Clinical trial NCT04057690: Automatic PredICtion of Edema After Stroke
Official title: Automatic Prediction of Malignant Brain Edema After Middle Cerebral Artery Ischemic -Stroke
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Stroke, Acute
Brain Edema
Keywords: Malignant Brain Edema
Stroke, Acute
Prediction
Lead sponsor: University Hospital Tuebingen
Sponsor class: OTHER
First posted: 2019-08-15
Enrollment: 1500
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

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.

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

Primary outcomes:
measure: Number of patients with stroke-related malignant edema after recanalization treatment detected by deep learning algorithms
timeFrame: 4/2019-3/2022
Secondary outcomes:
measure: Number of correctly identified specific imaging findings for early detection of malignant edema
timeFrame: 4/2019-3/2022
Other outcomes:
Locations:
city: Tuebingen
country: Germany
facility: University Hospital Tuebingen
Study officials:
References: