Code for data analysis.

BackgroundThis research utilizes clinical laboratory real-world data to explore the influence of in vitro hemolysis on 48 biochemical and immunological analytes, aiming to propose and validate the methods and tools based on big data for analysis of the impact of hemolysis on laboratory analytes.MethodsThis research initially employs univariate analysis to display the levels and distribution of 48 analytes across different H-index groups. Subsequently, it utilizes quantile regression models to analyze the impact of hemolysis on laboratory analytes, adjusting for age, gender, patient type, and PVD, with the magnitude of impact described using β values and 95% CIs, visualized through error bar graphs. Finally, the study compares its results with those obtained from homogenized experimental research using the same testing platforms and hemolysis assessment methods, validating the feasibility of conducting research based on big data.ResultsAdjusting for gender, age, patient type, and PVD, hemolysis showed a significant positive interference on ALT, Alb, TBil, GGT, AST, CK, LD, K, P, Mg, and FFA(P ConclusionsThe impact of hemolysis on laboratory analytes can be effectively evaluated through comprehensive big data analysis, demonstrating a level of consistency comparable to that of homogeneous experimental research.