Comparing three cardiothoracic ratio measurement techniques and creating multivariable scoring system to predict Bart’s hydrops fetalis at 17–22 weeks’ gestation
Issued Date
2024-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85190697860
Journal Title
Scientific Reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.14 No.1 (2024)
Suggested Citation
Anuwutnavin S., Rangseechamrat P., Sompagdee N., Ruangvutilert P., Viboonchard S. Comparing three cardiothoracic ratio measurement techniques and creating multivariable scoring system to predict Bart’s hydrops fetalis at 17–22 weeks’ gestation. Scientific Reports Vol.14 No.1 (2024). doi:10.1038/s41598-024-59719-8 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/98104
Title
Comparing three cardiothoracic ratio measurement techniques and creating multivariable scoring system to predict Bart’s hydrops fetalis at 17–22 weeks’ gestation
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Abstract
To assess the diagnostic performance of three cardiothoracic (CT) ratio techniques, including diameter, circumference, and area, for predicting hemoglobin (Hb) Bart’s disease between 17 and 22 weeks’ gestation, and to create a multivariable scoring system using multiple ultrasound markers. Before invasive testing, three CT ratio techniques and other ultrasound markers were obtained in 151 singleton pregnancies at risk of Hb Bart’s disease. CT diameter ratio demonstrated the highest sensitivity among the other techniques. Significant predictors included CT diameter ratio > 0.5, middle cerebral artery-peak systolic velocity (MCA-PSV) > 1.5 multiples of the median, and placental thickness > 3 cm. MCA-PSV exhibited the highest sensitivity (97.8%) in predicting affected fetuses. A multivariable scoring achieved excellent sensitivity (100%) and specificity (84.9%) for disease prediction. CT diameter ratio exhibited slightly outperforming the other techniques. Increased MCA-PSV was the most valuable ultrasound marker. Multivariable scoring surpassed single-parameter analysis in predictive capabilities.