Cargando…

Development of a biophysical screening model for gestational hypertensive diseases

BACKGROUND: To investigate the possibility of using maternal biophysical parameters only in screening for the different types of gestational hypertensive diseases. METHODS: A total of 969 pregnant women were randomly screened in first and second trimester, of which 8 developed Early-onset Preeclamps...

Descripción completa

Detalles Bibliográficos
Autores principales: Vonck, Sharona, Staelens, Anneleen S., Lanssens, Dorien, Tomsin, Kathleen, Oben, Jolien, Bruckers, Liesbeth, Gyselaers, Wilfried
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528347/
https://www.ncbi.nlm.nih.gov/pubmed/31109316
http://dx.doi.org/10.1186/s12929-019-0530-0
Descripción
Sumario:BACKGROUND: To investigate the possibility of using maternal biophysical parameters only in screening for the different types of gestational hypertensive diseases. METHODS: A total of 969 pregnant women were randomly screened in first and second trimester, of which 8 developed Early-onset Preeclampsia, 29 Late-onset Preeclampsia, 35 Gestational Hypertension and 897 women had a normal outcome. An observational maternal hemodynamics assessment was done via standardized electrocardiogram-Doppler ultrasonography, Impedance Cardiography and bio-impedance, acquiring functional information on heart, arteries, veins and body fluid. Preliminary prediction models were developed to test the screening potential for early preeclampsia, late preeclampsia and gestational hypertension using a Partial Least Square Discriminant Analysis. RESULTS: A combined model using maternal characteristics with cardiovascular parameters in first and second trimester offers high screening performance with Area Under the Curve of 99,9% for Early-onset Preeclampsia, 95,3% for Late-onset Preeclampsia and 94% for Gestational Hypertension. CONCLUSIONS: Using biophysical parameters as fundament for a new prediction model, without the need of biochemical parameters, seems feasible. However, validation in a large prospective study will reveal its true potential.