An improved method for estimating low LDL-C based on the enhanced Sampson-NIH equation.

BACKGROUND: The accurate measurement of Low-density lipoprotein cholesterol (LDL-C) is critical in the decision to utilize the new lipid-lowering therapies like PCSK9-inhibitors (PCSK9i) for high-risk cardiovascular disease patients that do not achieve sufficiently low LDL-C on statin therapy. OBJECTIVE: To improve the estimation of low LDL-C by developing a new equation that includes apolipoprotein B (apoB) as an independent variable, along with the standard lipid panel test results. METHODS: Using ß-quantification (BQ) as the reference method, which was performed on a large dyslipidemic population (N = 24,406), the following enhanced Sampson-NIH equation (eS LDL-C) was developed by least-square regression analysis: [Formula: see text] RESULTS: The eS LDL-C equation was the most accurate equation for a broad range of LDL-C values based on regression related parameters and the mean absolute difference (mg/dL) from the BQ reference method (eS LDL-C: 4.51, Sampson-NIH equation [S LDL-C]: 6.07; extended Martin equation [eM LDL-C]: 6.64; Friedewald equation [F LDL-C]: 8.3). It also had the best area-under-the-curve accuracy score by Regression Error Characteristic plots for LDL-C < 100 mg/dL (eS LDL-C: 0.953; S LDL-C: 0.920; eM LDL-C: 0.915; F LDL-C: 0.874) and was the best equation for categorizing patients as being below or above the 70 mg/dL LDL-C treatment threshold for adding new lipid-lowering drugs by kappa score analysis when compared to BQ LDL-C for TG < 800 mg/dL (eS LDL-C: 0.870 (0.853-0.887); S LDL-C:0.763 (0.749-0.776); eM LDL-C:0.706 (0.690-0.722); F LDL-C:0.687 (0.672-0.701). Approximately a third of patients with an F LDL-C < 70 mg/dL had falsely low test results, but about 80% were correctly reclassified as higher (≥ 70 mg/dL) by the eS LDL-C equation, making them potentially eligible for PCSK9i treatment. The M LDL-C and S LDL-C equations had less false low results below 70 mg/dL than the F LDL-C equation but reclassification by the eS LDL-C equation still also increased the net number of patients correctly classified. CONCLUSIONS: The use of the eS LDL-C equation as a confirmatory test improves the identification of high-risk cardiovascular disease patients, who could benefit from new lipid-lowering therapies but have falsely low LDL-C, as determined by the standard LDL-C equations used in current practice.

Accuracy and Clinical Impact of Estimating Low-Density Lipoprotein-Cholesterol at High and Low Levels by Different Equations.

New more effective lipid-lowering therapies have made it important to accurately determine Low-density lipoprotein-cholesterol (LDL-C) at both high and low levels. LDL-C was measured by the ß-quantification reference method (BQ) (N = 40,346) and compared to Friedewald (F-LDL-C), Martin (M-LDL-C), extended Martin (eM-LDL-C) and Sampson (S-LDL-C) equations by regression analysis, error-grid analysis, and concordance with the BQ method for classification into different LDL-C treatment intervals. For triglycerides (TG) < 175 mg/dL, the four LDL-C equations yielded similarly accurate results, but for TG between 175 and 800 mg/dL, the S-LDL-C equation when compared to the BQ method had a lower mean absolute difference (mg/dL) (MAD = 10.66) than F-LDL-C (MAD = 13.09), M-LDL-C (MAD = 13.16) or eM-LDL-C (MAD = 12.70) equations. By error-grid analysis, the S-LDL-C equation for TG > 400 mg/dL not only had the least analytical errors but also the lowest frequency of clinically relevant errors at the low (<70 mg/dL) and high (>190 mg/dL) LDL-C cut-points (S-LDL-C: 13.5%, F-LDL-C: 23.0%, M-LDL-C: 20.5%) and eM-LDL-C: 20.0%) equations. The S-LDL-C equation also had the best overall concordance to the BQ reference method for classifying patients into different LDL-C treatment intervals. The S-LDL-C equation is both more analytically accurate than alternative equations and results in less clinically relevant errors at high and low LDL-C levels.

Identification of Dysbetalipoproteinemia by an Enhanced Sampson-NIH Equation for Very Low-Density Lipoprotein-Cholesterol.

Dysbetalipoproteinemia (hyperlipoproteinemia type III, HLP3) is a genetic disorder that results in the accumulation of cholesterol on highly atherogenic remnant particles. Traditionally, the diagnosis of HLP3 depended upon lipoprotein gel electrophoresis or density gradient ultracentrifugation. Because these two methods are not performed by most clinical laboratories, we describe here two new equations for estimating the cholesterol content of VLDL (VLDL-C), which can then be used for the diagnosis of HLP3. Using results from the beta-quantification (BQ) reference method on a large cohort of dyslipidemic patients (N = 24,713), we identified 115 patients with HLP3 based on having a VLDL-C to plasma TG ratio greater than 0.3 and plasma TG between 150 and 1,000 mg/dl. Next, we developed two new methods for identifying HLP3 and compared them to BQ and a previously described dual lipid apoB ratio method. The first method uses results from the standard lipid panel and the Sampson-NIH equation 1 for estimating VLDL-C ( S -VLDL-C), which is then divided by plasma TG to calculate the VLDL-C/TG ratio. The second method is similar, but the Sampson-NIH equation 1 is modified or enhanced ( eS -VLDL-C) by including apoB as an independent variable for predicting VLDL-C. At a cut-point of 0.194, the first method showed a modest ability for identifying HLP3 (sensitivity = 73.9%; specificity = 82.6%; and area under the curve (AUC) = 0.8685) but was comparable to the existing dual lipid apoB ratio method. The second method based on eS -VLDL-C showed much better sensitivity (96.5%) and specificity (94.5%) at a cut-point of 0.209. It also had an excellent AUC score of 0.9912 and was superior to the two other methods in test classification. In summary, we describe two new methods for the diagnosis of HLP3. The first one just utilizes the results of the standard lipid panel and the Sampson-NIH equation 1 for estimating (VLDL-C) ( S -VLDL-C) and can potentially be used as a screening test. The second method ( eS -VLDL-C), in which the Sampson-NIH equation 1 is modified to include apoB, is nearly as accurate as the BQ reference method. Because apoB is widely available at most clinical laboratories, the second method should improve both the accessibility and the accuracy of the HLP3 diagnosis.

A New Equation for Calculation of Low-Density Lipoprotein Cholesterol in Patients With Normolipidemia and/or Hypertriglyceridemia.

Importance: Low-density lipoprotein cholesterol (LDL-C), a key cardiovascular disease marker, is often estimated by the Friedewald or Martin equation, but calculating LDL-C is less accurate in patients with a low LDL-C level or hypertriglyceridemia (triglyceride [TG] levels ≥400 mg/dL). Objective: To design a more accurate LDL-C equation for patients with a low LDL-C level and/or hypertriglyceridemia. Design, Setting, and Participants: Data on LDL-C levels and other lipid measures from 8656 patients seen at the National Institutes of Health Clinical Center between January 1, 1976, and June 2, 1999, were analyzed by the ß-quantification reference method (18 715 LDL-C test results) and were randomly divided into equally sized training and validation data sets. Using TG and non-high-density lipoprotein cholesterol as independent variables, multiple least squares regression was used to develop an equation for very low-density lipoprotein cholesterol, which was then used in a second equation for LDL-C. Equations were tested against the internal validation data set and multiple external data sets of either ß-quantification LDL-C results (n = 28 891) or direct LDL-C test results (n = 252 888). Statistical analysis was performed from August 7, 2018, to July 18, 2019. Main Outcomes and Measures: Concordance between calculated and measured LDL-C levels by ß-quantification, as assessed by various measures of test accuracy (correlation coefficient [R2], root mean square error [RMSE], mean absolute difference [MAD]), and percentage of patients misclassified at LDL-C treatment thresholds of 70, 100, and 190 mg/dL. Results: Compared with ß-quantification, the new equation was more accurate than other LDL-C equations (slope, 0.964; RMSE = 15.2 mg/dL; R2 = 0.9648; vs Friedewald equation: slope, 1.056; RMSE = 32 mg/dL; R2 = 0.8808; vs Martin equation: slope, 0.945; RMSE = 25.7 mg/dL; R2 = 0.9022), particularly for patients with hypertriglyceridemia (MAD = 24.9 mg/dL; vs Friedewald equation: MAD = 56.4 mg/dL; vs Martin equation: MAD = 44.8 mg/dL). The new equation calculates the LDL-C level in patients with TG levels up to 800 mg/dL as accurately as the Friedewald equation does for TG levels less than 400 mg/dL and was associated with 35% fewer misclassifications when patients with hypertriglyceridemia (TG levels, 400-800 mg/dL) were categorized into different LDL-C treatment groups. Conclusions and Relevance: The new equation can be readily implemented by clinical laboratories with no additional costs compared with the standard lipid panel. It will allow for more accurate calculation of LDL-C level in patients with low LDL-C levels and/or hypertriglyceridemia (TG levels, ≤800 mg/dL) and thus should improve the use of LDL-C level in cardiovascular disease risk management.