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An improved method for estimating low LDL-C based on the enhanced Sampson-NIH equation
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Abstract
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 β-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:
$$eS\,LDL-C= \frac{TC}{1.15}-\frac{HDL-C}{1.25}-\frac{TG}{6.99}-\frac{\left(TG\times NonHDL-C\right)}{1120}+\frac{{TG}^{2}}{8910}+\frac{\left(TG\times ApoB\right)}{1240}+\frac{ApoB}{4.54}-4.73$$
e
S
L
D
L
-
C
=
TC
1.15
-
H
D
L
-
C
1.25
-
TG
6.99
-
T
G
×
N
o
n
H
D
L
-
C
1120
+
TG
2
8910
+
T
G
×
A
p
o
B
1240
+
ApoB
4.54
-
4.73
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.
Springer Science and Business Media LLC
Title: An improved method for estimating low LDL-C based on the enhanced Sampson-NIH equation
Description:
Abstract
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 β-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:
$$eS\,LDL-C= \frac{TC}{1.
15}-\frac{HDL-C}{1.
25}-\frac{TG}{6.
99}-\frac{\left(TG\times NonHDL-C\right)}{1120}+\frac{{TG}^{2}}{8910}+\frac{\left(TG\times ApoB\right)}{1240}+\frac{ApoB}{4.
54}-4.
73$$
e
S
L
D
L
-
C
=
TC
1.
15
-
H
D
L
-
C
1.
25
-
TG
6.
99
-
T
G
×
N
o
n
H
D
L
-
C
1120
+
TG
2
8910
+
T
G
×
A
p
o
B
1240
+
ApoB
4.
54
-
4.
73
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.
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