Evaluation of Cervical Elastography Strain Pattern to Predict Preterm Birth

Infant, Newborn Cervix Uteri 3. Good health 03 medical and health sciences 0302 clinical medicine Cervical Length Measurement Predictive Value of Tests Pregnancy Case-Control Studies Elasticity Imaging Techniques Humans Premature Birth Female
DOI: 10.1055/a-0865-1711 Publication Date: 2019-03-25T23:52:48Z
ABSTRACT
Abstract Purpose To evaluate cervical elastography strain pattern as a predictive marker for spontaneous preterm delivery (SPTD). Materials and Methods In this case-control study cervical length (CL) and elastographic data (strain ratio, elastography index, strain pattern score) were acquired from 335 pregnant women (20th – 34th week of gestation) by transvaginal ultrasound. Data of 50 preterm deliveries were compared with 285 normal controls. Strain ratio and elastography index were calculated by placing two regions of interest (ROIs) in parallel on the anterior cervical lip. The strain ratio was determined by dividing the higher strain value by the lower one. The elastography index was defined as the maximum of the strain ratio curve. Elastographic images were assigned a new established strain pattern (SP) score between 0 and 2 according to the distribution of strain induced by compression. Results Elastography index, SP score and CL differed between preterm and normal pregnancies (1.61 vs. 1.27, p < 0.001; SP score value of “2”: n = 31 (62 %) vs. n = 36 (12.6 %), p < 0.001; CL 30.7 vs. 41.0 mm, p < 0.001; respectively). The elastography index and SP score were associated with a higher predictive potential than CL measurement alone (AUC 0.8059 (area under the curve); AUC 0.7716; AUC 0.7631; respectively). A combination of all parameters proved more predictive than any single parameter (AUC 0.8987; respectively). Conclusion Higher elastography index and SP scores were correlated with an elevated risk of SPTD and are superior to CL measurement as a predictive marker. A combination of these parameters could be used as a “Cervical Index” for the prediction of SPTD.
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