Comparison of Multiple Equations for Low-Density Lipoprotein Cholesterol Calculation Against the Direct Homogeneous Method
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Objective Several equations have been proposed as alternatives for the reference method of measuring low-density lipoprotein cholesterol (LDL-C). This study aimed to evaluate these alternatives in comparison to the homogeneous method and validate their clinical utility. Methods Data on the lipid profiles of 1,006 Sudanese individuals were analyzed. The paired t-test was used to compare the results of direct and calculated LDL-C. Bland-Altman plots were used to demonstrate the differences between the measured and calculated LDL-C against the mean values. Linear regression was conducted, using the correlation coefficient (r) to quantify the relationship between methods. The bias between measured and calculated LDL-C was compared to the National Cholesterol Education Program Laboratory Standardization Panel criteria (i.e., accuracy within ±4% of expected values). Results The Martin and Anandaraja equations showed no significant difference compared to directly measured LDL-C (p>0.05). The DeLong equation indicated an insignificant difference only with a 99% confidence interval (p>0.01). The Martin, DeLong, and Teerakanchana equations exhibited the smallest limits of agreement, with data points concentrated closely around the mean difference line. Linear regression analysis revealed strong positive correlations (r>0.8) for most equations, except for the Ahmadi equation. The DeLong, Rao, and Martin equations demonstrated superior performance for LDL cutoff points (bias within ± 4%). The DeLong formula also showed superior performance at different lipid levels, closely followed by the Martin equation (bias within ±4%). Conclusion The DeLong and Martin equations outperformed others, such as the widely used Friedewald equation, in calculating LDL-C. Further validation studies are needed.
Abstract
Objective Several equations have been proposed as alternatives for the reference method of measuring low-density lipoprotein cholesterol (LDL-C). This study aimed to evaluate these alternatives in comparison to the homogeneous method and validate their clinical utility. Methods Data on the lipid profiles of 1,006 Sudanese individuals were analyzed. The paired t-test was used to compare the results of direct and calculated LDL-C. Bland-Altman plots were used to demonstrate the differences between the measured and calculated LDL-C against the mean values. Linear regression was conducted, using the correlation coefficient (r) to quantify the relationship between methods. The bias between measured and calculated LDL-C was compared to the National Cholesterol Education Program Laboratory Standardization Panel criteria (i.e., accuracy within ±4% of expected values). Results The Martin and Anandaraja equations showed no significant difference compared to directly measured LDL-C (p>0.05). The DeLong equation indicated an insignificant difference only with a 99% confidence interval (p>0.01). The Martin, DeLong, and Teerakanchana equations exhibited the smallest limits of agreement, with data points concentrated closely around the mean difference line. Linear regression analysis revealed strong positive correlations (r>0.8) for most equations, except for the Ahmadi equation. The DeLong, Rao, and Martin equations demonstrated superior performance for LDL cutoff points (bias within ± 4%). The DeLong formula also showed superior performance at different lipid levels, closely followed by the Martin equation (bias within ±4%). Conclusion The DeLong and Martin equations outperformed others, such as the widely used Friedewald equation, in calculating LDL-C. Further validation studies are needed.
참고문헌 (21)
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