An observational study in Stroke, Acute and Brain Edema, sponsored by University Hospital Tuebingen. Active, not recruiting at 19 sites in 3 countries. Per ClinicalTrials.gov, last updated 2025-09-10.
Sponsored by University Hospital Tuebingen · Observational
To use machine learning for early detection of malignant brain edema in patients with MCA ischemia
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.
8,255 studies on the registry are indexed under Stroke; 2,056 are open to participants now.
This study's enrollment of 1,687 is above the median of 152 across 1,968 observational studies indexed under Stroke.
Browse Stroke studies →University Hospital Tuebingen is the lead sponsor of 490 studies on the registry; 108 are open to participants now.
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1500 retrospective datasets will be collected from 5 large German stroke units. Data sets include imaging data and clinical data from patients with subtotal MCA infarcts (M1-M2 occlusion), with or without malignant brain swelling, with or without reperfusion therapy, with or without neurosurgical decompression, and with or without death following malignant brain edema. Data sets from patients who have died following malignant brain edema will be included. Each data set consists of initial NCCT, CTA, (DSA if available), and follow-up NCCT until 14 days after stroke onset as well as clinical data.
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Exclusion Criteria:
MCA ischemia without malignant edema
MCA ischemia without malignant edema w/o surgical treatment
Number of patients with stroke-related malignant edema after recanalization treatment detected by deep learning algorithms
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.
Time frame: 4/2019-3/2022
Number of correctly identified specific imaging findings for early detection of malignant edema
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.
Time frame: 4/2019-3/2022
This study is active, not recruiting, as verified in Sep 2025. You cannot join it, but the record below documents what was studied.
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