Home 5 Clinical Diagnostics Insider 5 G2 Insider: New Method Improves Accuracy of LDL-C Estimation

G2 Insider: New Method Improves Accuracy of LDL-C Estimation

by | Feb 19, 2015 | Clinical Diagnostics Insider, Diagnostic Testing and Emerging Technologies, G2 Insider-dtet

Researchers have developed and validated a novel method to estimate low-density lipoprotein cholesterol (LDL-C) that offers accuracy advantages over traditional estimation methods, according to a study published Nov. 20 in the Journal of the American Medical Association (JAMA). If validated, the authors say, this method could be implemented into practice through “most laboratory reporting systems at virtually no cost.” “It is remarkable how well the [Friedewald] equation has withstood the test of time. Nevertheless, Friedewald et al recognized that inaccuracies in very LDL-C (VLDL-C) estimation could become more important at lower cholesterol concentrations and higher triglyceride concentrations,” write the authors, led by Seth Martin, M.D., from Johns Hopkins in Baltimore. LDL-C estimates eliminate the need for ultracentrifugation needed with direct LDL-C measures and are widely used in international recommendations to guide treatment. The most common estimation method is the Friedewald equation, which assumes a fixed ratio of 5-to-1 for triglycerides to VLDL-C (TG:VLDL-C). However, the authors say, this fixed ratio is “problematic” given the actual TG:VLDL-C ratio varies significantly across the range of triglyceride and cholesterol levels. Additionally, the currently used 70 mg/dL secondary prevention target is well below the LDL values seen in the data set used to derive […]

Researchers have developed and validated a novel method to estimate low-density lipoprotein cholesterol (LDL-C) that offers accuracy advantages over traditional estimation methods, according to a study published Nov. 20 in the Journal of the American Medical Association (JAMA). If validated, the authors say, this method could be implemented into practice through “most laboratory reporting systems at virtually no cost.” “It is remarkable how well the [Friedewald] equation has withstood the test of time. Nevertheless, Friedewald et al recognized that inaccuracies in very LDL-C (VLDL-C) estimation could become more important at lower cholesterol concentrations and higher triglyceride concentrations,” write the authors, led by Seth Martin, M.D., from Johns Hopkins in Baltimore. LDL-C estimates eliminate the need for ultracentrifugation needed with direct LDL-C measures and are widely used in international recommendations to guide treatment. The most common estimation method is the Friedewald equation, which assumes a fixed ratio of 5-to-1 for triglycerides to VLDL-C (TG:VLDL-C). However, the authors say, this fixed ratio is “problematic” given the actual TG:VLDL-C ratio varies significantly across the range of triglyceride and cholesterol levels. Additionally, the currently used 70 mg/dL secondary prevention target is well below the LDL values seen in the data set used to derive the equation. In the JAMA study the researchers used a convenience sample of consecutive clinical lipid profiles obtained from 1,350,908 children, teens, and adults in the United States (2009 to 2011) as part of the Very Large Database of Lipids (Atherotech Diagnostics Laboratory). Atherotech’s Vertical Auto Profile ultracentrifugation technique separates lipoprotein subfractions to measure cholesterol, including LDL-C, VLDL-C, and HDL-C. Clinical practice guideline LDL-C risk classification was compared using estimated and directly measured LDL-C. The researchers found using the derivation data set (n=900,605) that the median TG:VLDL-C was 5.2 with 65 percent of the variance in this ratio explained by the triglyceride and non–high-density lipoprotein cholesterol (HDL-C) levels. Using the strata of triglyceride and non–HDL-C values, a 180-cell table of median TG:VLDL-C values was developed and applied to the validation data set (n=450,303) to estimate the novel LDL-C (LDL-CN). There were significant differences in overall agreement in guideline risk classification using standard direct LDL-C (91.7 percent), LDL-CN (85.4 percent), compared to Friedewald-derived LDL-C. Using LDL-CN, the greatest improvement in concordance occurred in classifying LDL-C lower than 70 mg/dL, especially in patients with high triglyceride levels. While still requiring additional validation, this LDL-CN estimation method could be coded into an online calculator, smartphone application, or automated laboratory reporting system, suggest the authors, making clinical adoption easy.

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