Evidence map›Paper›PMID 41854968›Full record

ReviewAnnals of biomedical engineering2026

From Early Models to Emerging Trends: The Evolution of Computational Hemolysis Prediction.

Ilaria Guidetti, Maria Laura Costantino, Francesco De Gaetano

Abstract readReview
In one paragraph

Review in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ilaria GuidettiPolitecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", LaBS, Piazza Leonardo da Vinci, 32, 20133, Milan, Italy. ilaria.guidetti@polimi.it.ORCID http://orcid.org/0009-0004-5890-2514
Maria Laura CostantinoPolitecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", LaBS, Piazza Leonardo da Vinci, 32, 20133, Milan, Italy.ORCID http://orcid.org/0000-0001-6974-2692
Francesco De GaetanoPolitecnico di Milano, Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", LaBS, Piazza Leonardo da Vinci, 32, 20133, Milan, Italy.ORCID http://orcid.org/0000-0002-7597-020X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mechanical hemolysis remains one of the most critical complications associated with blood-contacting devices. Computational prediction of blood damage has been widely adopted as a key tool for evaluating the hemolytic potential of cardiovascular devices. Although numerical hemolysis modeling has been investigated since the 1990s, a universally accurate and predictive approach is still lacking. This review traces the evolution of computational hemolysis models, from the earliest stress-based power-law formulation to recent strain-based approaches that describe the red blood cells at cellular and molecular levels. It examines the various definitions of the power-law shear stress found in literature, including Von Mises-like formulations, extensions incorporating extensional or Reynolds stresses and those based on turbulent dissipation rate or deformation of the red blood cell. The broad range of power-law constants reported in literature is summarized, together with their development conditions. Furthermore, the Lagrangian and Eulerian approaches used for numerical hemolysis predictions are analyzed in detail. Finally, emerging trends and future directions are highlighted, offering insights into the pathways toward more reliable hemolysis modeling.

Indexed as

ErythrocytesHemolysisModels, CardiovascularAnimalsComputer SimulationHumansStress, MechanicalEulerian approachHemolysis prediction modelsLagrangian approachMechanical blood damageStrain-based modelsStress-based models

Identifiers

PMID41854968
PMCPMC13291019

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Registered trials

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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.