Assessing Inter-platform Variability of Blood Parameters across Three Automated Platforms: Report from Multi-centric Phenome India Study

BackgroundAccurate and consistent laboratory measurements are essential for reliable diagnosis and clinical decision making. However, variability among automated analyzers can compromise diagnostic reliability and in population studies, instrument-specific differences in estimates hinder meaningful cross-study comparisons. ObjectiveTo evaluate inter-platform variability and interchangeability of laboratory parameters relevant to disease diagnosis across Beckman, Siemens and Roche automated analyzers. MethodsFrom multi-centric Phenome India study, 400 blood samples were analysed for 29 parameters (25 clinical chemistry and 4 immunological) on the three platforms. Inter-platform comparability was assessed using Passing-Bablok regression, Bland-Altman analysis and concordance correlation coefficient (CCC) to evaluate biases and overall agreement. Platform-specific thresholds for low Vitamin B12 were estimated via multivariable regression, adjusting for Folate relative to homocysteine. ResultsInter-platform variability differed across parameters. Enzymatic markers (ALT, AST, ALP, GGT), Urea, Uric acid and HbA1c showed excellent agreement (CCC >0.96). Lipid assays showed mixed concordance: Total cholesterol and Triglycerides correlated strongly, whereas LDL-C and apolipoproteins (ApoA1, ApoB) exhibited systematic bias. Direct Bilirubin also showed divergence. Among immunoassays, Ferritin, Vitamin D and TSH had moderate to strong agreement, but Vitamin B12 showed poor agreement, with low cut-offs, adjusted for Folate and defined by the homocysteine inflection point, at 100 pg/mL (Beckman), 280 pg/mL (Siemens) and 330 pg/mL (Roche). ConclusionDespite consistent results within platforms, inter-platform agreement varies widely by parameter. This underscores the need for local validation, instrument-specific reference intervals and harmonisation to ensure diagnostic accuracy and enable meaningful cross-study comparisons.