Predicting spontaneous preterm birth risk is improved when quantitative ultrasound data are included with prior clinical data

Abstract

Predicting women at risk for spontaneous preterm birth (sPTB) has been medically challenging due to lack of signs and symptoms of preterm labor until intervention is too late. Hypothesis: advanced statistical modeling predicts sPTB risk from quantitative ultrasound (QUS) plus prior data (prior birth history through first clinical cervical length) better than that of prior data alone. Study population included 250 full-term births (FTBs) and 25sPTBs. QUS scans (Siemens S2000 & MC9-4) were performed using a standard cervical length approach by registered diagnostic medical sonographers. Two cervical QUS scans were conducted at 20 ± 2 and 24 ± 2 wk gestation. Multiple QUS features were processed from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: (1) QUS plus prior data and (2) prior data alone. Test ROC AUC compared both models. Using statistical methods, QUS plus prior data identified women at risk for sPTB with better AUC (0.68; std error 0.01; 95% CI, 0.66–0.70) than that of prior data alone (0.63; std error 0.01; 95% CI, 0.61–0.65). Even with only 25 sPTBs, data suggest that there is value added for predicting sPTB when QUS data are included with prior data. [R01HD089935.]