Martin Equation Research Articles

A-210 Ldl-c to non-hdl-c ratio method: a simple way to determine bias for ldl-c methods on hypertriglyceridemic samples

Abstract Background Beta-quantification (BQ) is the reference method for LDL-C but is not widely available. It is difficult, therefore, for clinical laboratories to independently assess the accuracy of their methods for either calculating or directly measuring LDL-C. Our goal was to investigate if an interrelationship between the tests in the standard lipid panel could be used to compare the performance of different LDL-C methods against BQ, specifically in samples with hypertriglyceridemia, a major cause of bias for LDL-C testing. Methods BQ results from Mayo Medical Labs (n=39,667) and the NIH (n=18,338) were used to identify a linear relationship by least-squares regression analysis between lipid panel test results and LDL-C to facilitate a statistical comparison between different LDL-C methods. Results It is well established that the number of VLDL particles increases as TG increases, because VLDL is the main carrier of TG. As a consequence, the ratio of LDL-C to non-HDL-C is inversely related to TG, which can be made linear by taking the square root of TG (Figure). Nearly identical regression equations were found for this relationship, using two different BQ datasets. When LDL-C was calculated by either Sampson equation, the LDL-C/non-HDL-C regression lines nearly overlapped with the one for BQ. In contrast, the Martin equation showed a positive bias with increasing TG, whereas the Friedewald equation showed a negative bias, consistent with past reports on their LDL-C biases with hypertriglyceridemia. In the UKBiobank, the Beckman direct LDL-C showed a positive bias for TG, which was confirmed in CAP proficiency test surveys. By simulation analysis, approximately 80 samples with high (>200 mg/dL) and low (<100 mg/dL) TG is statistically sufficient to assess if the LDL-C/non-HDL-C regressions lines of LDL-C methods differ from BQ. Conclusions The LDL-C/non-HDL-C ratio method is a simple way to examine LDL-C methods for bias on hypertriglyceridemic samples.

Comparison of estimated LDL cholesterol equations with direct measurement

Abstract Aim LDL-cholesterol is one of the most important risk factors for coronary heart disease (CHD). These equations use total cholesterol, HDL and triglycerides measurements. Friedwald equation sometimes shows variatiοn in LDL calculation, mainly in patients with extreme values of triglycerides and LDL cholesterol. The aim of this study is to evaluate the accuracy of 12 well-known equations (Friedewald, Vujovic, Chen, Puavillai, Cordova, Anandaraja, Hattori, Ahmadi, Sampson, Planella, Ephraim, Martin). Methods A sample of 280 patients was studied, of which 33% had CHD history (table 1). The agreement of the equations with measured LDL was calculated both with goodness of fit test (r-Pearson) and test of discrimination (ROC curves). Results The Martin equation showed the best agreement (r=0.955,95% CI:(0.944,0.964)), with similar results given by the Sampson equation (r=0.954). The widely used Friedewald equation seems to fall short with r=0.939 as does the Planella equation (r=0.916), which is the only one that takes Apo-B values into account. Other simple linear equations did not differ greatly in accuracy from the Friedewald equation (table 2). Also in extreme triglycerides values (> 300mg/dl) the Sampson equation showed a better performance (r=0.935) than the Martin equation (r=0.928). Conclusions Equations such as those of Sampson and Martin take into account the interaction between triglycerides and non-HDL cholesterol and appear to be superior to simple linear ones (eg, Friedewald equation) in the accuracy of predicting LDL-cholesterol values even in individuals with elevated VLDL, IDL and non-HDL cholesterol. Further research is needed to verify our results.Descriptive characteristics of the studyCorrelations table

A-208 An Improved Formula for Predicting Low LDL-C Based on an Enhanced Sampson-NIH Equation

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 > 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 < 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.

Comparison of low-density lipoprotein cholesterol estimation methods in individuals with insulin resistance: A cross-sectional study

Background and aimsThe Friedewald, Sampson, and Martin equations were developed to estimate low-density lipoprotein cholesterol levels; however, the validation data of these equations with and without insulin resistance are insufficient. Materials and methodsWe collected data on low-density lipoprotein cholesterol and lipid profiles from the Korea National Health and Nutrition Examination Survey. Using the data on insulin requirement, insulin resistance was calculated for 4,351 participants (median age, 48 [36–59] years; 49.9% male) using the homeostatic model assessment for insulin resistance (n = 2,713) and quantitative insulin-sensitivity check index (n = 2,400). ResultsAccording to the mean and median absolute deviation, the Martin equation yielded more accurate estimates than other equations when the triglyceride level was < 400 mg/dL with insulin resistance; the Sampson equation yielded lower estimates when the direct low-density lipoprotein cholesterol level was < 70 mg/dL and triglyceride level was < 400 mg/dL without insulin resistance. However, the three equations yielded similar estimates when the triglyceride level was < 150 mg/dL with and without insulin resistance. ConclusionThe Martin equation yielded more appropriate estimates than the Friedewald and Sampson equations for triglyceride levels < 400 mg/dL with and without insulin resistance. If the triglyceride level was < 150 mg, the Friedewald equation could also be considered.

Indirect calculation of LDL using thirteen equations in Pakistani population

BackgroundOwing to the atherogenic properties, low density lipoprotein cholesterol (LDL-C) is the primary target for treatment and diagnosis of cardiovascular diseases (CVDs), hence accurate measurement of LDL-C is critical. Despite the availability of direct measurement assays for LDL-C, it is routinely calculated by Friedewald equation in clinical settings in Pakistan mostly due to financial constraints. However, the validity of this equation is impacted by several factors, therefore several other equations have been developed for the calculation of LDL-C. Materials and methodsLDL-C of 39,385 individuals measured directly by homogenous assays (dLDL) was compared with LDL-C calculated by thirteen equations (cLDL-C). Stratifications based on different lipids i.e., triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL) were made to check the validity of these equations across all ranges of lipid profile. The correlation and median difference between dLDL and cLDL-C was statistically analyzed. ResultsOverall Teerakanchana equation displayed a strong positive correlation (ρ = 0.967) and least median difference (-8.81) with dLDL, followed by Martin equation (ρ = 0.967). For higher TG ranges (>500 mg/dL), Teerakanchana equation had the least median difference (1.31) and a strong correlation (ρ = 0.800). ConclusionOur data suggest that Teerakanchana equation may be employed as an alternative to Friedewald equation for Pakistani population.

Evaluation of fatty tissue representative solvents in extraction of medical devices for chromatographic analysis of devices’ extractables and leachables based on Abraham general solvation model

Extraction solvents used in chemical characterization (i.e., extractables and leachables testing, E&L) of fatty tissue-contacting medical devices for biocompatibility assessment per ISO 10993 have been studied by Abraham general solvation models. Chemically suitable alternative solvents to fatty tissues in solvation properties (solubility, partition, extraction, etc.) have been proposed based on Abraham's organic solvent system coefficients for water and air to condensed organic solvent phases. This evaluation is built upon the conclusion by Abraham, Acree Jr and Cometto-Muñiz that olive oil is chemically corresponding to fatty tissues. However, olive oil, if used as an extraction solvent to simulate fatty tissues, is in general not analytically expedient (realistic) per ISO 10993-18 (2020) for chromatographic analysis, and it is critical to seek alternative solvents to olive oil to perform the extraction. Although nonpolar solvents such as alkanes have been proposed and used as alternative solvents to vegetable oils, they are not equivalent to olive oil in solvation properties. Due to the practical challenge in chromatographic analysis of oil samples and the difference in migration kinetics of E&L between oil and organic solvents, the computational approach is the only realistic option to evaluate chemically alternative solvents to olive oil to simulate fatty tissue extraction. By comparing Abraham solvent system coefficients for water and air to condensed organic solvent phases distribution, a five-dimensional space distance (D) between solvents and olive oil as a reference solvent is calculated using Abraham and Martin equation to predict alternative or similar solvents to olive oil. The results of the calculation are further evaluated using E&L solubility ratio between solvents and olive oil, taking into consideration of solvent safety and physical properties. It is concluded from the study that butanone and dioxane are chemically the most suitable alternative or representative solvents to olive oil. They can be used as fatty tissue representative solvents in chemical characterization study of medical device. As Abraham solvation model is solvent system specific, not solute specific, the conclusions from this study are considered as universal.

Comparison of a Machine Learning Method and Various Equations for Estimating Low-Density Lipoprotein Cholesterol in Korean Populations.

BackgroundLDL-C is the primary target of lipid-lowering therapy and used to classify patients by cardiovascular disease risk. We aimed to develop a deep neural network (DNN) model to estimate LDL-C levels and compare its performance with that of previous LDL-C estimation equations using two large independent datasets of Korean populations.MethodsThe final analysis included participants from two independent population-based cohorts: 129,930 from the Gangnam Severance Health Check-up (GSHC) and 46,470 participants from the Korean Initiatives on Coronary Artery Calcification registry (KOICA). The DNN model was derived from the GSHC dataset and validated in the KOICA dataset. We measured our proposed model's performance according to bias, root mean-square error (RMSE), proportion (P)10–P20, and concordance. P was defined as the percentage of patients whose LDL was within ±10–20% of the measured LDL. We further determined the RMSE scores of each LDL equation according to Pooled cohort equation intervals.ResultsOur DNN method has lower bias and root mean-square error than Friedewald's, Martin's, and NIH equations, showing a high agreement with LDL-C measured by homogenous assay. The DNN method offers more precise LDL estimation in all pooled cohort equation strata.ConclusionThis method may be particularly helpful for managing a patient's cholesterol levels based on their atherosclerotic cardiovascular disease risk.

Estimation of low-density lipoprotein cholesterol levels using machine learning

BackgroundLow-density lipoprotein-cholesterol (LDL-C) is used as a threshold and target for treating dyslipidemia. Although the Friedewald equation is widely used to estimate LDL-C, it has been known to be inaccurate in the case of high triglycerides (TG) or non-fasting states. We aimed to propose a novel method to estimate LDL-C using machine learning. MethodsUsing a large, single-center electronic health record database, we derived a ML algorithm to estimate LDL-C from standard lipid profiles. From 1,029,572 cases with both standard lipid profiles (total cholesterol, high-density lipoprotein-cholesterol, and TG) and direct LDL-C measurements, 823,657 tests were used to derive LDL-C estimation models. Patient characteristics such as sex, age, height, weight, and other laboratory values were additionally used to create separate data sets and algorithms. ResultsMachine learning with gradient boosting (LDL-CX) and neural network (LDL-CN) showed better correlation with directly measured LDL-C, compared with conventional methods (r = 0.9662, 0.9668, 0.9563, 0.9585; for LDL-CX, LDL-CN, Friedewald [LDL-CF], and Martin [LDL-CM] equations, respectively). The overall bias of LDL-CX (−0.27 mg/dL, 95% CI −0.30 to −0.23) and LDL-CN (−0.01 mg/dL, 95% CI -0.04–0.03) were significantly smaller compared with both LDL-CF (−3.80 mg/dL, 95% CI −3.80 to −3.60) or LDL-CM (−2.00 mg/dL, 95% CI −2.00 to −1.94), especially at high TG levels. ConclusionsMachine learning algorithms were superior in estimating LDL-C compared with the conventional Friedewald or the more contemporary Martin equations. Through external validation and modification, machine learning could be incorporated into electronic health records to substitute LDL-C estimation.

Comparison of Formula-Based Methods with Diverse TGL: VLDL-C Ratio for Calculating LDL-C in a Tertiary Care Hospital.

Background The most popular Friedewald formula (FF) was tailored with a fixed factor of 5 for triglyceride-very low-density lipoprotein cholesterol (TGL:VLDL-C) ratio. Some of the subsequent studies on diverse population demonstrated modified FF with only altered TGL:VLDL-C ratio, comprising either a fixed or an adjustable factor. Hata and Nakajima as well as Puavilai et al proposed fixed factors of 4 and 6, respectively. Recently, Martin et al recommended an adjustable factor derived as N-strata-specific median TGL:VLDL-C ratio based on TGL and non-high-density lipoprotein cholesterol (non-HDL-C). Aim This comparative retrospective study evaluates the efficacy of LDL-C formulae, varying only in TGL-VLDL-C ratio, using direct LDL-C assay as a reference method in a tertiary care hospital. Materials and Methods A total of 1,747 patient records with lipid profile data were procured. Concordance analysis, absolute difference, and post hoc test were employed as analytical tools. The impact of total cholesterol (TChol), TGL, and HDL-C on formulae was also evaluated. Results Overall, Martin equation had relatively the highest concordance, narrowest absolute difference, and minimal influence of TChol, TGL, and HDL-C. On the contrary, the Hata method revealed comparatively the lowest concordance, widest absolute difference, and high influence of TChol, TGL, and HDL-C. The remaining formula-based approaches, that is, FF and Puavilai calculation, executed mostly inconsistent intermittent features between Martin equation and Hata method. Conclusion Relatively dominant and competitive analytical attributes of the Martin equation with an adjustable TGL:VLDL-C factor outweigh the remaining three formulae-based methods with fixed TGL:VLDL-C factor in Indian adults.

Evaluation of blood simulating solvents in extractables and leachables testing for chemical characterization of medical devices based on Abraham general solvation model

Extraction vehicles (simulating solvents) used in chemical characterization (i.e., extractables/leachables testing) of blood-contacting medical devices for biocompatibility assessment per ISO 10993 have been studied by Abraham general solvation models. Chemically equivalent solvents to blood in solvation properties (solubility, partition, extraction, etc.) have been proposed based on Abraham’s organic solvent system coefficients for water and air to condensed organic solvent phases. This evaluation is built upon the conclusion by Abraham, Acree Jr and Cometto-Muñiz that water saturated n-butanol (called wet n-butanol) is chemically corresponding to blood. Although wet n-butanol can be directly used to replace blood in an extraction study (such as a simulated-use extraction study per ISO 10993-18 (2020)), it can be beneficial to have alternate solvents to wet n-butanol (butan-1-ol) when chromatographic interference is practically significant. By comparing Abraham solvent system coefficients for water to condensed organic solvent phases distribution, a five-dimensional space distance (D) between solvents and reference solvent (wet n-butanol) is calculated using Abraham and Martin equation to predict equivalent or similar solvents (to wet n-butanol). It is concluded from the study that ethanol/water (60/40, V/V) and ethanol/water (50/50, V/V) are chemically equivalent or quite similar to wet n-Butanol. Other two less similar solvents are ethylene glycol and ethanol/water (70/30, V/V). Thus, n-butanol, ethanol/water (60/40, V/V) and ethanol/water (50/50, V/V) can be used as blood simulating solvents in chemical characterization study of medical device. In certain situations, ethylene glycol might be desirable as an alternate solvent, as it is not a mixture. As Abraham solvation model is general (solvent system specific, not solute specific), the conclusions from this study are considered as universal.

Stanovenie LDL-cholesterolu novými rovnicami Martin a Sampson, porovnanie so starým Friedewaldom na reálnych dátach a na umelom súbore

Objective LDL-cholesterol (LDL-C) is determined by methods whose accuracy is significantly affected in various clinical or analytical situations. Two computational methods have recently been described, the Martin equation and the Sampson equation, validity of which compare with the Friedewald equation. Methods LDL-C comparisons determined by the 3 equations were performed on 4 real sets of lipid data, generated in various previous studies, ranging from n = 140 to n = 7 393. We have created an artificial set of data on the extent of 900 members with equally distributed values of TC, HDL-C and TG troughout the commonly found range. Such a data set is independent of the phrase we performed the calculations on our Comparisons were also made on this artificial file. Results The difference between the LDL-C values determined by the different equations gradually increases with decreasing LDL-C levels, both in the subgroup of low TG values and in the subgroups of medium and higher TG values. This applies to all 4 real files as well as to the artificial file. These differences are more visible the larger the file size. For the artificial set, the overall agreement between the LDL-C categories was lowest when comparing the Friedewald and Martin equations (83.1%), higher between the Sampson and Martin equations (88.9%) and highest when comparing the Friedewald and Sampson equations (90.9%). In all 4 real sets, the trends of overestimation and underestimation between the equations were exactly the same as in the artificial set. Conclusion The results of clinical and epidemiological studies are significantly influenced by the method used to determine LDL-C. When comparing the calculation methods for determining LDL-C, it is possible to preferably use the described artificial set.

The direct correlation between oxidative stress and LDL-C levels in adults is maintained by the Friedewald and Martin equations, but the methylation levels in the MTHFR and ADRB3 genes differ

Low-density lipoprotein (LDL-C) concentrations are a standard of care in the prevention of cardiovascular disease and are influenced by different factors. This study compared the LDL-C concentrations estimated by two different equations and determined their associations with inflammatory status, oxidative stress, anthropometric variables, food intake and DNA methylation levels in the LPL, ADRB3 and MTHFR genes. A cross-sectional population-based study was conducted with 236 adults (median age 37.5 years) of both sexes from the municipality of João Pessoa, Paraíba, Brazil. The LDL-C concentrations were estimated according to the Friedewald and Martin equations. LPL, ADRB3 and MTHFR gene methylation levels; malondialdehyde levels; total antioxidant capacity; ultra-sensitive C-reactive protein, alpha-1-acid glycoprotein, homocysteine, cobalamin, and folic acid levels; usual dietary intake; and epidemiological variables were also determined. For each unit increase in malondialdehyde concentration there was an increase in the LDL-C concentration from 6.25 to 10.29 mg/dL (p <0.000). Based on the Martin equation (≥70 mg/dL), there was a decrease in the DNA methylation levels in the ADRB3 gene and an increase in the DNA methylation levels in the MTHFR gene (p <0.05). There was a positive relation of homocysteine and cholesterol intake on LDL-C concentrations estimated according to the Friedewald equation and of waist circumference and age based on the two estimates. It is concluded the LDL-C concentrations estimated by the Friedewald and Martin equations were different, and the Friedewald equation values were significantly lower than those obtained by the Martin equation. MDA was the variable that was most positively associated with the estimated LDL-C levels in all multivariate models. Significant relationships were observed based on the two estimates and occurred for most variables. The methylation levels of the ADRB3 and MTHFR genes were different according to the Martin equation at low LDL-C concentrations (70 mg/dL).

Verification of Low-Density Lipoprotein Cholesterol Levels Measured by Anion-Exchange High-Performance Liquid Chromatography in Comparison with Beta Quantification Reference Measurement Procedure.

A new lipoprotein testing method based on anion-exchange HPLC (AEX-HPLC) was recently established. We verified the accuracy of LDL-C levels, a primary therapeutic target for the prevention of cardiovascular disease (CVD), measured by AEX-HPLC comparing with LDL-C levels measured by beta quantification-reference measurement procedure (BQ-RMP), homogenous assays, and calculation methods. We compared LDL-C levels measured by AEX-HPLC (adLDL-Ch: LDL-Ch and IDL-Ch) and BQ-RMP using blood samples from 52 volunteers. AdLDL-Ch levels were also compared with those measurements by homogeneous assays and calculation methods (Friedewald equation, Martin equation, and Sampson equation) using blood samples from 411 participants with dyslipidemia and/or type 2 diabetes. The precision and accuracy of adLDL-Ch were verified by BQ-RMP. The mean percentage bias [bias (%)] for LDL-C was 1.2%, and the correlation was y = 0.990x + 3.361 (r = 0.990). These results met the acceptable range of accuracy prescribed by the National Cholesterol Education Program. Additionally, adLDL-Ch levels were correlated with LDL-C levels measured by the 2 homogeneous assays (r > 0.967) and the calculation methods (r > 0.939), in serum samples from patients with hypertriglyceridemia. AEX-HPLC is a reliable method for measuring LDL-C levels for CVD risk in daily clinical laboratory analyses.

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

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). To design a more accurate LDL-C equation for patients with a low LDL-C level and/or hypertriglyceridemia. 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. 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. 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. 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.

Remnant cholesterol, coronary atheroma progression and clinical events in statin-treated patients with coronary artery disease.

Remnant cholesterol has been proposed to promote atherosclerotic cardiovascular disease independent of low-density lipoprotein cholesterol, yet the underlying mechanisms are not well understood. We aimed to study the association of remnant cholesterol with coronary atheroma progression and clinical events. We analyzed data from 5754 patients with coronary artery disease undergoing serial intravascular ultrasonography who were enrolled in 10 trials examining various medical therapies. Remnant cholesterol was calculated as (non-high-density lipoprotein cholesterol - low-density lipoprotein cholesterol (estimated using the Hopkins-Martin equation)). Changes in percentage atheroma volume and 2-year major adverse cardiovascular events were compared across various levels of remnant cholesterol, and multivariable models were used to assess the independent relationship of remnant cholesterol with changes in percentage atheroma volume. The mean age was 58.1 ± 9.2 years, 28% were women and 96% received a statin. Percentage atheroma volume progression (changes in percentage atheroma volume > 0) occurred in a linear fashion at on-treatment remnant cholesterol levels of 25 mg/dL or greater. The highest on-treatment remnant cholesterol quartile demonstrated greater percentage atheroma volume progression (+0.53 ± 0.26 vs. -0.15 ± 0.25%, P < 0.001) and 2-year major adverse cardiovascular events (23% vs. 14%, log-rank P < 0.001) compared with the lowest. In multivariable analyses, changes in percentage atheroma volume significantly correlated with on-treatment remnant cholesterol (P < 0.001] independent of low-density lipoprotein cholesterol, apolipoprotein B, C-reactive protein, high-density lipoprotein cholesterol levels and clinical risk factors. Changes in percentage atheroma volume also significantly correlated with changes in remnant cholesterol following multivariable adjustment. In statin-treated patients with atherosclerotic cardiovascular disease, remnant cholesterol was associated with coronary atheroma progression regardless of conventional lipid parameters, C-reactive protein or clinical risk factors. Higher remnant cholesterol levels also correlated with higher major adverse cardiovascular events. These data support further investigations into remnant cholesterol-lowering interventions in statin-treated patients harboring residual atherosclerotic cardiovascular disease risk.

On round dates, acute issues and solving problems of low-parametric equations of state by logical abduction

Within the framework of a simple molecular thermodynamic model, the problems of comparing equations of state (EOS) and the choice of analogs in three sets are analyzed: equations based on the model of interacting point centers, van der Waals type equations, and Martin equations derived from volume translation. A method for the selection of EOS was proposed and approved by calculating the critical isotherms Ar and C4F8. According to the results of the PV calculations, the selected EOSs turned out to be better than known those of Peng-Robinson, Martin, Redlich-Kwong. On the basis of a new characteristic of the equation, a parameter comparing the configuration contributions, a method has been proposed for choosing EOS-analogs in different families. An explanation is given for the impossibility of describing equally well with one equation two states with a reduced density of 2 and 3/2. The results obtained for the EOS of the new model confirm its adequacy in full accordance with the method of abduction.

Impulse breakdown of liquid water - influence of pulse duration and gap distance

In this study, the impulse breakdown of conductive water (350 μS/cm) was carried out under 25~1500 μs pulses and with a gap distance of 0.5~100 mm. The influence of pulse duration, gap distance and voltage polarity on the breakdown characteristics are shown. Martin's equation and streamer linear propagation (SLP) model for breakdown voltage calculation are compared, and their application conditions are indicated. The results show the pulse duration and the gap distance on the breakdown characteristics are opposite; however, their influence on voltage polarity are similar. Martin's equation is only appropriate for short impulse breakdown (nanosecond) while the SLP model is shown to be suitable for long impulse breakdown (microsecond or longer).

Evaluation of Martin's equation for LDL-C estimation in type 2 diabetes mellitus Egyptian patients

IntroductionType 2 Diabetes Mellitus has characteristic dyslipidemia. Low-density lipoprotein cholesterol (LDL-C) measurement plays a role in cardiovascular risk assessment and management. Friedewald equation (FE) has several limitations. This study aims to evaluate the effectiveness of Martin equation (ME) in Egyptian patients, especially those with type 2 diabetes. MethodsA cross-sectional study was conducted on 454 diabetic and non-diabetic patients who were referred to the internal medicine outpatient clinic. Lipid profile was assessed by Cobas 8000 Modular Analyzer. ResultsThe LDL-C was estimated by both FE and ME. In diabetic patients, LDL-C estimated by FE was underestimated with a bias of −3.9 ± 5.3 mg/dL (p = .04). But LDL-C estimated by ME was not significantly different compared to directly measured LDL-C. FE underestimate LDL-C with a bias of −4.6 ± 6.4 mg/dL (p = .042) in uncontrolled diabetic patients. A non-significant difference in both uncontrolled patients and controlled ones was detected by ME. FE had lower sensitivity and specificity (80% and 88.9 respectively) compared to the ME (95.9% sensitivity, and 95.6% specificity). ME was not influenced by triglyceride levels (p = .34). ConclusionThe ME improves concordance of calculated LDL-C with a direct LDL-C assay in Egyptian diabetic patients.

Evaluation of ldl cholesterol: Martin equation versus direct measurement for triglycerides levels above 400 mg/dl

Evaluation of ldl cholesterol: Martin equation versus direct measurement for triglycerides levels above 400 mg/dl

Rheological Properties of Acrylonitrile Terpolymer Solutions Synthesized by Different Methods

Series of polyacrylonitrile terpolymers with molecular weights of (7–20) × 104 have been synthesized in dimethylsulfoxide using classical radical polymerization and reversible addition-fragmentation chain transfer (RAFT) polymerization. The effect of the synthesis method on the rheological behavior of dilute and concentrated solutions of the resulting copolymers has been analyzed. No differences have been found in the viscous properties and viscoelastic behavior of the concentrated solutions in the temperature range of 20–80°C. However, viscometry of the dilute solutions made it possible to detect a difference in the interaction between the solvent and macromolecules of the copolymers obtained by these methods. The copolymers differ in the values of the second virial coefficient, which indicates that the interaction of the solvent and polymer is specific to the synthesis method. This phenomenon seems to be caused by different branching of chains in the copolymer and/or its compositional heterogeneity. For dilute solutions, it was possible to construct a single generalized concentration dependence of reduced viscosity in the coordinates of the Martin equation. Transition to the region of concentrated solutions required an introduction of yet another parameter that describes the effective density of the mesh network, namely, the ratio of the current solution concentration to the crossover concentration. This approach made it possible to construct a generalized dependence of viscosity in the entire concentration range of solutions of polyacrylonitrile copolymers.