ArticlePloS one2026
Real-world study: Assessing the impact of hemolysis on 48 biochemical and immunological analytes through big data analysis and its feasibility validation.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Beyond the Numbers: Assessing Clinical Risk Associated with Significant Magnesium Assay Bias.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 < 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.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.