Is data mining approach a best fit formula for estimation of low-density lipoprotein cholesterol?

With the change in the National Cholesterol Education Program ATP III guidelines, the risk of developing atherosclerosis has been now focused on total cholesterol and low-density lipoprotein (LDL) cholesterol levels. Different treatment modalities are now targeted at lowering LDL cholesterol values. Hence greater emphasis is now led on the accurate and precise measurement of LDL cholesterol. Beta-quantification, though, is the best reference method for LDL cholesterol estimation, it has the disadvantage of being inconvenient in our routine practice. The new generation direct homogenous assay is now the method of choice. But being more expensive, various calculated methods have now been developed. This study is an attempt to compare different calculated formula with direct cholesterol assessment and to find out the best one.

We compared LDL cholesterol measured by direct homogenous assay with the data mining approach (DM) and another calculated formula [Friedewald's Formula (FF) and Anandaraja Formula (AF)] in 266 samples with age greater than 18 years. Enrolled participants were divided into seven groups based upon their TG levels. Mean, percentage difference, and the correlation coefficient was assessed between calculated and direct LDL. Bland-Altman analysis was done to see the agreement between calculated vs direct LDL. All formulas were assessed among various TG levels with direct LDL by the Wilcoxon sign rank test.

1% level of significance was found between calculated and direct LDL with TG < 600 mg/dl. Mean and the percentage difference between direct and calculated LDL was lowest with the DM approach. Bland-Altman plot shows the best agreement of the DM approach with direct LDL.

This study indicates that the DM approach is closer to direct LDL compared to FF & AF.