Background: Low-density lipoprotein cholesterol (LDL-C) is essential in diagnosing and managing dyslipidemias. While direct assays are faster than the reference beta-quantification method, many labs continue using the Friedewald (FW) equation, despite its limitations. Methods: Two large datasets were analyzed: 10,174 hospital samples (Cobas/Roche) and 21,091 private lab samples (Alinity/Abbott). Various literature-based LDL-C equations were compared, focusing on FW, Sampson (SN), and Martin–Hopkins (MH). Direct LDL-C served as the reference. Evaluation metrics included bias and classification accuracy. Results: In samples with triglycerides < 400 mg/dL, several lesser-known equations showed acceptable bias (±5%), outperforming FW, SN, and MH, which had biases from −7.4% to −4.9%. Classification accuracy was higher with equations like Vujovic (up to 82.5%), compared to FW (65.8%), SN (73.1%), and MH (72.4%). The Vujovic equation showed minimal bias across triglyceride levels and the highest net gain in correct classification (3.4% and 1.57%). Conclusions: Multiple lesser-known LDL-C formulas outperformed widely used ones. The Vujovic equation yielded the best results, but the limited net clinical improvement suggests that replacing Friedewald may not be urgently necessary.

Background: Chronic kidney disease (CKD) is a major global health issue, often underdiagnosed due to its asymptomatic progression. Estimated glomerular filtration rate (eGFR) equations are essential for CKD diagnosis and management, but their performance varies across populations and age groups. This study aimed to compare three validated eGFR equations by assessing their impact on CKD classification and patient reclassification in a Romanian adult population. We retrospectively analyzed 135,630 creatinine results from adult patients admitted to a tertiary care hospital in Romania (2021–2023). eGFR was calculated using CKD-EPI 2009, CKD-EPI 2021, and European Kidney Function Consortium (EKFC) equations. Patients were categorized by age and sex, and classified according to KDIGO GFR stages. Reclassification analysis was focused on two medical decision limits: 60, 30 and 15mL/min/1.73 m². Significant differences in CKD staging were observed across equations. CKD-EPI 2021 consistently led to up-classification of GFR category, with up to 29% of patients over 40 years reclassified into less severe stages. In contrast, EKFC assigned more elderly patients, especially women over 65 years, to stage G3a. Correlation between equations ranged from moderate to strong (τ = 0.59–0.88), with better agreement in older age groups. Reclassification was most frequent around the 60 mL/min/1.73 m² threshold. The choice of eGFR equation substantially impacts CKD classification and may influence clinical decision-making and diagnosis. While EKFC and CKD-EPI 2021 correlate with CKD-EPI 2009, they produce divergent staging patterns. Further validation using measured GFR and outcome data is needed.

Due to technological advancements, haematology analysers are becoming increasingly more complex. Before introducing new analyzers, laboratories must compare the agreement between the new and the old instruments. This study aimed to quantify the method agreement between Sysmex XT-4000i and Alinity hq analysers in order to establish whether they can be used interchangeably. A total of 415 complete blood counts (CBC) from adult patients of the Emergency Clinical County Hospital of Târgu Mureș, Romania, were analysed within 4 h from the collection on Sysmex XT-4000i (considered the reference method), then on Alinity hq. Statistical analysis consisted of outlier removal, Spearman Correlation, Bland–Altman test, and Passing–Bablok regression. For each CBC parameter, the analytical difference between methods was compared with the Reference Change Value (RCV) at medical decision levels (MDL). Despite using different technologies, the instruments have a good agreement regarding cell differentiation and counting. Cell counting and haemoglobin measurement showed a good agreement at all (Medical Decision Limits) MDLs. The analytical difference between methods surpassed the (Reference Change Value) RCV with 1.2% at the 14% MDL of HCT and with 0.2% at the 100 fL MDL of MCV. This study can not tell whether Sysmex or Alinity is superior, only if the two methods agree. The poorer agreement observed for RBC indices, especially MCHC, suggests an accumulation of differences caused by the different working principles of the two methods. However, it is reasonable to assume that such small differences will not affect clinical decision-making and patient outcome.

Low-density lipoprotein cholesterol (LDL-C) remains a key biomarker for cardiovascular risk assessment. While the Friedewald equation is widely used for estimating LDL-C, its accuracy can vary across populations and analytical platforms. This study aimed to develop and validate population-specific, platform-adapted LDL-C equations in two independent cohorts and to compare their performance with the Friedewald equation. A retrospective analysis was conducted using lipid profiles from 31,265 individuals across two datasets: a tertiary hospital (n = 10,174; Roche Cobas platform) and a private laboratory (n = 21,091; Abbott Alinity platform). For each, two linear regression models (50:50 and 80:20 random training-validation splits) were used to develop LDL-C estimation equations using total cholesterol, high-density lipoprotein cholesterol, and triglycerides as predictors. Performance was evaluated by median absolute error (MAE), median percentage error (MPE), and agreement with direct LDL-C in clinical risk categories. The training/validation models performed nearly identically; therefore, only the 50:50 models were retained for the final analysis, with one equation generated for each platform. Both novel equations showed significantly lower MAE (-0.015 to -0.010 mmol/L) and MPE (-0.5% to -0.4%) compared to the Friedewald equation (-0.217 and -0.209 mmol/L MAE; -7.4% and -6.6% MPE) and had more balanced error distributions. The Roche Cobas-derived equation achieved higher overall classification accuracy (85.1%) than the Abbott Alinity-based model (78.6%), while both substantially outperformed Friedewald (67.1% and 65.3%) in all but the <1.03 mmol/L LDL-C category. Platform-specific, population-adapted LDL-C equations offer superior accuracy and risk classification over Friedewald. These findings further support the clinical relevance of implementing such equations; however, broader validation and formal guidance from professional bodies are needed to facilitate their integration into clinical practice.

Objectives: While manual review is the gold standard, automated hematology analyzers are increasingly used. This study assessed the efficiency of white blood cell (WBC)-related flagging messages from the Sysmex XN-1000 and Alinity hq analyzers compared to peripheral blood smear (PBS) findings and evaluated their inter-platform agreement. Methods: K₃EDTA blood samples from hospitalized patients were analyzed using the Sysmex XN-1000. Samples triggering a morphology flag were reanalyzed on the Alinity hq, with PBS reviewed per CLSI protocol H20-A2-2007. Results: Of 5530 samples, 196 had morphology-related flags requiring PBS review. Sysmex flagged 144 samples with leukocyte-related messages; Alinity flagged 120. The positive predictive value (PPV) for the Left Shift flag was 100 % for Sysmex and 77.5 % for Alinity; for Immature Granulocytes, it was 19.4 % for Sysmex and 94.6 % for Alinity. The Blast Flag’s PPVs were 9.3 % for Sysmex and 17.9 % for Alinity. Left Shift specificities were high (>94 %), but sensitivities varied. Sysmex showed 100 % sensitivity for the Blast flag but moderate specificity (53 %), while Alinity performed well (77–82 %). Agreement between platforms ranged from poor to good. Conclusions: Tailored SOPs are crucial for optimizing laboratory workflow based on different flagging performances. Understanding each analyzer’s strengths and limitations improves interpretation and workflow management.

Objectives: Lipid and apolipoprotein measurements are essential for cardiovascular risk assessment, particularly in patients with metabolic syndrome, type 2 diabetes, or borderline lipid values. Despite standardization efforts, analytical discrepancies between platforms may impact clinical decisions. This study aimed to evaluate the comparability of Cobas Integra and Abbott Alinity c for measuring total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), and of Siemens BN ProSpec and Abbott Alinity c for measuring apolipoproteins A1 (ApoA1) and B (ApoB). Methods: Paired plasma samples from 128 statin-treated adults with documented coronary heart disease were analyzed. Method comparison was performed using Passing-Bablok regression and Bland-Altman analysis. Results: Good agreement was observed for TC, TG, and LDL-C, with minor biases and no significant deviation from linearity. HDL-C values measured on Alinity c were consistently lower than those from Cobas Integra (mean bias: −4.5 mg/dL). ApoA1 values were significantly higher on Alinity c than on BN ProSpec (mean bias: −0.49 g/L), suggesting a systematic discrepancy likely related to calibration differences. ApoB showed the highest level of concordance, with negligible bias (−0.09 g/L). Conclusions: The Abbott Alinity c and Siemens BN ProSpec systems demonstrated good overall agreement in measuring lipids and apolipoproteins. However, notable differences in HDL-C and ApoA1 values may affect clinical interpretation, especially in patients with borderline results or when switching between analytical platforms.