Automatic PredICtion of Edema After Stroke (APICES)

University Hospital Tuebingen

Status

Unknown

Conditions

Brain Edema

Stroke, Acute

Study type

Observational

Funder types

Other

Identifiers

NCT04057690

APICES

Details and patient eligibility

About

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

Full 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.

Enrollment

1,500 estimated patients

Sex

All

Volunteers

No Healthy Volunteers

Inclusion criteria