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Comparability of 11 different equations for estimating LDL cholesterol on different analysers

  • Helgard M. Rossouw , Susanna E. Nagel and Tahir S. Pillay ORCID logo EMAIL logo
Published/Copyright: August 12, 2021
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 59 Issue 12

Abstract

Objectives

Low-density lipoprotein cholesterol (LDL-C) estimation is critical for risk classification, prevention and treatment of atherosclerotic cardiovascular disease (ASCVD). Predictive equations and direct LDL-C are used. We investigated the comparability between the Martin/Hopkins, Sampson, Friedewald and eight other predictive equations on two analysers, to determine whether the equation or analyser influences predicted LDL-C result.

Methods

In two unpaired datasets, 9,995 lipid profiles were analysed by the Abbott Architect and 4,782 by the Roche Cobas analysers. Non-parametric statistics and Bland Altman plots were used to compare LDL-C.

Results

On the Abbott analyser; the Martin/Hopkins, Sampson and Friedewald LDL-C were comparable (median bias ≤1.8%) over a range of 1–4.9 mmol/L. On the Roche platform, Martin/Hopkins LDL-C was comparable to Friedewald (median bias 0.3%) but not to Sampson LDL-C (median bias 25%). In patients with LDL-C <1.8 mmol/L and triglycerides (TG) ≤1.7 mmol/L, predicted LDL-C using Abbott reagents was similar between Martin/Hopkins, Sampson and Friedewald equations but not comparable using Roche reagents. Abbott reagents classified 10–20% of patients in the 1.0–1.8 mmol/L range (Martin/Hopkins 13.4%; Sampson 14.5%; Friedewald 16%; direct LDL-C 13.2%). Roche reagents classified 11–30% in the 1.0–1.8 mmol/L range (Martin/Hopkins 23%; Sampson 11%; Friedewald 25%; direct LDL-C 17%).

Conclusions

Performance of predictive equations is influenced by the choice of analyser for total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and TG. Replacement of the Friedewald equation with Martin/Hopkins estimation to improve quality of LDL-C results can be safely implemented across analysers, whereas caution is advised regarding the Sampson equation.

Keywords: atherosclerosis; cholesterol; equation; Friedewald; low density lipoprotein; Martin/Hopkins
Corresponding author: Tahir S. Pillay, Department of Chemical Pathology, Faculty of Health Sciences, University of Pretoria and National Health Laboratory Service Tshwane Academic Division, Private Bag X323, Arcadia, Pretoria, 0007, South Africa; and Division of Chemical Pathology, University of Cape Town, Pretoria, South Africa, Phone: +27 (0) 12 319 2114, Fax: +27 (0) 328 3600, E-mail:

Acknowledgments

We are grateful to the National Health Laboratory Service (NHLS) Corporate Data Warehouse for granting permission for the use of patient data. This work is submitted in fulfilment of the MMed (Chem Path) degree dissertation requirements for H.M. Rossouw at the University of Pretoria.

  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: The study was approved by the University of Pretoria Faculty of Health Sciences Research Ethics Committee – reference number 733/2020.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2021-0747).

Received: 2021-03-18
Accepted: 2021-07-29
Published Online: 2021-08-12
Published in Print: 2021-11-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2021-0747
Keywords for this article
atherosclerosis; cholesterol; equation; Friedewald; low density lipoprotein; Martin/Hopkins

Articles in the same Issue

  1. Frontmatter
  2. Editorials
  3. Optimizing effectiveness of COVID-19 vaccination: will laboratory stewardship play a role?
  4. Mathematical recalibration of total bile acids: comparing the incomparable?
  5. Reviews
  6. Could metabolomics drive the fate of COVID-19 pandemic? A narrative review on lights and shadows
  7. Low free-T3 serum levels and prognosis of COVID-19: systematic review and meta-analysis
  8. Opinion Paper
  9. Pancreatic lipase: why laboratory community does not take enough care of this clinically important test?
  10. General Clinical Chemistry and Laboratory Medicine
  11. The BACH project protocol: an international multicentre total Bile Acid Comparison and Harmonisation project and sub-study of the TURRIFIC randomised trial
  12. Comparability of 11 different equations for estimating LDL cholesterol on different analysers
  13. Streamlined three step total vitamin C analysis by HILIC-UV for laboratory testing
  14. Hematology and Coagulation
  15. Anti-phosphatidyl-serine/prothrombin antibodies (aPS/PT) in isolated lupus anticoagulant (LA): is their presence linked to dual test positivity?
  16. Cancer Diagnostics
  17. Consideration should be given to smoking, endometriosis, renal function (eGFR) and age when interpreting CA125 and HE4 in ovarian tumor diagnostics
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  24. Daily monitoring of viral load measured as SARS-CoV-2 antigen and RNA in blood, IL-6, CRP and complement C3d predicts outcome in patients hospitalized with COVID-19
  25. Alternative detection of SARS-CoV-2 RNA by a new assay based on mass spectrometry
  26. Performance evaluation of an automated SARS-CoV-2 Ag test for the diagnosis of COVID-19 infection on nasopharyngeal swabs
  27. Predicting the protective humoral response to a SARS-CoV-2 mRNA vaccine
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