi.org/10.1042/cs20000316.Search in Google Scholar

Received: 2023-02-24
Accepted: 2023-05-18
Published Online: 2023-06-05
Published in Print: 2023-10-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. The development of reference measurement procedures to establish metrological traceability
  4. Opinion Paper
  5. Establishing metrological traceability for small molecule measurands in laboratory medicine
  6. Articles
  7. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of aldosterone in human serum and plasma
  8. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of methotrexate in human serum and plasma
  9. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of lamotrigine in human serum and plasma
  10. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of topiramate in human serum and plasma
  11. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of gabapentin in human serum and plasma
  12. An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure for the quantification of levetiracetam in human serum and plasma
  13. Review
  14. Recent advances of drugs monitoring in oral fluid and comparison with blood
  15. Genetics and Molecular Diagnostics
  16. One fits all: a highly sensitive combined ddPCR/pyrosequencing system for the quantification of microchimerism after hematopoietic and solid organ transplantation
  17. General Clinical Chemistry and Laboratory Medicine
  18. Design of an algorithm for the detection of intravenous fluid contamination in clinical laboratory samples
  19. Sex-specific disparities of serum pepsinogen I in relation to body mass index
  20. Rapid and efficient LC-MS/MS diagnosis of inherited metabolic disorders: a semi-automated workflow for analysis of organic acids, acylglycines, and acylcarnitines in urine
  21. Persistently elevated serum concentrations of human chorionic gonadotropin (hCG)
  22. Reference Values and Biological Variations
  23. Pediatric reference interval verification for 16 biochemical markers on the Alinity ci system in the CALIPER cohort of healthy children and adolescents
  24. Serum GFAP – pediatric reference interval in a cohort of Danish children
  25. Cardiovascular Diseases
  26. Higher troponin T serum concentrations in hospital patients without diagnosed cardiac diseases compared to a population-based cohort
  27. Infectious Diseases
  28. Neopterin and kynurenine in serum and urine as prognostic biomarkers in hospitalized patients with delta and omicron variant SARS-CoV-2 infection
  29. Letters to the Editor
  30. Limitations in using the EFLM WG-A/ISO approach for assessment of reagent lot variability
  31. In reply to: Limitations in using the EFLM WG-A/ISO approach for assessment of reagent lot variability
  32. ChatGPT, critical thing and ethical practice
  33. AI, diabetes and getting lost in translation: a multilingual evaluation of Bing with ChatGPT focused in HbA1c
  34. UK diagnostics in the era of ‘permacrisis’: is it fit for purpose and able to respond to the challenges ahead?
  35. The underestimated potential of vibrational spectroscopy in clinical laboratory medicine: a translational gap to close
  36. A universal reference interval for serum immunoglobulins free light chains may be outdated
  37. The likelihood ratios of FIB-4-values for diagnosing advanced liver fibrosis in patients with NAFLD
Downloaded on 16.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2023-0200/html?lang=en
Scroll to top button