The Effect of AI-assisted cEEG Diagnosis on the Administration of Antiseizure Medication in Neonatal Seizures

Neonatal Seizure Clinical Trial

Official title:

AI-assisted cEEG Diagnosis of Neonatal Seizures to Optimize the Administration of Antiseizure Medication: a Multicenter, Randomised, Controlled Trial

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05036395
• Other study ID #: CHFudanU_NNICU17
• Status: Recruiting
• Phase: N/A
• Start date: March 16, 2022
• Est. completion date: March 10, 2024

Study information

• Verified date: April 2023
• Source: Children's Hospital of Fudan University
• Contact: Wenhao Zhou, Ph.D
• Phone: +86-21-64931913
• Email: zhouwenhao[@]fudan.edu.cn
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

This is a prospective randomised clinical trial study to test an artificial intelligence (AI)-assisted continuous electroencephalogram(cEEG) diagnostic tool for optimizing the administration of antiseizure medication (ASM) in neonatal intensive care units(NICUs).

Description:

The occurrence of neonatal seizures may be the first, and perhaps the only, clinical sign of a central nervous system disorder in the newborn infant. The promoted treatment of seizures can limit the secondary injury to the brain and positively affect the infant's long-term neurological development. However, the current antiseizure medication (ASM) are both overused and underused. Studies indicated that early automated seizure detection tool had a high diagnostic accuracy of neonatal seizures. However, there is little evidence that early automated seizure detection tool could the optimize the administration of ASM and improved the neurological outcomes in neonatal seizures. Therefore, the primary study aim is to investigate whether the utility of AI assisted cEEG diagnostic tool could optimize the administration of ASM in NICUs.

This project will enroll the neonates with suspected or high risk of seizures who will receive at least 72 hours cEEG monitoring during hospitalization. All the cEEG monitoring methodology is standardized across recruiting hospitals.

The intervention will be an artificial intelligence (AI)-assisted continues electroencephalogram (cEEG) diagnostic tool.

The individuals were randomly allocated to one of the two groups using a predetermined randomisation sequence and block randomisation generator (block of 4). The group 1 will be monitored with cEEG and the cEEG recording will be assessed by neonatologists with AI assisted cEEG diagnostic tool in real time during cEEG monitoring. The group 2 will be monitored with cEEG and the cEEG recording will be assessed by neonatologists when as routine during cEEG monitoring. Both groups will follow the standard clinical protocols for ASM administration of the recruiting hospitals The reference standard is the electrographic seizures interpreted by 3 clinicians who had attended the uniformly training program and were certified by the Chinese Anti-Epilepsy Association. These 3 clinicians are blinded to the group allocation.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: March 10, 2024
• Est. primary completion date: March 10, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 0 Days to 6 Months

Eligibility

Inclusion Criteria:

• Postnatal age < or = 28 days;

• cEEG monitoring at least 24hours monitoring;

• Suspected seizures;

• Abnormal movement;

• Brain infarction;

• Risk of Intracranial hemorrhage;

• Abnormality of brain MRI or ultrasound;

• Hypoxic-ischemic encephalopathy or suspected Hypoxic-ischemic encephalopathy;

• Central nervous system (CNS) or systemic infections;

• Suspected genetic diseases or Positive genetic diagnoses;

Exclusion Criteria:

• The neonates with head scalp defect, scalp hematoma, edema and other contraindications which are not suitable for cEEG monitoring during hospitalization.

Related Conditions & MeSH terms

• Neonatal Seizure
• Seizures

Intervention

Other:
• The routine assessment protocol and AI-assisted cEEG Diagnostic tool
The AI-assisted cEEG diagnostic tool is an automated seizure reporting system, including a quantitively EEG neural signal processing pipeline to extract features from the original signal datasets, machine learning models based on gradient boosted model for prediction. The tool can report electrographic seizures in real time during cEEG monitoring. The neonatologists will evaluate the neonates by AI-assisted cEEG diagnostic tool, clinical conditions, real-time cEEG and amplitude-integrated EEG traces. The investigators will make a decision after review the neonates clinical conditions, AI-assisted cEEG diagnostic report, the cEEG and amplitude-integrated EEG.
• The routine assessment protocol
The routine assessment protocol is that the neonatologists will evaluate the neonates by clinical conditions, real-time cEEG and amplitude-integrated EEG traces.

Locations

Country	Name	City	State
China	Chengdu Women's and Children's Central Hospital	Chengdu	Sichuan
China	Children Hospital of Fudan University	Shanghai	Shanghai
China	Henan Children's Hospital	Zhengzhou	Henan

Sponsors (6)

Lead Sponsor	Collaborator
Children's Hospital of Fudan University	Chengdu Women's and Children's Central Hospital, Children's Hospital of Zhengzhou University, Kunming Children's Hospital, The Affiliated Hospital Of Southwest Medical University, Xiamen Children's Hospital

Country where clinical trial is conducted

China, 

References & Publications (3)

Hoodbhoy Z, Masroor Jeelani S, Aziz A, Habib MI, Iqbal B, Akmal W, Siddiqui K, Hasan B, Leeflang M, Das JK. Machine Learning for Child and Adolescent Health: A Systematic Review. Pediatrics. 2021 Jan;147(1):e2020011833. doi: 10.1542/peds.2020-011833. Epub — View Citation

Rennie JM, de Vries LS, Blennow M, Foran A, Shah DK, Livingstone V, van Huffelen AC, Mathieson SR, Pavlidis E, Weeke LC, Toet MC, Finder M, Pinnamaneni RM, Murray DM, Ryan AC, Marnane WP, Boylan GB. Characterisation of neonatal seizures and their treatmen — View Citation

Shellhaas RA, Chang T, Tsuchida T, Scher MS, Riviello JJ, Abend NS, Nguyen S, Wusthoff CJ, Clancy RR. The American Clinical Neurophysiology Society's Guideline on Continuous Electroencephalography Monitoring in Neonates. J Clin Neurophysiol. 2011 Dec;28(6 — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The percentage of the individuals with the inappropriate administration of ASM	The inappropriate administration of ASM is defined: (1) the administration of an ASM before the electrographic seizure episode; or (2) an ASM is given to the neonates without electrographic seizure episode.	Immediately after the end of cEEG monitoring
Secondary	Gesell Developmental Schedules (GDS)	The GDS comprise comprehensive checklists for assessing neuromotor wholeness, functional maturity, and mental development of infants and toddlers from the perspectives of adaptability, large exercise, fine motor skills, language, and personal-social networking. The GDS score provides an objective assessment of neurological and mental development in this age group.	at corrected gestational age of 6 months
Secondary	Total electrographic seizure times per hour (second/hour)	Total electrographic seizure times per hour (second/hour) is defined as total duration of all seizures in every hour from the start of the EEG monitoring to the end of the cEEG monitoring.	Immediately after the end of cEEG monitoring
Secondary	The mortality of neonates	The proportion of the deceased neonates	Immediately after discharge