# Real-world study: Assessing the impact of hemolysis on 48 biochemical and immunological analytes through big data analysis and its feasibility validation

## TL;DR

This study uses real-world clinical data to assess how hemolysis affects 48 biochemical and immunological analytes and validates big data analysis as a feasible method.

## Contribution

The study proposes and validates big data-based methods to evaluate hemolysis impact on lab analytes using real-world data.

## Key findings

- Hemolysis significantly affects analytes like ALT, TBil, and GGT with high statistical significance.
- Big data analysis showed 93% concordance with experimental results, validating its feasibility.
- Negative interference was observed for analytes like DBil, Na, and Cr due to hemolysis.

## Abstract

This 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.

This 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.

Adjusting 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 < 0.001)., and a significant negative interference on DBil, Na, Cl, TCO2, and Cr (P < 0.001). High hemolysis levels also negatively interfere UA, PA, and GA. No consistent pattern of significance was observed for other analytes. Our multivariate analysis, when compared to experimental data, revealed a 93.0% concordance, with discrepancies noted in GGT, ALP, and RF.

The 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.