Clinical trial NCT04991779: AI-assisted cEEG Diagnosis of Neonatal Seizures in Neonatal Intensive Care Unit
Official title: A Multicenter Diagnostic Study on the Utility of AI-assisted Continue EEG Diagnosis of Neonatal Seizures
Study type: OBSERVATIONAL
Overall status: WITHDRAWN
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
First posted: 2021-08-05
Enrollment: 0
Sex: ALL
Minimum age (years): 0
Maximum age (years): 28
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Neonates with suspected seizures and high risk of seizures

Brief summary: A diagnostic accuracy study on Artificial intelligence assisted continue EEG diagnostic tool is to carried out comparing with manually EEG interpretation as the golden standard for neonatal seizure.

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 incidence of neonatal seizures is variable based on gestational age. The etiology of seizures may indicate the presence of a potentially treatable etiology and should prompt an immediate evaluation to determine the cause and to initiate etiology-specific therapy. Importantly, the earlier treatment of seizures positively affects the infant's long-term neurological development. However, even when continue electroencephalogram (cEEG) monitoring is available, the availability of on-site expertise to interpret cEEG signals is limited and in practice, the diagnosis is still based only on clinical signs. The previous study indicated that the reliable seizure detection was as little as 10% of seizure events. Therefore, an early automated seizure detection tool has been developed based on machine learning. The lack of an automated seizure detection tool has been validated in the external neonatal seizures cohort. The evidence on the utility of the automated seizure detection tool remains uncertain. This is a prospective, continuous double-blind designed diagnostic accuracy study. The study aims to validate the accuracy of the artificial intelligence (AI)-assisted cEEG diagnostic tool comparing the manually cEEG interpretation as the golden standard of neonatal seizure in neonatal intensive care units. Analysis of sensitivity and specificity is to evaluate the accuracy of AI-assisted cEEG diagnostic tool.

Eligibility criteria: Inclusion Criteria:

* Postnatal age \< or = 28 days;
* cEEG monitoring at least 12hours monitoring;
* Suspected seizures;
* Risk of Intracranial hemorrhage;
* Abnormality of MRI or ultrasound before cEEG;
* Neonates diagnosed with encephalopathy or suspected of encephalopathy;
* Hypoxic-ischemic encephalopathy or suspected hypoxic-ischemic encephalopathy;
* Metabolic disturbances (Hypoglycemia, Hypocalcemia, Hypomagnesemia, Inborn errors of metabolism);
* Central nervous system (CNS) or systemic infections;
* Postsurgical neonatal within 3 days;
* Suspected genetic disease 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 accuracy of AI-assisted cEEG diagnostic tool in evaluating the neonatal seizure
timeFrame: within 7 days since the end of cEEG monitoring during hospitalization
Secondary outcomes:
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, Ph.D
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.