Noncontrast CT-Based Deep Learning for Predicting Hematoma Expansion Risk in Patients with Spontaneous Intracerebral Hemorrhage

NCT06602115 | Not Yet Recruiting DMC

• 1 other identifier: K2023-138
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

Hematoma expansion is an independent predictor of poor prognosis and early neurological deterioration in patients with spontaneous intracerebral hemorrhage. Early identification of high-risk patients and timely targeted medical interventions may provide a crucial opportunity to limit hematoma growth and improve neurological outcomes. This study aims to develop an end-to-end deep learning model based on noncontrast computed tomography images to predict the risk of hematoma expansion in patients with spontaneous intracerebral hemorrhage. This model could serve as a valuable risk stratification tool for patients with hematoma expansion, facilitating targeted treatment and providing clinicians with streamlined decision-making support in emergency situations.

Conditions

Spontaneous Intracerebral Hemorrhage; Hematoma Expansion

Keywords

Spontaneous Intracerebral Hemorrhage; Hematoma Expansion; Deep Learning

Outcome Measures

Primary Outcomes (1)

• Prediction of Hematoma Expansion

  Proportion of patients with hematoma expansion

  From the onset of ICH symptoms to 72 hours after baseline CT

Study Arms (2)

Hematoma Expansion Group

Hematoma Expansion Group

Other: Observational study, no interventions involved

No Hematoma Expansion Group

Patients without hematoma expansion as defined in the study

Other: Observational study, no interventions involved

Interventions

Observational study, no interventions involved OTHER

Observational study, no interventions involved

Hematoma Expansion Group; No Hematoma Expansion Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

A multicenter retrospective cohort of 2000 patients with spontaneous intracerebral hemorrhage, including 500 cases of hematoma expansion and 1500 cases without hematoma expansion.

You may qualify if:

• Primary, spontaneous (non-traumatic) intracerebral hemorrhage (ICH).
• Age ≥ 18 years.
• Baseline CT performed within 24 hours of ICH symptom onset or last seen well (LSW).
• Follow-up CT within 72 hours.

You may not qualify if:

• Secondary ICH caused by trauma, vascular anomalies (e.g., aneurysm, cavernous angioma, arteriovenous malformation), brain tumor, or hemorrhagic transformation in brain infarction.
• Primary intraventricular hemorrhage (IVH).
• Surgical treatment with external ventricular drain placement or craniotomy.
• Obvious artifacts observed in CT images.

Sponsors & Collaborators

• Qiang Yu: lead
• Xiangya Hospital of Central South University: collaborator
• The First Affiliated Hospital with Nanjing Medical University: collaborator
• Southwest Hospital, China: collaborator
• Liuzhou Workers' Hospital: collaborator

Study Sites (1)

The First Affiliated Hospital of Chongqing Medical University

Chongqing, Chongqing Municipality, 400016, China

Location

Related Publications (1)

• Tran AT, Zeevi T, Haider SP, Abou Karam G, Berson ER, Tharmaseelan H, Qureshi AI, Sanelli PC, Werring DJ, Malhotra A, Petersen NH, de Havenon A, Falcone GJ, Sheth KN, Payabvash S. Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan. NPJ Digit Med. 2024 Feb 6;7(1):26. doi: 10.1038/s41746-024-01007-w.

  PMID: 38321131 BACKGROUND

MeSH Terms

Interventions

Observation

Intervention Hierarchy (Ancestors)

Methods; Investigative Techniques

Central Study Contacts

Qiang Dr.Yu, MD

CONTACT

+86 23 15023340201; yuqiang0915@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Sponsor-Investigator

Study Record Dates

• First Submitted: September 10, 2024
• First Posted: September 19, 2024
• Study Start: September 25, 2024
• Primary Completion: October 1, 2024
• Study Completion: December 1, 2024
• Last Updated: September 19, 2024

Record last verified: 2024-08

Locations

CN (1)