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

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Abstract Background Accurate 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. Objective To evaluate inter-platform variability and interchangeability of laboratory parameters relevant to disease diagnosis across Beckman, Siemens and Roche automated analyzers. Methods From 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. Results Inter-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). Conclusion Despite 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.

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