Enhanced Identification of At-Risk Women for Preterm Birth via Quantitative Ultrasound: A Prospective Cohort Study

Historically, clinicians have relied upon medical risk factors and clinical symptoms for preterm birth risk assessment. In nulliparous women, clinicians may rely solely on the reported symptoms to assess for the risk of preterm birth. The routine use of ultrasound during pregnancy offers the opportunity to incorporate Quantitative Ultrasound scanning of the cervix to potentially improve the precision of preterm birth risk. The goal of this study was to investigate the efficiency of Quantitative Ultrasound measurements at relatively early stages during pregnancy to enhance identification of women who might be at risk for spontaneous preterm birth. A prospective cohort study of pregnant women was conducted with volunteer participants receiving care from the University of Illinois Hospital in Chicago, Illinois. Participants received a standard clinical screening followed by two research screenings conducted at 20 ± 2 weeks and 24 ± 2 weeks. Quantitative Ultrasound scans were performed during research screenings by registered diagnostic medical sonographers using a standard cervical length approach. Quantitative Ultrasound features were computed from calibrated raw radiofrequency backscattered signals. Full-term birth outcomes and spontaneous preterm birth outcomes were included in the analysis. Medically indicated preterm births were excluded from the analysis. Using data from each visit, logistic regression with Akaike Information Criterion feature selection was conducted to derive predictive models for each time frame based on historical clinical and Quantitative Ultrasound features. Model evaluations included likelihood ratio test of Quantitative Ultrasound features, cross-validated receiver operator characteristic curve analysis, sensitivity, and specificity. Based on historical clinical features alone, the best predictive model had an estimated receiver operating characteristic area under the curve of 0.56 ± 0.03. By the time frame of Visit 1, a predictive model using both historical clinical and Quantitative Ultrasound features provided a modest improvement in area under the curve (0.63 ± 0.03) versus that of the predictive model using only historical clinical features. By the time frame of Visit 2, the predictive model using historical clinical and Quantitative Ultrasound features provided significant improvement (likelihood ratio test, p < 0.01) with the area under the curve (0.69 ± 0.03). Accurate identification of women at risk for spontaneous prete