Predicting Spontaneous Pre-term Birth Risk Is Improved When Quantitative Ultrasound Data Are Included With Historical Clinical Data

Predicting women at risk for spontaneous pre-term birth (sPTB) has been medically challenging because of the lack of signs and symptoms of pre-term birth until interventions are too late. We hypothesized that prediction of the sPTB risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared with using only HC data. HC data defined herein included birth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment and physical examination data.The study population included 248 full-term births (FTBs) and 26 sPTBs. QUS scans (Siemens S2000 and MC9-4) were performed by registered diagnostic medical sonographers using a standard cervical length approach. Two cervical QUS scans were conducted at 20 ± 2 and 24 ± 2 wk of gestation. Multiple QUS features were evaluated from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: (i) HC data alone and (ii) combined HC and QUS data. Model comparisons included a likelihood ratio test, cross-validated receiver operating characteristic area under the curve, sensitivity and specificity. The study's birth outcomes were only FTBs and sPTBs; medically induced pre-term births were not included.Combined HC and QUS data identified women at risk of sPTB with better AUC (0.68, 95% confidence interval [CI]: 0.57-0.78) compared with HC data alone (0.53, 95% CI: 0.40-0.66) and HC data + cervical length at 18-20 wk of gestation (average AUC = 0.51, 95% CI: 0.38-0.64). A likelihood ratio test for significance of QUS features in the classification model was highly statistically significant (p < 0.01).Even with only 26 sPTBs among 274 births, value was added in predicting sPTB when QUS data were included with HC data.