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High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo
Published on: November 8, 2018
2D Shear Wave Elastography: An Evolving Technique for Comparison of Placental Elasticity in Normal and Preeclamptic
Ritika Sihmar1, Shahina Bano1, Renuka Malik2
1Department of Radiodiagnosis, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, New Delhi, India.
Objective:
This article evaluates differences in placental elasticity between normal and preeclamptic pregnancies using shear wave elastography and assesses the potential of the placental elasticity values in predicting preeclampsia.
Materials And Methods:
The study included 60 pregnant women, 30 diagnosed with preeclampsia and 30 with normal pregnancies, in their second and third trimesters. Shear wave elastography was performed to assess placental elasticity. Both mean (average) and maximum elasticity values were recorded and cutoff values were determined using receiver operating characteristic (ROC) curve analysis to evaluate their potential in predicting preeclampsia.
Results:
Placental elasticity values were significantly higher in preeclamptic women (group A) compared with healthy pregnant women (group B). The mean elasticity in group A was 15.74 ± 3.51 kPa, with a maximum elasticity of 27.4 ± 4.66 kPa; whereas in group B, the corresponding values were 4.42 ± 1.93 and 7.13 ± 3.05 kPa, respectively ( p < 0.0001). In preeclampsia cases, the shear wave modulus was higher in the central region of the placenta than at the edges. ROC curve analysis evaluated a mean elasticity cutoff value of 8.1 kPa, with a sensitivity of 100%, specificity of 96.67%, positive predictive value of 96.8%, negative predictive value of 100%, and a diagnostic accuracy of 98.33%.
Conclusion:
The study revealed significant differences in placental elasticity between preeclamptic and healthy pregnancies, highlighting the potential of shear wave elastography as a valuable tool for early prediction of preeclampsia. Early prediction using this method could significantly reduce maternal and perinatal mortality and morbidity, especially in developing countries, by identifying cases before the onset of clinical symptoms.
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