Evidence map›Paper›PMID 41601695›Full record

ArticleFrontiers in immunology2025

Classification of angioedema types using decision tree modeling.

Felix Aulenbacher, Annika Gutsche, Henriette Farkas, Kinga Viktória Kőhalmi, Emek Kocatürk, Emel Aygören-Pürsün, Ludovic Martin, Hilary Longhurst, Petra Staubach, Andrea Zanichelli and 5 more

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Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing 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

The trial behind it

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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

15 authors.

Felix AulenbacherAngioedema Center of Reference and Excellence (ACARE), Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Annika GutscheAngioedema Center of Reference and Excellence (ACARE), Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Henriette FarkasHungarian Angioedema Center of Reference and Excellence, Department of Internal Medicine and Haematology, Semmelweis University, Budapest, Hungary.
Kinga Viktória KőhalmiHungarian Angioedema Center of Reference and Excellence, Department of Internal Medicine and Haematology, Semmelweis University, Budapest, Hungary.
Emek KocatürkAngioedema Center of Reference and Excellence (ACARE), Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Emel Aygören-PürsünAngioedema Center, Internal Medicine and Hemostaseology, Department for Children and Adolescents, University Hospital Frankfurt, Goethe University, Frankfurt, Germany.
Ludovic MartinDepartment of Dermatology, Angers University Hospital, Angers, France.
Hilary LonghurstDepartment of Immunology, Auckland City Hospital, Auckland, New Zealand.
Petra StaubachDepartment of Dermatology, University Medical Center Mainz, Mainz, Germany.
Andrea ZanichelliDepartment of Biomedical Sciences for Health, University of Milan, Milan, Italy.
Werner AbererDepartment of Dermatology, Medical University of Graz, Graz, Austria.
Anette BygumClinical Institute, University of Southern Denmark, Odense, Denmark.
Mignon van den ElzenDepartment of Dermatology/Allergology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Thomas ButtgereitAngioedema Center of Reference and Excellence (ACARE), Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Markus MagerlAngioedema Center of Reference and Excellence (ACARE), Institute of Allergology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: All angioedema (AE) presents with transient, localized swelling; however, the underlying causes, prognosis, and treatments vary significantly. Consequently, identifying a specific AE type is challenging. Methods: We aimed to apply a machine learning (ML) model to improve AE diagnosis. Random forest (RF) ML was used to create a prediction model for diagnosing correct AE types. Development comprised a literature search to establish AE's clinical characteristics, developing and translating questions in collaboration with 12 European AE centers, and selecting, testing, validating and optimizing the established ML model. Analysis included 342 specialist-diagnosed patients with one of six AE types. Results: The final optimized RF model correctly identified AE types with true positive rates of up to 94% in hereditary AE due to C1 inhibitor deficiency (C1INH), with a Percentage Accuracy of 89·2% and a Kappa value of 81·8% across the six AE types, with a high agreement with the diagnoses made by experts. Discussion: This is the first ever reported ML algorithm designed to pre-assess to aid AE diagnosis.

Indexed as

AngioedemaDecision TreesAlgorithmsClassification AlgorithmsHumansMachine LearningPrediction AlgorithmsPredictive Learning ModelsRandom Forestangioedemabradykinin-mediated angioedemaclinical decision supportdiagnostic classificationhereditary angioedemamachine learningmast cell-mediated angioedemaRandom Forest

Identifiers

PMID41601695
PMCPMC12833243

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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.