Automatic PredICtion of Edema After Stroke (APICES)
|The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.|
|ClinicalTrials.gov Identifier: NCT04057690|
Recruitment Status : Recruiting
First Posted : August 15, 2019
Last Update Posted : August 15, 2019
|Condition or disease|
|Stroke, Acute Brain Edema|
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.
|Study Type :||Observational|
|Estimated Enrollment :||1500 participants|
|Official Title:||Automatic Prediction of Malignant Brain Edema After Middle Cerebral Artery Ischemic -Stroke|
|Actual Study Start Date :||April 1, 2019|
|Estimated Primary Completion Date :||March 31, 2020|
|Estimated Study Completion Date :||March 31, 2022|
MCA ischemia without malignant edema
MCA ischemia without malignant edema
MCA ischemia with 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 [ 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.
- 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.
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04057690
|Contact: Sven Poli, MD MSc||+497071290 ext email@example.com|
|Contact: Julia Zeller, MBA||+497071290 ext firstname.lastname@example.org|
|University Hospital Tuebingen||Recruiting|
|Tuebingen, Germany, 72076|
|Contact: Sven Poli, MD MSc +497071290 ext 83269 email@example.com|
|Contact: Julia Zeller, MBA +497071290 ext 68293 firstname.lastname@example.org|