Evidence map›Paper›PMID 40615756›Full record

ArticleAnnals of biomedical engineering2025

Quantifying Experimental Variability in Shear-Induced Hemolysis to Support Uncertainty-Aware Hemolysis Models.

Christopher Blum, Markus Mous, Ulrich Steinseifer, Johanna C Clauser, Michael Neidlin

Abstract read
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Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Christopher BlumDepartment of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany.
Markus MousDepartment of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany.
Ulrich SteinseiferDepartment of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany.
Johanna C ClauserDepartment of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany.
Michael NeidlinDepartment of Cardiovascular Engineering, Institute of Applied Medical Engineering, Medical Faculty, RWTH Aachen University, Aachen, Germany. neidlin@ame.rwth-aachen.de.ORCID http://orcid.org/0000-0002-5582-6905

Funding

Deutsche Forschungsgemeinschaft 467133626
6 · The paper itself

Abstract

purposeNumerical hemolysis models rely on experimental data to fit parameters and predict hemolysis under various conditions. However, existing experiments often use few replicates per condition, leaving inherent variability largely unaddressed. This can lead to oversimplified models that fail to capture the true nature of hemolysis. Here, we quantify intra- and inter-donor variability at a single, well-defined shear stress and exposure time and examine how sample size affects measurement precision

methodsHuman blood from five healthy donors was subjected to a fixed shear stress and exposure time condition. For each donor, 20 independent measurements were performed to calculate a hemolysis index (HI). Intra-donor variability (variation within a single donor's measurements) and inter-donor variability (variation between donor means) were compared. Additionally, bootstrap analyses were used to explore the effect of the sample size on the confidence intervals of the mean HI.

resultsIntra-donor variability was approximately four times higher than inter-donor variability, indicating that most of the uncertainty originated from within a single donor's set of samples rather than between donors. Increasing the sample size from 2 to 20 replicates substantially narrowed the confidence intervals of the mean hemolysis estimate, suggesting that commonly used small sample sizes may underrepresent the true variability in hemolysis measurements.

conclusionIntra-donor variability is a significant driver of uncertainty in hemolysis measurements at a fixed shear stress and exposure time condition, surpassing differences among donors. Obtaining robust and reliable hemolysis estimates requires increasing the number of replicate measurements to reduce uncertainty. Integrating these insights into future experimental designs and uncertainty-aware hemolysis models will improve the reliability of in silico predictions and inform safer, more effective blood-contacting medical device designs.

Indexed as

HemolysisModels, BiologicalModels, CardiovascularAdultFemaleHumansMaleStress, MechanicalExperimental hemolysisInter-variabilityIntra-variabilityUncertainty quantification

Identifiers

PMID40615756
PMCPMC12457503

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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.