Automatic PredICtion of Edema After Stroke

NCT ID: NCT04057690

Last Updated: 2025-09-10

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 1687 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-04-01
• Study Completion Date: 2025-12-31

Brief Summary

To use machine learning for early detection of malignant brain edema in patients with MCA ischemia

Detailed Description

Malignant cerebral edema following large ischemic strokes account for up to 10% of all ischemic strokes. Mortality rates are high and most of the survivors are left severely disabled. Although decompressive craniectomy has been shown to significantly decrease mortality, high morbidity rates among survivors are reported. The optimal timepoint when neurosurgical decompression should be performed in the individual patient varies and is a subject of debate.

Early prediction of malignant brain edema to identify those patients who benefit from surgical treatment is a clinical challenge. The aim of this study is to use machine learning for comprehensive analysis of CT images as well as clinical data from 1500 patients with large ischemic MCA strokes in oder to develop a model for early prediction of malignant brain edema. In a first step algorithms automatically identify characteristic imaging features and clinical data of 1400 retrospective data sets to create a multistage model (learning phase). This is followed by a validation phase where the model is tested with 100 other retrospective data sets.

Conditions

Stroke, Acute; Brain Edema

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

MCA ischemia without malignant edema

MCA ischemia without malignant edema

No interventions assigned to this group

MCA ischemia with malignant edema

MCA ischemia without malignant edema w/o surgical treatment

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• Acute ≥ subtotal MCA infarct (M1-M2 occlusion)
• with or without malignant brain swelling
• with or without reperfusion therapy
• with or without neurosurgical decompression
• with or without death following malignant brain edema

Exclusion Criteria

• Non-acute MCA infarct
• < subtotal MCA infarct

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University Hospital Tuebingen

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Sven Poli, MD MSc

Role: PRINCIPAL_INVESTIGATOR

sven.poli@uni-tuebingen.de

Locations

St. John's Hospital

Vienna, , Austria

Charité Universitätsmedizin Berlin

Berlin, , Germany

Universitätsklinikum Bonn

Bonn, , Germany

Fraunhofer- Gesellschaft zur Förderung der angewandten Forschung e.V., Fraunhofer MEVIS

Bremen, , Germany

Universitätsklinikum Düsseldorf

Düsseldorf, , Germany

Universitätsklinikum Hamburg-Eppendorf

Hamburg, , Germany

Klinikum der Medizinischen Hochschule Hannover

Hanover, , Germany

Universitätsklinikum Heidelberg

Heidelberg, , Germany

Universitätsklinikum Leipzig

Leipzig, , Germany

Klinikum der Ludwig-Maximilians-Universität München

Munich, , Germany

Technische Universität München

Munich, , Germany

Universitätsklinikum Münster

Münster, , Germany

Universitätsklinikum Regensburg

Regensburg, , Germany

Klinikum Stuttgart

Stuttgart, , Germany

University Hospital Tuebingen

Tübingen, , Germany

Hertie Institute for AI in Brain Health

Tübingen, , Germany

Universitätsklinikum Ulm

Ulm, , Germany

Universitätsklinikum Würzburg

Würzburg, , Germany

BRAINOMIX Limited

Oxford, , United Kingdom

Countries

Austria; Germany; United Kingdom

Other Identifiers

APICES

Identifier Type: -

Identifier Source: org_study_id