Comparison and Validation of 10 Equations Including a Novel Method for Estimation of LDL-cholesterol in a 168,212 Asian Population

John Hoon Rim, Yong Ho Lee, Myung Ha Lee, Ha Yan Kim, Jiin Choi, Byung Wan Lee, Eun Seok Kang, Hyun Chul Lee, Jeong Ho Kim, Sang Guk Lee, Bong Soo Cha

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20 Citations (Scopus)

Abstract

Low-density lipoprotein cholesterol (LDL-C) is frequently estimated using the empirical Friedewald equation. We compared the accuracy of the novel equation named as the 180-cell method (180-c), which estimates LDL-C using a stratification approach, to those of 9 previously suggested formulas, including the Friedewald equation. We compared the accuracy of 10 equations by calculating intraclass correlation coefficient (ICC) and weighted kappa index in relation to direct LDL-C measurement values. Two independent populations used in the validation were the Severance Hospital LDL-C (SHL) registry (n=164,358) and the Korea National Health and Nutrition Examination Survey (KNHANES) 2009 to 2010 (n=3,854), each representing the hospital patient population and the general Korean population, respectively. The 180-c and DeLong equations showed the highest ICCs, indicating the best agreement with direct LDL-C measurement. The 180-c and Chen equations showed the highest kappa indices. For the hypertriglyceridemic subpopulation from SHL, the 180-c equation showed the best agreement with direct LDL-C measurement in terms of ICC. We compared the novel 180-c method for LDL-C estimation with 9 previous formulas in a non-US population as the first external validation. The 180-c equation, with Chen equation, appeared to be more accurate than the Friedewald equation. Although the DeLong equation showed better performance in the hypertriglyceridemic subpopulation, the 180-c equation performed appropriately in Asian population.

Original languageEnglish
Article numbere3230
JournalMedicine (United States)
Volume95
Issue number14
DOIs
Publication statusPublished - 2016 Apr 1

Bibliographical note

Publisher Copyright:
© 2016 Wolters Kluwer Health, Inc. All rights reserved.

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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