Automatic PredICtion of Edema After Str… | Clinical Trial

Key information

Conditions

Stroke, Acute Brain Diseases Brain Edema Cardiovascular Diseases Central Nervous System Diseases Cerebrovascular Disorders Nervous System Diseases Stroke Vascular Diseases

Sex eligibility

All sexes

Study type

Observational

Primary location

St. John's Hospital, Vienna, Austria

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

Number of patients with stroke-related malignant edema after recanalization treatment detected by deep learning algorithms

Time frame: 4/2019-3/2022

Deep learning algorithms will be used for automatic identification of specific image findings and specific clinical data that indicate a stroke-related malignant edema. Primary outcome measures are Sensitivity/Specificity/negative predictive value/positive predictive value of early detection of patients developing stroke-related malignant edema based on initial CT and 24 hour follow up CT and clinical parameters.

Secondary outcomes

Number of correctly identified specific imaging findings for early detection of malignant edema

Time frame: 4/2019-3/2022

Used specific imaging findings for early detection of malignant brain edema are Collateral status, Clot Burden Score, Vein Score, Change in CSF volume. In this study the specific image findings are manually annotated and also automatically detected using deep learning algorithms. Secondary outcome measures are Sensitivity/Specificity/NPV/PPV of specific imaging findings identified by deep learning algorithms.

Sponsors and collaborators

Lead sponsor

University Hospital Tuebingen

Other

Registry information

Official study title

Automatic Prediction of Malignant Brain Edema After Middle Cerebral Artery Ischemic -Stroke

Acronym: APICES

Important dates

Study start: 2019
Primary completion: 2023
Study completion: 2025
First posted: Aug 15, 2019
Registry last updated: Sep 10, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed