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. 2020 May 1;5(5):540-548.
doi: 10.1001/jamacardio.2020.0013.

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

Maureen Sampson  1 , Clarence Ling  1 , Qian Sun  1 , Roa Harb  1 , Mohmed Ashmaig  2 , Russell Warnick  2 , Amar Sethi  3 , James K Fleming  4 , James D Otvos  5 , Jeff W Meeusen  6 , Sarah R Delaney  6 , Allan S Jaffe  7 , Robert Shamburek  8 , Marcelo Amar  8 , Alan T Remaley  8
Affiliations

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

Maureen Sampson et al. JAMA Cardiol. .

Erratum in

  • Errors in Figures.
    [No authors listed] [No authors listed] JAMA Cardiol. 2020 May 1;5(5):613. doi: 10.1001/jamacardio.2020.0526. JAMA Cardiol. 2020. PMID: 32211817 Free PMC article. No abstract available.

Abstract

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 β-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 β-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 β-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 β-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.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Jaffe reported receiving personal fees from Abbott, Beckman, Siemens, Roche, ET Healthcare, Sphingotec, Brava, Quidel, Blade, and Novartis outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Calculated vs β-Quantification (BQ) Very Low-Density Lipoprotein Cholesterol (VLDL-C) Levels
A, VLDL-C was calculated and plotted against VLDL-C as measured by BQ for Equation 1. B, VLDL-C was calculated and plotted against VLDL-C as measured by BQ for the Friedewald equation. C, VLDL-C was calculated and plotted against VLDL-C as measured by BQ for the Martin equation. The dotted line represents the line of identity, and the solid line is the linear fit for the indicated regression equation. Root mean square error (RMSE) and correlation coefficient (R2) values are from the validation data set (n = 9358), whereas the numbers in parentheses are corresponding values from the training set (n = 9357). The points on the graphs indicating the individual samples are color-coded according to triglyceride (TG) level. The color number scale for individual points indicates the start of the interval. To convert TGs to millimoles per liter, multiply by 0.0113; and to convert VLDL-C to millimoles per liter, multiply by 0.0259.
Figure 2.
Figure 2.. Calculated vs β-Quantification (BQ) Low-Density Lipoprotein Cholesterol (LDL-C) Levels
A, LDL-C was calculated with Equation 2 and plotted against LDL-C as measured by BQ. B, LDL-C was calculated with the Friedewald equation and plotted against LDL-C as measured by BQ. C, LDL-C was calculated with the Martin equation and plotted against LDL-C as measured by BQ. The points on the graphs indicating the individual samples are color-coded according to triglyceride (TG) level. The color number scale indicates the start of the interval. The dotted line represents the line of identity, and the solid line is the linear fit for the indicated regression equation. R2 indicates correlation coefficient; RMSE, root mean square error. To convert LDL-C to millimoles per liter, multiply by 0.0259.
Figure 3.
Figure 3.. Residual Error Plots for Low-Density Lipoprotein Cholesterol (LDL-C) by Various Equations
A, Difference between calculated LDL-C and direct LDL-C (dLDL-C) results (Roche; n = 174 179) for patients from a major reference laboratory is shown for Equation 2 plotted against triglyceride (TG) level. B, Difference between calculated LDL-C and dLDL-C results for patients from a major reference laboratory is shown for the Friedewald equation plotted against TG level. C, Difference between calculated LDL-C and dLDL-C results for patients from a major reference laboratory is shown for the Martin equation plotted against TG levels. Negative LDL-C test results are shown as red dots, placed on top of nonnegative test results (green dots), and hence make it appear that they are more abundant. The exact percentage of negative LDL-C test results are indicated for each panel. The mean absolute deviation (MAD) for all indicated equations is shown in each panel for each data set for TG levels less than 400 mg/dL or 400 mg/dL or higher. To convert LDL-C to millimoles per liter, multiply by 0.0259; and to convert TGs to millimoles per liter, multiply by 0.0113.
Figure 4.
Figure 4.. Test Accuracy and Classification of Patients Into Low-Density Lipoprotein Cholesterol (LDL-C) Treatment Categories
A, Mean absolute deviation (MAD) score for LDL-C from a general population with dyslipidemia (n = 27 646) is shown for Equation 2 (blue line), the Friedewald equation (gray line), and the Martin equation (orange line) for the indicated triglyceride (TG) intervals. The inset shows a close-up for low TG samples. B, MAD score is shown for Equation 2 (blue line), the Friedewald equation (gray line), and the Martin equation (orange line) for the indicated non–high-density lipoprotein cholesterol (HDL-C) intervals. The inset shows a close-up for low non–HDL-C samples. C, Misclassification at the 3 LDL-C treatment thresholds (70, 100, and 190 mg/dL) for the 3 LDL-C equations is shown for patients with TG levels from 400 to 800 mg/dL (n = 1177). D, Misclassification at the 3 LDL-C treatment thresholds (70, 100, and 190 mg/dL) for the 3 LDL-C equations is shown for patients with TG levels less than 400 mg/dL (n = 26 312). Blue bars indicate patients misclassified into the lower LDL-C treatment category in which the bar is plotted. Red bars indicate patients misclassified into the higher LDL-C treatment category in which the bar is plotted. Darker blue and red bars indicate misclassifications that crossed more than 1 LDL-C treatment category. The numbers in the bars indicate the percentage of total patients misclassified and are proportional to the bar length. To convert LDL-C and non–HDL-C to millimoles per liter, multiply by 0.0259; and to convert TGs to millimoles per liter, multiply by 0.0113.
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