During veno-venous extracorporeal membrane oxygenation (vv ECMO) therapy, antimicrobial drugs are frequently used, and appropriate dosing is challenging due to there being limited data to support the dosage. Linezolid is effective against multidrug-resistant Gram-positive pathogens frequently isolated in ECMO patients. In total, 53 steady-state linezolid levels were obtained following 600 mg intravenous (IV) injections every 8 h, and these were used to develop a population pharmacokinetic (PopPK) model in patients with COVID-19-associated acute respiratory distress syndrome (CARDS) on vv ECMO. The data were analyzed using a nonlinear mixed-effects modelling approach. Monte Carlo simulation generated 5000 patients’ individual PK parameters and corresponding concentration–time profiles using the PopPK model, following the administration of 600 mg/8 h (a higher-than-standard dosing) and 600 mg/12 h (standard). The probabilities of pharmacokinetic/pharmacodynamic (PK/PD) target attainment (PTA) and the cumulative fraction of responses (CFR) for three pathogens were calculated and compared between the two dosing scenarios. Linezolid 600 mg/8 h was predicted to achieve greater than or equal to 85%Tf>MIC in at least 90% of the patients with CARDS on vv ECMO compared to only approximately two thirds of the patients after dosing every 12 h at a minimal inhibitory concentration (MIC) of 2 mg/L. In addition, for the same MIC, fAUC₂₄/MIC ≥ 80 was achieved in almost three times the number of patients following an 8-h versus a 12-h interval. PopPK simulation predicted that a significantly higher proportion of the patients with CARDS on vv ECMO would achieve the PK/PD targets following the 8-h dosing interval compared to standard linezolid dosing. Nevertheless, the safety concern, in particular, for thrombocytopenia, with higher-than-standard linezolid dosage is reasonable, and consequently, monitoring is essential.

Background/Objectives: Low-density lipoprotein cholesterol (LDL-C) is a primary therapeutic target in cardiovascular risk assessment. While direct assays are available, LDL-C is frequently estimated using various formulas, whose performance can vary by population and lipid concentration. This study evaluates the analytical agreement and clinical classification performance of four formulas (Spasić–Kotur–Vujović, Friedewald, De Long, and Martin–Hopkins) against directly measured LDL-C. Methods: Lipid profiles from 3935 fasting adult patients were analyzed. LDL-C was directly measured via a homogeneous assay and compared with calculated values using linear regression and Bland–Altman analyses. Analytical bias and classification accuracy were assessed across stratified lipid levels. Results: Among the evaluated equations, the Spasić–Kotur–Vujović formula showed the closest agreement with directly measured LDL-C. It had the lowest mean absolute bias and mean percentage differences across most lipid strata, as well as narrower limits of agreement compared with Friedewald, De Long, and Martin–Hopkins formulas. Although all formulas showed strong correlations with directly measured LDL-C (Pearson’s r correlation coefficient = 0.956–0.958; intraclass correlation coefficient > 0.974), systematic underestimation of LDL-C was observed for all equations and became more pronounced with increasing total cholesterol, LDL-C, triglycerides, and non-high-density lipoprotein cholesterol concentrations. The Spasić–Kotur–Vujović formula achieved the highest overall classification accuracy (70%), followed by De Long (68%), Martin–Hopkins (61%), and Friedewald (58%). Conclusions: Despite strong correlations, clinically relevant differences in bias and classification exist among LDL-C formulas. The Spasić–Kotur–Vujović formula demonstrated superior agreement and accuracy in this population, suggesting it is a more reliable tool for routine laboratory practice. It should be noted that these findings reflect agreement with the specific homogeneous LDL-C assay used on the Alinity c platform (Abbott Laboratories,, Chicago, IL, USA) and may not be directly generalizable to other analytical systems.