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Fetal cardiac function in pregnancy affected by congenital heart disease: protocol for a multicentre prospective
Anna Erenbourg1, Tracie Barber2, Vera Cecotti3
1UNSW School of Clinical Medicine, Perinatal Imaging Research Group (PIRG), Level 0, Royal Hospital for Women, Barker Street (Locked Bag 2000), Sydney, NSW, 2031, Australia. a.erenbourg@unsw.edu.au.
Insights
This study investigates automated cardiac function measurements in fetuses with congenital heart disease (CHD). Automated parameters may help identify fetuses at risk for cardiac failure, improving early detection and management of fetal cardiac conditions.
Area of Science:
- Cardiology
- Fetal Medicine
- Medical Imaging
Background:
- Congenital heart disease (CHD) is the most common fetal malformation, leading to cardiac dysfunction and failure.
- Current functional parameter assessments lack standardization and repeatability, hindering clinical translation.
- Automated techniques offer a potential solution to overcome these limitations in evaluating fetal cardiac function.
Purpose of the Study:
- To evaluate automated cardiac function parameters in fetuses with CHD compared to healthy controls.
- To determine if automated parameters can identify fetuses at risk of cardiac failure.
- To assess the potential of automated parameters to improve hydrops prediction.
Main Methods:
- A multicenter cohort study involving 330 healthy and 165 CHD-affected pregnancies.
- Utilizing automated pulsed wave Doppler (PWD) myocardial performance index (MPI) and spatio-temporal image correlation (STIC) for assessing cardiac function.
- Employing generalized linear mixed models and logistic regression to analyze functional parameters and hydrops incidence.
Main Results:
- The study aims to provide evidence on significant differences in automated functional parameters between fetuses with CHD and healthy controls.
- Primary objective: Compare automated PWD-MPI and STIC-derived parameters (TAPSE, MAPSE, SAPSE).
- Secondary objective: Estimate the predictive value of these parameters for hydrops.
Conclusions:
- Automated cardiac function parameters show potential for differentiating fetuses with CHD from healthy ones.
- These automated techniques may enhance the early identification of fetuses at risk of cardiac failure.
- The findings could lead to improved diagnostic and prognostic tools in fetal cardiology.
Background:
Congenital heart disease (CHD) is the most common fetal malformation, and it can result first in cardiac remodeling and dysfunction and later in cardiac failure and hydrops. A limited number of studies have evaluated cardiac function in fetuses affected by CHD. Functional parameters could potentially identify fetuses at risk of cardiac failure before its development. However, these techniques have not translated from research to clinical settings, due to a lack of standardization and poor repeatability. We seek to evaluate whether application of automated techniques to a cohort with fetal pathology could overcome these factors.
Methods:
A multicenter cohort study will be carried out in eight teaching hospitals across Europe, Australia, and Middle East. Based on a previous observed standard deviation, a total sample of 381 pregnancies is required to achieve 80% power to detect a difference of 0.03 in mean myocardial performance index (MPI) with a two-sided type I error rate of 5%. After adjustments allowing for patient exclusions or incomplete datasets, a total of 330 healthy singleton pregnancies and 165 diagnosed with CHD will be recruited. Two fetal cardiac function evaluations at 19 + 6-28 + 6 and 32 + 6-36 + 6 weeks will be offered assessing automated pulsed wave doppler (PWD) MPI, spatio-temporal image correlation (STIC) annular and septal plane excursion (TAPSE, MAPSE and SAPSE), alongside cardiac morphometric and Doppler evaluations of flow across the valves. A secondary nested case-control study will evaluate fetuses with hydrops compared to those without. Differences in functional parameters between cases and controls and over time will be assessed using generalized linear mixed models. Logistic regression will estimate the association between cardiac parameters and hydrops' incidence.
Discussion:
This study will provide evidence as to whether automated functional parameters could be significantly different in pregnancy affected by CHD versus healthy pregnancies. The primary objective is to compare automated PWD-MPI and STIC TAPSE, MAPSE and SAPSE in fetuses affected by CHD versus healthy. The secondary objective is to estimate whether these automated parameters could improve the predictive value of the classical cardiovascular profile score in case of hydrops.
Trial Registration:
The study protocol has been registered in the ClinicalTrials.gov Protocol Registration System, identification number NCT05698277.
