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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT04186975
Other study ID # 0529-19-RMB
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date December 1, 2019
Est. completion date December 1, 2023

Study information

Verified date December 2019
Source Rambam Health Care Campus
Contact Oren Grunwald, MD
Phone +972506914415
Email oren.grunwald@gmail.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The long term aim of this research is to evaluate a portable NI-FECG (Non-invasive fetal ECG) monitor (Holter NI-FECG) which can be used for regular remote assessment of fetal health in pregnancies at risk or to follow-up on treatments. The elaboration of a NI-FECG Holter device will offer new opportunities for fetal diagnosis and remote monitoring of problematic pregnancies because of its low-cost, non-invasiveness, portability and minimal set-up requirements.


Description:

Pregnant patients that are of gestational age in which fetal heart rate monitoring is recommended and feasible will be enrolled to this cohort study. Each patient will be monitored via conventional fetal heart rate monitoring in addition to the NI-FECG method and both methods will be directly compared. Each patient will be her own control. NI-FECG is a non-invasive method of fetal monitoring' thus no ethical issues are relevant. Nevertheless, each patient will sign informed consent before participating in the study.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 500
Est. completion date December 1, 2023
Est. primary completion date December 1, 2023
Accepts healthy volunteers Accepts Healthy Volunteers
Gender Female
Age group 18 Years to 50 Years
Eligibility Inclusion Criteria:

- Singleton pregnancies.

- low-risk pregnancy: women from the post-date clinic (after 40 weeks' gestation) in which we perform routinely Non stress test and ultrasound.

- High-risk pregnancy: women who are hospitalized for different indications: IUGR, diabetes, hypertension, non-reassuring fetal heart rate, Decreased fetal movements

Exclusion Criteria:

- Non singleton pregnancies.

- Do not want to participate in the study

Study Design


Related Conditions & MeSH terms


Intervention

Device:
Non-Invasive fetal ECG
Non-Invasive fetal ECG
Fetal heart rate monitor
Fetal heart rate monitor

Locations

Country Name City State
n/a

Sponsors (2)

Lead Sponsor Collaborator
Rambam Health Care Campus Technion, Israel Institute of Technology

Outcome

Type Measure Description Time frame Safety issue
Primary Identification of abnormal fetal heart rate from NI-FECG To compare the clinical interpretation of the NI-FECG obtained fetal heart rate trace to the fetal heart rate interpretation from the fetal heart rate obtained from conventional CTG. This will involve blinded reading and scoring of FHR obtained from the NI-FECG and CTG from a panel of expert clinician. 2 years
Primary Computerized NI-FECG for the prediction of abnormal FHR traces To compare the computerized analysis of the FHR trace obtained using NI-FECG to the clinician visual interpretation of the FHR trace obtained using CTG (usual care). This will involve the implementation of algorithms that can detect standard14 and new features assessing the Fetal HRV (FHRV) and the elaboration of a machine learning model which can predict abnormal traces from these features 2 years
Primary Comparison between computerized CTG and NI-FECG To compare the predictive power of computerized CTG versus computerized NI-FECG for the assessment of abnormal traces. For that purpose, a machine learning model will be trained (1) on features extracted from the FHR trace obtained using CTG and (2) on features extracted from the FHR obtained using the NI-FECG trace 4 years
Primary Develop a portable NI-FECG monitor for remote fetal monitoring. To develop a portable NI-FECG monitor which can be used to record the fetal ECG at the patient's home. The monitor will transfer the data to a remote server where source separation will be performed to extract the fetal ECG. Algorithms implemented for extracting characteristic features and the machine learning model will be run to predict whether the traces are normal or abnormal. The elaboration of such algorithm is particularly relevant for resource constrained region where medical experts is scarce. 4 years
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