Low-density Lipoprotein Cholesterol Estimation Research Articles

Comparison of low-density lipoprotein cholesterol (LDL-c) estimates by in-lab and point-of-care devices in the context of emerging LDL-c equations

Abstract Low-density lipoprotein cholesterol (LDL-c) and lipid measurements are used for hyperlipidemia diagnosis, cardiovascular disease risk classification, and medication management. Millions of lipid panels are performed annually. Given the widespread need for lipid testing, there are two CLIA-waived point-of-care (POC) lipid testing devices. These devices were FDA-approved using the Friedewald equation to estimate LDL-c based on measurements of (triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), and total cholesterol (TC). By contrast, for laboratory-based LDL-c estimation, the Friedewald equation is being replaced by newer, more accurate equations such as the NIH-Sampson equation. These equations use the same lipid panel results to estimate LDL-c and only differ in the arithmetic of the estimation. This study evaluates how differences between the in-clinic POC machines and in-laboratory lipid measurements affect the estimated LDL-c results and are compounded by differences in the equations used for LDL-c estimation. This retrospective observational study collected deidentified lipid results (TG, HDL-c, TC, LDL-c) and demographic data (age, gender) for all samples that had lipid panel results from in-lab and POC within twenty-four hours of each other in a single hospital system between 2014 and 2023. Three hundred twenty samples were identified; samples with non-numerical values or results outside of the reportable range for TG, HDL-c, or TC were not included in subsequent LDL-c calculations using the Friedewald and NIH-Sampson equations (69 results). Patients under the age of 18 were excluded (2 results). Two hundred and forty-nine samples met inclusion criteria; this cohort showed non-significant differences in lipid, age, and gender distributions relative to all POC results tested in the same time frame. Consistent with prior reports, we observed discordance between POC and the in-lab lipid measurements, particularly for TG (Bland-Altman: bias:-23.04 mg/dL, standard deviation:61.3 mg/dL). These analytical differences may not change clinical management if the cardiovascular risk classification using estimated LDL-c is unchanged. However, the risk classification based on the in-lab versus in-clinic testing differed for 20% of patients. The most pronounced difference in LDL-c estimation occurred when the POC machine used Friedewald and in-lab testing used NIH-Sampson, and resulted in 5% more patients differentially classified. Regardless of testing platform, more patients are categorized as higher risk on an individual and population-based level with the NIH-Sampson equation. This study highlights the need for transparency in lipid panel reports regarding which LDL-c equation is used in both POC and in-lab testing formats, and the need for readily available tools for conversion between different LDL-c estimation equations.

Comparison of Martin/Hopkins, Sampson/NIH, and Friedewald Equations for the Estimation of Low-Density Lipoprotein Cholesterol and its Effect on Treatment Thresholds for Patients at a Large Academic Medical Center

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is routinely used in the clinical management of patients at risk for cardiovascular disease (CVD). Currently, LDL-C is frequently calculated with results from the standard lipid panel (total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and triglycerides (TG)); the Friedewald equation is one commonly utilized calculation for LDL-C (FLDL-C). Despite its well-documented limitations, including incorrect estimation of LDL-C with high TGs, the Friedewald equation is still used by many clinical laboratories where patients having TG levels >400 mg/dL are reflexed to direct LDL (DLDL-C) measurement. Recent guidelines recommend the use of “modern” equations such as Martin/Hopkins (MLDL-C) and Sampson/NIH (SLDL-C) equations which account for variations in TG and non-HDL-C. In this study, we assessed the accuracy of the Martin/Hopkins and Sampson/NIH equations for estimating LDL-C levels up to 800 mg/dL in our patient populations and the impact of the equations to patients’ reclassification. As a comparison with previous studies, the misclassification of patients with dyslipidemia into incorrect LDL-C based treatment groups (<70, 70-99, 100-189, and >= 190 mg/dL) is calculated. Methods Lipid panel and DLDL-C results are obtained using Siemens Advia and Atellica chemistry analyzers. Data analysis including calculation of mean absolute difference (MAD) between DLDL-C and calculated LDL-C and analysis of treatment group misclassification for TG < 400 and 400 – 800 mg/dL is performed using Python 3.11.5, Pandas 2.1.4, and Scipy 1.11.1. Results 9,306 out of 296,362 lipid panels from 2021 to 2023 have either corresponding provider ordered (DLDL-C) testing or reflex DLDL-C testing performed. For all TG values, MAD (mg/dL)/total treatment group misclassification (%) is: FLDL-C 42.14/50.66, MLDL-C 21.09/30.03, and SLDL-C 25.51/40.22. For TG values < 400 mg/dL: FLDL-C 19.33/32.21, MDLD-C 16.67/27.84, and SLDL-C 16.73/28.20 with a sample size of 3,614. For TG values 400 - 800 mg/dL: FLDL-C 47.66/65.25, MLDL-C 20.75/30.48, and SLDL-C 31.36/48.8 with a sample size of 4,855. In particular, the Martin/Hopkins vs. Sampson/NIH vs. Friedewald treatment group misclassifications (%) for TG 400 – 800 mg/dL are: DLDL < 70 mg/dL 6.34/1.67/0.60, DLDL 70 – 99 mg/dL 9.31/14.91 /20.62, DLDL 100 – 189 mg/dL 10.53/26.26/38.19, DLDL >= 190 mg/dL 4.30/5.95/5.58. Conclusion In terms of misclassification rates across LDL-C based treatment groups used in prior studies, the Martin/Hopkins equation performs better overall than the current Friedewald equation and gives similar overall rates of misclassification (27.84 vs. 30.48%) between the TG < 400 mg/dL and the TG 400 – 800 mg/dL groups, allowing the possibility to reflex to direct LDL-C testing only at the higher threshold of TG 800 mg/dL, reducing reflexed DLDL tests by over 100 per month at our institution. A potential source of error is chemistry analyzer DLDL testing rather than the gold standard LDL-C β-quantification used in previous studies.

A-213 Comparison of low-density lipoprotein cholesterol (LDL-c) estimates by in-lab and point-of-care devices in the context of emerging LDL-c equations

Abstract Background Low-density lipoprotein cholesterol (LDL-c) and lipid measurements are used for hyperlipidemia diagnosis, cardiovascular disease risk classification, and medication management. Millions of lipid panels are performed annually. Given the widespread need for lipid testing, there are two CLIA-waived point-of-care (POC) lipid testing devices. These devices were FDA-approved using the Friedewald equation to estimate LDL-c based on measurements of (triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), and total cholesterol (TC). However, in laboratory-based testing, the Friedewald equation is being replaced by newer, more accurate equations such as the NIH-Sampson equation. These equations use the same lipid panel results to estimate LDL-c and only differ in the arithmetic of the estimation. The study objective is to evaluate how differences between the in-clinic POC machines and in-laboratory lipid measurements affect the estimated LDL-c results and are compounded by differences in the equations used for LDL-c estimation. Methods This retrospective observational study collected deidentified lipid results (TG, HDL-c, TC, LDL-c) and demographic data (age, gender) for all samples that had lipid panel results from in-lab and POC within twenty-four hours of each other in a single hospital system between 2014 and 2023. Three hundred twenty samples were identified; samples with non-numerical values or results outside of the reportable range for TG, HDL-c, or TC were not included in subsequent LDL-c calculations using the Friedewald and NIH-Sampson equations (69 results). Patients under the age of 18 were excluded (2 results). Two hundred and forty-nine samples met inclusion criteria; this cohort showed non-significant differences in lipid, age, and gender distributions relative to all POC results tested in the same time frame. Results Consistent with prior reports, we observed discordance between POC and the in-lab lipid measurements, particularly for TG (Bland-Altman: bias:-23.04 mg/dL, standard deviation:61.3 mg/dL). Regardless of the testing platform, LDL-c results are higher on an individual and population-based level with the NIH-Sampson equation. These analytical differences may not change clinical management if the cardiovascular risk classification using estimated LDL-c is unchanged. However, the risk classification based on the in-lab versus in-clinic testing differed for 20-30% of patients. The most pronounced difference in LDL-c estimation occurred when the POC machine used Friedewald and in-lab testing used NIH-Sampson, and resulted in 5% more patients differentially classified. Conclusions This study highlights the need for transparency in lipid panel reports regarding which LDL-c equation is used in both POC and in-lab testing formats, and the need for readily available tools for conversion between different LDL-c estimation equations.

A-223 Direct LDL or not Direct-LDL, that is the question

Abstract Background Management of Cardiovascular Disease (CVD) risk is dependent on Low Density Lipoprotein Cholesterol (LDL-C) levels; therefore LDL-C serves as the marker for the assessment of CVD risk and the focus of lipid lowering treatment. The gold standard reference method for measurement of LDL-C is beta-quantification. We compared calculated LDL-C with Friedewald (F-LDL-C), Martin-Hopkins (M-LDL-C) and Sampson (S-LDL-C) equations versus LDL-C directly measured (D-LDL-C). In CVD management also cholesterol ratios are evaluated. TC/HDL is associated with morbidity and mortality in the general population. Furthermore, several studies have stated that high triglycerides (TG) levels and low HDL-C (TG/HDL ratio) could function as an atherogenic index. Methods 11399 samples were analyzed on Siemens Atellica Solution® for the lipid profile determination measuring Total Cholesterol (TC), D-LDL-C, HDL-C and TG. In the absence of additional clinical parameters, based on desirable levels for LDL-C recommended by guidelines, CVD risk was evaluated by assigning to LDL-C a cut-off of 100 mg/dL. In presence of misclassifications, we used two other parameters to assess LDL-C results: TG/HDL-C ratio and CT/HDL-C. Results Patients cohort had a mean age of 57.7 (±16.8) years of which 53% (age=58.6, ±15.9) were males and 47% (age=56.5, ±17.8 years) were females. Of these, the 57.7% were outpatients and 42.3% inpatients (42.1% of infectious diseases - 24.4% of the cardiovascular department). Data about LDL-C misclassification and ratio data parameters are shown in table 1. Discordant patients for D-LDL-C vs F-LDL-C are 8.5%, that of D-LDL-C vs M-LDL-C are 7.7% and D-LDL-C vs S-LDL-C are 7.2%. Conclusions Is known that LDL plays a central role in the pathogenesis of atherosclerosis. Accurate estimation of LDL-C is a fundamental point for the stratification of CVD risk and the achievement of the therapeutic goal; however, we are still uncertain about the best and accurate way to measure it.

Low-Density Lipoprotein Cholesterol Gymnastics: Exploring the Advantages and Limitations of the Friedewald, Martin-Hopkins, and Sampson Equations for Personalized Lipid Management.

The most commonly used method for low-density lipoprotein cholesterol (LDL-C) estimation is the Friedewald equation, which has notable limitations. However, more accurate methods have been proposed. This study investigates the advantages and limitations of these methods and identifies the contexts in which each equation is the most or least applicable. A cohort of 222 individuals underwent a standard lipid profile assessment, including directly measuring their LDL-C (dLDL-C). LDL-C was also estimated using the Friedewald, Martin-Hopkins, and Sampson equations. The differences (%Delta) between the estimated and measured LDL-C were analyzed in relation to dLDL-C, high-density lipoprotein cholesterol (HDL-C), and triglyceride levels. The %Delta was significantly lower (p < 0.0001) for the Martin-Hopkins (-8.8 ± 9.8) and Sampson (-9.5 ± 9.2) equations compared to Friedewald (-12.2 ± 9.2). All equations increasingly underestimated LDL-C as the dLDL-C levels decreased. The %Delta of the Martin-Hopkins equation showed significant positive correlations with dLDL-C (≤130 mg/dL) and triglycerides and a significant negative correlation with HDL-C. In a subgroup of 30 individuals with extreme %Delta values, patterns of gross underestimation were observed, particularly when low LDL-C, low triglycerides, and high HDL-C coincided. The Martin-Hopkins equation is a superior method for LDL-C estimation and a valuable tool in precision medicine. However, clinicians and laboratory professionals must be aware of its limitations and recognize patterns that could lead to significant LDL-C underestimation. We propose an algorithm for clinical laboratories to provide personalized LDL-C assessments.

More precise method of low-density lipoprotein cholesterol estimation for tobacco and electronic cigarette smokers: A cross-sectional study

More precise method of low-density lipoprotein cholesterol estimation for tobacco and electronic cigarette smokers: A cross-sectional study

Comparison of ten formulae for calculating low-density lipoprotein cholesterol with direct low-density lipoprotein cholesterol measurement

ABSTRACT Background: Accurate lipid profile assessment, mainly low-density lipoprotein cholesterol (LDL-C) measurement, is crucial for diagnosing and managing cardiovascular disease (CVD). The widely used Friedewald formula for LDL-C calculation has limitations, prompting the exploration of alternative equations. Aims: This study aims to compare ten LDL-C calculation formulas with direct measurement among study participants attending a tertiary care centre. Materials and Methods: Conducted from September 2021 to July 2022 at a tertiary healthcare centre in India, the study involved 114 participants aged 18 years or older. The exclusion criteria were high-serum triglyceride (TG) levels, renal failure, diabetes, liver diseases or unwillingness to participate. Blood samples were analysed for total cholesterol, TG, high-density lipoprotein cholesterol and LDL-C. The very-low-density lipoprotein cholesterol level was calculated. The LDL-C formulas evaluated were Friedewald, Anandaraja, De Cordova, Vujovic, Ahmadi, Puavillai, Chen, Hattori, Martin–Hopkin and Sampson. Results: Vujovic’s formula showed a negligible mean difference and strong correlation (r = 0.916, P &lt; 0.001) with direct LDL-C estimation across TG concentrations. Sampson’s formula performed best for TG &lt;150 mg/dL, whereas Vujovic’s formula excelled for 151–399 mg/dL TG concentrations. All formulas were highly correlated with direct LDL-C levels (P &lt; 0.001). Bland–Altman analysis indicated Vujovic’s formula as the closest match to direct measurement, with a mean bias of −1.42 ± 20.59 mg/dL. Conclusion: Vujovic’s formula demonstrated the slightest difference and strong correlation with direct LDL-C measurement, outperforming other formulas. This finding enhances the accuracy of LDL-C assessment and improves CVD risk management in clinical practice.

Choosing the right equation for calculating indirect LDL-Cholesterol (LDL-C) in adult Pakistani population: Evaluation of seven equations using big data analytics

ObjectiveCardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Low density lipoprotein cholesterol (LDL-C) contributes to the atherogenic process. However, direct LDL-C (d-LDL) has rarely been estimated by the gold standard method because it is cumbersome and expensive. We aim to evaluate calculated low density lipoprotein (LDL-c) by various equations with reference to directly measured LDL-C in the Pakistani adult population as a cost-effective alternative. MethodsWe retrospectively evaluated the validity of seven equations for estimating calculated LDL-C by computing correlation coefficients (r) and Bland Altman plots to assess agreement (mean %) for (d-LDL) and calculated (LDL-c) on all seven equations. Statistical analysis was performed in Stata Statistical Software: Release 17, College Station, TX: StataCorp LLC. ResultsWe analyzed 247082 direct assays of lipid profiles of adults aged ≥18 years. The mean LDL-C levels computed on Friedewald, de Cordova, Chen, Hattori, Vujovic, Teerakanchana, Sampson equations were 106.8 ± 31.4, 103.7 ± 25.0, 108.6 ± 28.2, 100.1 ± 29.5, 115.2 ± 31.2, 113.1 ± 28.3 and 110.3 ± 30.6 respectively. Friedewald and Hattori equations correlated strongly with direct LDL-C (r = 0.937) for each followed by Sampson (r = 0.935) and Vujovic (r = 0.931). However, the median bias was least for the Friedwald equation (−1.6) compared to the other equations. ConclusionIn contrast to the global literature advocating for the use of newer equations, although the conventional and widely utilized Friedewald equation remains the best alternative for calculated LDL-C estimation in adult Pakistani population.

Explainable artificial intelligence for LDL cholesterol prediction and classification

IntroductionMonitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions. Materials and methodsWe retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability. ResultsPredicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas. ConclusionsIt can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.

Comparison of Performance of Various Calculated Low-Density Lipoprotein (LDL) Methods in the Indian Population: A Hospital-Based Study.

In recent times, there has been a surge in proposed alternative approaches to computing low-density lipoprotein cholesterol (LDL-C), with a focus on enhancing precision, particularly within diverse demographic and clinical groups. Our aim is to assess the agreement, precision, and practicality of these methods compared to direct LDL-C measurements, with the goal of identifying the most effective approach for estimating LDL-C in the Indian context. It is a retrospective analytical study. Lipid profile data were gathered from the laboratory and organized in Microsoft Excel for analysis. LDL-C was computed using three different methods: the Friedwald formula, the Martin-Hopkins formula, and Sampson's formula. These calculations were then compared with the direct method of LDL-C estimation in two distinct groups: when triglyceride (TG) levels were less than 400 mg/dL and when TG levels exceeded 400 mg/dL. Bland-Altman plots were generated, and concordance correlation coefficients (CCCs) were computed to determine the most suitable calculated method. Data from 1,776 participants were analysed and divided into two groups. In both Group 1 (TG < 400 mg/dL) and Group 2 (TG > 400 mg/dL) considering bias, limits of agreements, and correlation coefficient, as seen on the Bland-Altman and CCC, Martin-Hopkins equation was found to be performing better than Friedwald and Sampson's equation. In this study, the Martin-Hopkins formula appears to be the most appropriate choice for precise LDL-C level measurements and indicated improved accuracy and consistency in LDL-C measurements, especially in individuals with elevated TG levels. This underscores its importance in ensuring precise assessment and suitable clinical management.

A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations

BackgroundGiven the critical importance of Low-density lipoprotein cholesterol (LDL-C) levels in determining cardiovascular risk, it is essential to measure LDL-C accurately. Since the Friedewald formula generates incorrect predictions in many circumstances, new equations have been developed to overcome the Friedewald equations' shortcomings. This study aimed to compare estimated LDL-C with directly measured LDL-C (dLDL-C), as well as their performance in predicting LDL-C, utilizing Friedewald, extended Martin–Hopkins, Sampson, de Cordova, and Vujovic formulas and five machine learning (ML) algorithms. MethodsA total of 29,504 samples from the ISLAB-2 Core Laboratory were included in the study. All statistical analysis was performed using R version 4.1.2. Statistical Language. ResultsBayesian-Regularized Neural Network (BRNN) (r = 0.957) and Random Forest (RF) (r = 0.957) algorithms showed a higher correlation with dLDL-C than the other equations in all-testing dataset. All ML algorithms demonstrated less bias than pre-existing LDL-C equations with dLDL-C and outperformed the LDL-C estimation equations in terms of concordance in all-testing dataset. ConclusionsThe results of our research indicate that when compared to conventional equations, ML algorithms are much more effective in predicting LDL-C. ML algorithms, aided by a vast dataset, could have the capability to predict LDL-C levels even in cases where triglyceride levels are high, unlike the limited usage of Friedewald formula.

A novel equation for the estimation of low-density lipoprotein cholesterol in the Saudi Arabian population: a derivation and validation study

Low-density lipoprotein cholesterol (LDL-C) is typically estimated by the Friedewald equation to guide atherosclerotic cardiovascular disease (ASCVD) management despite its flaws. Martin–Hopkins and Sampson-NIH equations were shown to outperform Friedewald’s in various populations. Our aim was to derive a novel equation for accurate LDL-C estimation in Saudi Arabians and to compare it to Friedewald, Martin–Hopkins and Sampson-NIH equations. This is a cross-sectional study on 2245 subjects who were allocated to 2 cohorts; a derivation (1) and a validation cohort (2). Cohort 1 was analyzed in a multiple regression model to derive an equation (equationD) for estimating LDL-C. The agreement between the measured (LDL-CDM) and calculated levels was tested by Bland–Altman analysis, and the biases by absolute error values. Validation of the derived equation was carried out across LDL-C and triglyceride (TG)-stratified groups. The mean LDL-CDM was 3.10 ± 1.07 and 3.09 ± 1.06 mmol/L in cohorts 1 and 2, respectively. The derived equation is: LDL-CD = 0.224 + (TC × 0.919) – (HDL-C × 0.904) – (TG × 0.236) – (age × 0.001) – 0.024. In cohort 2, the mean LDL-C (mmol/L) was estimated as 3.09 ± 1.06 by equationD, 2.85 ± 1.12 by Friedewald, 2.95 ± 1.09 by Martin–Hopkins, and 2.93 ± 1.11 by Sampson-NIH equations; statistically significant differences between direct and calculated LDL-C was observed with the later three equations (P < 0.001). Bland–Altman analysis showed the lowest bias (0.001 mmol/L) with equationD as compared to 0.24, 0.15, and 0.17 mmol/L with Friedewald, Martin–Hopkins, and Sampson-NIH equations, respectively. The absolute errors in all guideline-stratified LDL-C categories was the lowest with equationD, which also showed the best classifier of LDL-C according to guidelines. Moreover, equationD predicted LDL-C levels with the lowest error with TG levels up to 5.63 mmol/L. EquationD topped the other equations in estimating LDL-C in Saudi Arabians as it could permit better estimation when LDL-C is < 2.4 mmol/L, in familial hyperlipidemia, and in hypertriglyceridemia, which improves cardiovascular outcomes in high-risk patients. We recommend further research to validate equationD in a larger dataset and in other populations.

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

BackgroundThe 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.ObjectiveTo 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.MethodsUsing 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:eSLDL-C=TC1.15-HDL-C1.25-TG6.99-TG×NonHDL-C1120+TG28910+TG×ApoB1240+ApoB4.54-4.73\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$eS\\,LDL-C= \\frac{TC}{1.15}-\\frac{HDL-C}{1.25}-\\frac{TG}{6.99}-\\frac{\\left(TG\ imes NonHDL-C\\right)}{1120}+\\frac{{TG}^{2}}{8910}+\\frac{\\left(TG\ imes ApoB\\right)}{1240}+\\frac{ApoB}{4.54}-4.73$$\\end{document}ResultsThe 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.ConclusionsThe 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.

Applicability of Artificial Intelligence in the Field of Clinical Lipidology: A Narrative Review.

The development of advanced technologies in artificial intelligence (AI) has expanded its applications across various fields. Machine learning (ML), a subcategory of AI, enables computers to recognize patterns within extensive datasets. Furthermore, deep learning, a specialized form of ML, processes inputs through neural network architectures inspired by biological processes. The field of clinical lipidology has experienced significant growth over the past few years, and recently, it has begun to intersect with AI. Consequently, the purpose of this narrative review is to examine the applications of AI in clinical lipidology. This review evaluates various publications concerning the diagnosis of familial hypercholesterolemia, estimation of low-density lipoprotein cholesterol (LDL-C) levels, prediction of lipid goal attainment, challenges associated with statin use, and the influence of cardiometabolic and dietary factors on the discordance between apolipoprotein B and LDL-C. Given the concerns surrounding AI techniques, such as ethical dilemmas, opacity, limited reproducibility, and methodological constraints, it is prudent to establish a framework that enables the medical community to accurately interpret and utilize these emerging technological tools.

Concordance between low density lipoprotein cholesterol concentration measurement by enzymatic method and calculation by Friedewald formula in cardiovascular risk classification

Accurate estimation of low-density lipoprotein cholesterol (LDL-C) is important for cardiovascular risk assessment and guiding cholesterol-lowering therapy. Due to the high cost of β-quantification (Gold standard) and time-consuming, direct measurement of LDL-C is an alternative method. However, unlike the calculation of LDL-C by Friedewald formula, there is an additional cost in terms of reagents for performing a direct LDL-C test. The current study aimed to compare direct LDL-C concentration determination to data calculated by Friedewald formula. 752 lipid profiles of 710 people with LDL-C estimated by direct LDL assay, in the Biochemistry laboratory of university hospital center of Angré, were included in the study. In the same group, LDL-C was calculated using Friedewald formula. Lin’s concordance correlation coefficient (ccc) and Passing-Bablok regression analysis using, MedCal software, were performed to assess the strength of concordance between the 2 methods, and identify any possible bias. The concordance between the two methods was moderate (ρc = 0.9466). Passing-Bablok regression analysis revealed a systematic bias between the two methods. The total error observed (TEobs) between the two methods was higher than allowable total error recommended by the NCEP-ATPIII when LDL-C values was less than 159 mg/dL (4.112 mmol/L). The Friedewald formula resulted in lower LDL-C concentration values. Despite its cost-effectiveness in the estimation of LDL-C, an underestimation of LDL-C levels could result in inaccurate cardiovascular diseases (CVD) risk assessments and potentially significant future societal costs due to inadequate prevention and treatment of CVD.

Assessing the Practical Differences in LDL-C Estimates Calculated by Friedewald, Martin/Hopkins, or NIH Equation 2: An Observation Cross-Sectional Study.

Low-density lipoprotein-cholesterol (LDL-C) remains a clinically important cholesterol target in primary prevention of atherosclerotic cardiovascular disease. The present study aimed to assess the practical differences among three equations utilized for the estimation of LDL-C: the Friedewald, the Martin/Hopkins, and the NIH equation 2. Blood lipid measurements from 4,556 noninstitutionalized participants, aged 12 to 80, were obtained from the 2017-2020 National Health and Nutrition Examination Survey study. We 1) assessed the differences between three calculated LDL-C estimates, 2) examined the correlations between LDL-C estimates using correlation coefficients and regression, and 3) investigated the degree of agreement in classifying individuals into the LDL-C category using weighted Kappa and percentage of agreement. The differences in LDL-C estimates between equations varied by sex and triglyceride levels (p<0.001). Overall, the mean of absolute differences between Friedewald and Martin/Hopkins was 3.17 mg/dL (median=2.0, 95% confidence interval [CI] [3.07-3.27]). The mean of absolute differences between Friedewald and NIH Equation 2 was 2.08 mg/dL (median=2.0, 95% CI [2.03-2.14]). Friedewald correlated highly with Martin/Hopkins (r=0.991, rho=0.989) and NIH Equation 2 (r=0.998, rho=0.997). Cohen's weighted Kappa=0.92 between Friedewald and Martin/Hopkins, and 0.95 between Friedewald and NIH equation 2. The percentage of agreement in classifying individuals into the same LDL-C category was 93.0% between Friedewald and Martin/Hopkins, and 95.4% between Friedewald and NIH equation 2. Understanding the practical differences in LDL-C calculations can be helpful in facilitating decision-making during a paradigm shift.

Correlation of extended Martin/Hopkins equation with a direct homogeneous assay in assessing low-density lipoprotein cholesterol in patients with hypertriglyceridemia.

The Friedewald or Martin/Hopkins equation is widely used to estimate low-density lipoprotein cholesterol (LDL-C) at triglyceride (TG) levels <400 mg/dL. In this study, we aimed to validate the recently developed Sampson and extended Martin/Hopkins equations intended for use in patients with TG levels up to 800 mg/dL by comparing them to a direct homogenous assay. In total, 8676 participants with serum TG levels <800 mg/dL were enrolled in this study. LDL-C was directly measured using Abbott homogeneous assay (DLDL) and estimated using the Friedewald (FLDL), Martin/Hopkins (MLDL), extended Martin/Hopkins (EMLDL), and Sampson equations (SLDL). The overall concordance between the DLDL and LDL-C estimates was calculated. The performance of the four equations was also compared using Bland-Altman plots and mean absolute difference (MAD). The EMLDL was more accurate than other LDL-C equations particularly for patients with TG≥400 mg/dL (MAD = 10.43; vs. FLDL: MAD = 21.1; vs. SLDL: MAD 11.62). The overall concordance of FLDL, MLDL, EMLDL, and SLDL with DLDL in TG values ranging from 200 to 799 mg/dL were 52.2, 70.5, 71.6, and 65.7%, respectively (p < 0.001), demonstrating the EMLDL as the most optimal estimation method, particularly for high TG levels (≥200 mg/dL). Both the original and extended Martin/Hopkins method are optimal in estimating LDL-C levels in clinical laboratories using the Abbott analyzer in patients with TG levels of 200-399 and 400-799 mg/dL, respectively. Meanwhile, caution is need that considerable underestimation of Friedewald and Sampson equation could lead to undertreatment in hypertriglyceridemia.

Development of a Modified Friedewald's Formula to Calculate Low-Density Lipoprotein in an Iranian Population.

Elevated low-density lipoprotein cholesterol (LDL-C) is a significant risk factor for cardiovascular diseases. LDL-C can be directly measured using various methods, but this requires expensive equipment. Currently, clinical laboratories estimate LDL-C based on Friedewald's formula (FF). We aimed to develop a modified formula based on directly measured LDL-C (D-LDL-C) values in a large population in Southern Iran and compare the results with various other estimation formulas. The participants of this cross-sectional study were adults aged >18 years living in Southern Iran. Blood samples from 15,200 individuals were collected, and the measured lipid parameters were randomly divided into training (n=10,184) and validation (n=5,016) datasets. A new formula was developed using a linear regression model, and its accuracy was validated. Pearson's correlation and Cohen's kappa were used to determin the relationship between D-LDL-C and calculated LDL-C (C-LDL-C). The developed formula for the estimation of LDL-C was 0.857 total cholesterol (TC)-0.915 high-density lipoprotein cholesterol (HDL-C)-0.115 triglycerides (TG). Based on our proposed formula, for TG<150 and TG≥150 mg/dL, there was a significant correlation between mean values of D-LDL-C and C-LDL-C (r=0.985 and r=0.974, respectively). Compared to other formulas, C-LDL-C obtained from the proposed formula had the highest correlation with D-LDL-C. The agreement between D-LDL-C and C-LDL-C for TC<200, 200-239, and ≥240 mg/dL was 80.8%, 63.2%, and 67.4%, respectively, indicating a higher level of agreement than other formulas. The new formula appears to be more accurate than FF when applied to the population of Southern Iran.

Factors to consider with estimated low-density lipoprotein cholesterol using the new Sampson/NIH equation

IntroductionThe most commonly utilized method for determining low-density lipoprotein cholesterol (LDLc) is by Friedewald estimation (FeLDLc). A new approach to better estimate LDLc has been proposed by Sampson et al. 2020, known as the Sampson/National Institutes of Health (NIH) estimation of LDLc (NeLDLc), to overcome the limitations of FeLDLc. Non-high-density lipoprotein-cholesterol (Non-HDLc), has equivalent cut-offs to LDLc, established by the 2021 Canadian Cardiovascular Society (CCS) guideline. We hypothesized that NeLDLc remains an inadequate substitute at high triglyceride levels when compared to Non-HDLc. MethodsA retrospective analysis of 120,959 lipid profiles (47085 patients) spanning five years across a large academic medical center was utilized for comparison of NeLDLc and FeLDLc relative to Non-HDLc as a function of triglyceride content. Regression and concordance between calculated methods were determined at various triglyceride levels to determine optimal utilization of NeLDLc. ResultsNeLDLc is generally more correlated and has greater concordance than FeLDLc with Non-HDLc. NeLDLc with increasing triglycerides can produce negatively erroneous results, even with triglycerides < 4.52 mmol/L (400 mg/dL). The largest variation of NeLDLc results is notable at < 0.5 mmol/L (19 mg/dL). Currently, the 2021 CCS guideline recommends reliance on Non-HDLc when triglycerides are > 1.5 mmol/L (133 mg/dL). With the use of NeLDLc, this triglyceride cut-off can be increased to 1.7 mmol/L(150 mg/dL), making it consistent with the hypertriglyceridemia flagging limit. ConclusionNeLDLc offers increased concordance and correlation to Non-HDLc when compared to FeLDLc. However, caution is warranted when triglycerides are > 4.5 mmol/L and when NeLDLc results are < 0.5 mmol/L. Adopting NeLDLc enables flagging at 1.7 mmol/L (vs. 1.5 mmol/L) of triglycerides to suggest reliance on Non-HDLc while simultaneoulsly indicating hypertriglyceridemia.

Martin's Formula As the Most Suitable Method for Estimation of Low-Density Lipoprotein Cholesterol in Indian Population.

Background Because of cost effectiveness, most of the laboratories in India estimate low-density lipoprotein cholesterol (LDL-C) levels with the Friedewald's formula. There were many shortcomings of the Friedewald's formula. Recently, Martin and colleagues have derived a new formula for calculating LDL-C. The present study was undertaken to calculate LDL-C using various formulae (Friedewald's formula, Anandaraja's formula, and Martin's formula) and to compare directly measured LDL-C (D-LDL-C) with calculated LDL-C at various ranges of triglyceride (TG) concentration. Materials and Methods The present study compared LDL-C measured by Martin's formula, Friedewald's formula, and Anandaraja's formula with D-LDL-C in 280 outpatient fasting samples between the age groups of 18 and 50 years. Depending on the TG values, study samples were divided into four groups. Group 1: less than 200 mg/dL; Group 2: 200 to 300 mg/dL; Group 3: 300 to 400 mg/dL; and Group 4: more than 400 mg/dL. Results Martin's formula shows highest correlation with r -value of 0.9979 compared with Friedewald's (0.9857) and Anandaraja's (0.9683) r -values. The mean difference was least for Martin's formula (0.31 ± 3.53) compared with other formulae. Among all the groups, percentage of error was least for Martin's formula (0.23%). Martin's LDL-C shows highest concordance (90.90%) compared with Friedewald's (79.60%) and Anandaraja's formulae (82.90%). Conclusion Among all the groups, Martin's formula shows highest correlation, least percentage of error, highest concordance, and least mean differences. At all TG levels, Martin's formula is the best formula compared with the Friedewald's formula and Anandaraja's formula.