Clinical trial NCT05036395: The Effect of AI-assisted cEEG Diagnosis on the Administration of Antiseizure Medication in Neonatal Seizures
Official title: AI-assisted cEEG Diagnosis of Neonatal Seizures to Optimize the Administration of Antiseizure Medication: a Multicenter, Randomised, Controlled Trial
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Neonatal Seizure
Lead sponsor: Children's Hospital of Fudan University
Sponsor class: OTHER
Collaborators: name: Chengdu Women's and Children's Central Hospital
name: Xiamen Children's Hospital
name: Kunming Children's Hospital
name: The Affiliated Hospital Of Southwest Medical University
name: Children's Hospital of Zhengzhou University
First posted: 2021-09-05
Enrollment: 1000
Sex: ALL
Minimum age (years): 0
Maximum age (years): 6
Standard ages: CHILD
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

Brief 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).

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: The percentage of the individuals with the inappropriate administration of ASM
timeFrame: Immediately after the end of cEEG monitoring
Secondary outcomes:
measure: Gesell Developmental Schedules (GDS)
timeFrame: at corrected gestational age of 6 months
measure: Total electrographic seizure times per hour (second/hour)
timeFrame: Immediately after the end of cEEG monitoring
measure: The mortality of neonates
timeFrame: Immediately after discharge
Other outcomes:
Locations:
city: Zhengzhou
country: China
facility: Henan Children's Hospital
state: Henan
city: Shanghai
country: China
facility: Children Hospital of Fudan University
state: Shanghai
city: Chengdu
country: China
facility: Chengdu Women's and Children's Central Hospital
state: Sichuan
Study officials:
affiliation: Children's Hospital of Fudan University
name: Wenhao Zhou
role: STUDY_CHAIR
References:
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 treatment using continuous EEG monitoring: a multicentre experience. Arch Dis Child Fetal Neonatal Ed. 2019 Sep;104(5):F493-F501. doi: 10.1136/archdischild-2018-315624. Epub 2018 Nov 24.
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):611-7. doi: 10.1097/WNP.0b013e31823e96d7. No abstract available.
citation: 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 2020 Dec 15.