Automatic PredICtion of Edema After Stroke (APICES)

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

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

Study Overview

• Status: Recruiting
• Conditions: Stroke, Acute; Brain Edema

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.

• Study Type: Observational
• Enrollment (Anticipated): 1500

Contacts and Locations

• Study Contact: Name: Sven Poli, MD MSc; Phone Number: 83269 +497071290; Email: sven.poli@uni-tuebingen.de
• Study Contact Backup: Name: Julia Zeller, MBA; Phone Number: 68293 +497071290; Email: julia.zeller@med.uni-tuebingen.de

Study Locations

• Germany
  • Tuebingen, Germany, 72076
    • Recruiting
    • University Hospital Tuebingen
    • Contact: Sven Poli, MD MSc; Phone Number: 83269 +497071290; Email: sven.poli@uni-tuebingen.de
    • Contact: Julia Zeller, MBA; Phone Number: 68293 +497071290; Email: julia.zeller@med.uni-tuebingen.de

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: ADULT; OLDER_ADULT; CHILD
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
MCA ischemia without malignant edema
MCA ischemia with malignant edema; MCA ischemia without malignant edema w/o surgical treatment

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	4/2019-3/2022

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	4/2019-3/2022

Collaborators and Investigators

• Sponsor: University Hospital Tuebingen

Study record dates

Study Major Dates

• Study Start (ACTUAL): April 1, 2019
• Primary Completion (ANTICIPATED): December 31, 2021
• Study Completion (ANTICIPATED): March 31, 2023

Study Registration Dates

• First Submitted: April 25, 2019
• First Submitted That Met QC Criteria: August 13, 2019
• First Posted (ACTUAL): August 15, 2019

Study Record Updates

• Last Update Posted (ACTUAL): May 3, 2021
• Last Update Submitted That Met QC Criteria: April 30, 2021
• Last Verified: November 1, 2020

More Information

Terms related to this study

• Keywords: Prediction; Stroke, Acute; Malignant Brain Edema
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Vascular Diseases; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Stroke; Edema; Brain Edema
• Other Study ID Numbers: APICES

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No