The Effect of AI-assisted cEEG Diagnosis on the Administration of Antiseizure Medication in Neonatal Seizures
NCT ID: NCT05036395
Last Updated: 2023-04-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
NA
1000 participants
INTERVENTIONAL
2022-03-16
2024-03-10
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
OTHER
DOUBLE
Study Groups
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The neonates evaluated by the routine assessment protocol and AI-assisted cEEG Diagnostic tool
This group will be monitored by cEEG with standard operating procedure. The cEEG recording will be evaluated by neonatologists with the routine assessment protocol and AI assisted cEEG diagnostic tool in real time during cEEG monitoring. Both real-time cEEG and amplitude-integrated EEG traces are displayed at the bedside for clinical review.
This group will follow the standard clinical protocols of the recruiting hospitals for ASM administration after the neonatologists' review.
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 neonates evaluated by the routine assessment protocol
This group will be monitored by cEEG with standard operating procedure. The cEEG recording will be evaluated by neonatologists with the routine assessment protocol during cEEG monitoring. Both real-time cEEG and amplitude-integrated EEG traces are displayed at the bedside for clinical review.
This group will follow the standard clinical protocols of the recruiting hospitals for ASM administration after the neonatologists' review.
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* 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
0 Days
6 Months
ALL
No
Sponsors
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Chengdu Women's and Children's Central Hospital
OTHER
Xiamen Children's Hospital
OTHER
Kunming Children's Hospital
OTHER
The Affiliated Hospital Of Southwest Medical University
OTHER
Children's Hospital of Zhengzhou University
UNKNOWN
Children's Hospital of Fudan University
OTHER
Responsible Party
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Principal Investigators
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Wenhao Zhou
Role: STUDY_CHAIR
Children's Hospital of Fudan University
Locations
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Henan Children's Hospital
Zhengzhou, Henan, China
Children Hospital of Fudan University
Shanghai, Shanghai Municipality, China
Chengdu Women's and Children's Central Hospital
Chengdu, Sichuan, China
Countries
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Central Contacts
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Facility Contacts
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Jing Guo, MD
Role: primary
Xuhong Hu
Role: primary
References
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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.
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.
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.
Other Identifiers
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CHFudanU_NNICU17
Identifier Type: -
Identifier Source: org_study_id
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