Comparing three cardiothoracic ratio measurement techniques and creating multivariable scoring system to predict

Sanitra Anuwutnavin1, Patsawee Rangseechamrat2, Nalat Sompagdee2

  • 1Department of Obstetrics and Gynaecology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Prannok Road, Bangkoknoi, Bangkok, 10700, Thailand. asanitra@hotmail.com.

Scientific Reports
|April 17, 2024
PubMed

Insights

Cardiothoracic (CT) diameter ratio and middle cerebral artery-peak systolic velocity (MCA-PSV) show promise for predicting hemoglobin Bart

Area of Science:

  • Obstetrics and Gynecology
  • Fetal Medicine
  • Diagnostic Imaging

Background:

  • Hemoglobin Bart's disease is a severe inherited blood disorder.
  • Prenatal diagnosis is crucial for managing affected pregnancies.
  • Ultrasound markers are being explored for non-invasive prediction.

Purpose of the Study:

  • To evaluate cardiothoracic (CT) ratio techniques (diameter, circumference, area) for predicting Hemoglobin Bart's disease.
  • To develop a multivariable scoring system using ultrasound markers for improved diagnostic accuracy.

Main Methods:

  • Prospective study of 151 singleton pregnancies at risk for Hemoglobin Bart's disease.
  • Assessment of three CT ratio techniques and other ultrasound markers before invasive testing.
  • Analysis of diagnostic performance and identification of significant predictors.

Main Results:

  • CT diameter ratio showed higher sensitivity compared to circumference and area methods.
  • Significant predictors included CT diameter ratio > 0.5, MCA-PSV > 1.5 MoM, and placental thickness > 3 cm.
  • Middle cerebral artery-peak systolic velocity (MCA-PSV) demonstrated the highest sensitivity (97.8%) for affected fetuses.

Conclusions:

  • CT diameter ratio is a valuable ultrasound marker for predicting Hemoglobin Bart's disease.
  • A multivariable scoring system incorporating CT diameter ratio, MCA-PSV, and placental thickness achieved excellent diagnostic performance (100% sensitivity, 84.9% specificity).
  • Multivariable analysis significantly enhances predictive capabilities over single-parameter assessments.

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