A-208 An Improved Formula for Predicting Low LDL-C Based on an Enhanced Sampson-NIH Equation
Authors: Coverdell T C , Sampson M , Meeusen J W , Donato L J , Jaffe A S , Remaley A T 
Published: September 2023

Abstract

Background
Low-density lipoprotein cholesterol (LDL-C) is used to assess atherosclerotic cardiovascular disease (ASCVD) risk and to manage lipid-lowering therapy. Proprotein convertase subtilisin/kexin (type 9) serine protease (PCSK9) inhibitors decrease LDL-C by up to 70% when used in conjunction with statins. Due to cost, insurance companies restrict their use. To be eligible for PCSK9 therapy, a patient must have pre-existing ASCVD and LDL-C &gt; 70 mg/dL, while on a maximally tolerated dose of statins. Most clinical laboratories calculate LDL-C by the Friedewald Equation (FWLDL-C), which utilizes results from the standard lipid panel (total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C)). However, this often leads to an underestimation of LDL-C, especially when TG concentrations are elevated. More accurate equations have recently been developed, such as the Martin (MLDL-C), Sampson-NIH (SLDL-C) and extended Martin (ext-MLDL-C) equations. Using LDL-C determined by the beta-quantification reference method (BQ), we developed the following enhanced version of the Sampson-NIH Equation (eSLDL-C), which includes apoB as an independent variable: eSLDL-C = TC/1.15-(HDL-C)/1.25-TG/6.98-(TG × NonHDL-C)/1115 + TG2/8903.23 + (TG × ApoB)/1237 + ApoB/4.54–4.73

Methods
Results for clinically ordered ApoB, BQ, and lipid panels were used. To test the accuracy of the various equations, we compared BQLDL-C to FWLDL-C, MLDL-C, extMLDL-C, SLDL-C and eSLDL-C by regression analysis (N = 12 101, TG &lt; 800). Accuracy of each equation was compared to the BQ reference method for classifying patients (LDL-C ≤ 150, n = 9374) as either being below or above the 70 mg/dL treatment decision threshold for PCSK9 therapy.

Results
The eSLDL-C equation performed substantially better than the other equations for estimating low LDL-C (BQLDL-C ≤ 100 mg/dL, TG 5–800 mg/dL, n = 4115), with a mean absolute difference (MAD) of 3.80 mg/dL (compared to SLDL-C: 6.05; FWLDL-C: 8.71; MLDL-C: 6.43; extMLDL-C: 6.39). It also had the best overall normalized Matthew’s Correlation Coefficient (nMCC) for the best balance of sensitivity and specificity for identifying patients that are above the 70 mg/dL treatment threshold (eSLDL-C-94.7% (92.2% spec, 98.5% sen), SLDL-C-90.7% (94.8% spec, 95.5% sen), FWLDL-C-88.2% (98.0% spec, 92.7% sen), MLDL-C-90.3% (88.9% spec, 96.5% sen), and extMLDL-C-90.1% (88.4% spec, 96.5% sen)). In our dataset with 1125 subjects being below the 70 mg/dL threshold by BQLDL-C, FWLDL-C misclassified an additional 601 subjects below this threshold (Negative Predictive Value 65%). MLDL-C and extMLDL-C misclassified 289 subjects falsely low, whereas eSLDL-C only has 126 subjects falsely classified lower than the 70 mg/dL threshold.

Conclusion
We show that the enhanced Sampson-NIH equation, which includes apoB, estimates low LDL-C more accurately than other equations. The use of the new equation would improve access to those patients who might benefit from PCSK9 therapy but have falsely low LDL-C by the FWLDL-C equation or other LDL-C equations. The new equation is also more specific than the MLDL-C and extMLDL-C equations and would, therefore, also reduce the overutilization of PCSK9 therapy by these particular equations for those patients who do not require it, according to the reference BQ method and current guidelines.