NCT04357236

Brief Summary

This original article is a novel investigation on the metabolic characteristics of different patterns of antiepileptic drug (AED) responses in benign epilepsy with centrotemporal spikes (BECTS) patients using 18F-FDG PET imaging. In this study, we demonstrated remitting-relapsing group showed more widespread hypo-metabolism regions than AED responders. Results indicated that metabolic differences had the ability to distinguish the remitting-relapsing patients from AED responders. 18F-FDG PET could be used as a marker to infer the current seizure activity of BECTS. We think that the established hybrid model based on PET and clinical features may be a critical reference for better personalized medication in patients with BECTS.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
55

participants targeted

Target at P25-P50 for all trials

Timeline
Completed

Started Jun 2019

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

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Study Timeline

Key milestones and dates

Study Start

First participant enrolled

June 1, 2019

Completed
10 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

March 30, 2020

Completed
21 days until next milestone

First Submitted

Initial submission to the registry

April 20, 2020

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 20, 2020

Completed
2 days until next milestone

First Posted

Study publicly available on registry

April 22, 2020

Completed
Last Updated

April 22, 2020

Status Verified

April 1, 2020

Enrollment Period

10 months

First QC Date

April 20, 2020

Last Update Submit

April 20, 2020

Conditions

Keywords

Benign Epilepsy with Centrotemporal Spikesglucose metabolismpositron emission tomographymachine learning

Outcome Measures

Primary Outcomes (1)

  • The 'area under curve' (AUC ) of our model in classification performance

    To evaluate the performance of our model, the investigators calculated the AUC of three different logistic regression models based on clinical features, 18F-FDG PET images and a hybrid of both.

    Through study completion, about 6 months

Study Arms (2)

Experimental Group

The experimental group received 18F-FDG PET examination

Control Group

The control group received 18F-FDG PET examination

Eligibility Criteria

Age6 Years - 18 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64)
Sampling MethodNon-Probability Sample
Study Population

Pediatric patients with benign epilepsy with centrotemporal spikes

You may qualify if:

  • clinical diagnosis of BECTS; 2. aging between 6 and 18 years ; 3.taking MRI and EEG examination; 4. taking AEDs as prescribed; 5. continuous 12-month clinical follow-up after 18F-FDG PET examination; 6.the last seizure occurring earlier than 24 h before 18F-FDG PET study

You may not qualify if:

  • any history of neurological disorders, such as head trauma, tumor or infarct

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310009, China

Location

MeSH Terms

Conditions

Epilepsy, Rolandic

Condition Hierarchy (Ancestors)

Epilepsies, PartialEpilepsyBrain DiseasesCentral Nervous System DiseasesNervous System DiseasesEpileptic Syndromes

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

April 20, 2020

First Posted

April 22, 2020

Study Start

June 1, 2019

Primary Completion

March 30, 2020

Study Completion

April 20, 2020

Last Updated

April 22, 2020

Record last verified: 2020-04

Data Sharing

IPD Sharing
Will not share

Locations