PDF(Pubmed)
Abstract：
BACKGROUND: Historically, clinicians have relied on medical risk factors and clinical symptoms for preterm birth risk assessment. In nulliparous women, clinicians may rely solely on 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 assessment of preterm birth risk.
OBJECTIVE: This study aimed to investigate the efficiency of quantitative ultrasound measurements at relatively early stages of pregnancy to enhance identification of women who might be at risk for spontaneous preterm birth.
METHODS: 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 2 research screenings conducted at 20±2 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 a likelihood ratio test of quantitative ultrasound features, cross-validated receiver operating characteristic curve analysis, sensitivity, and specificity.
RESULTS: On the basis of 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 the area under the curve (0.63±0.03) relative to 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<.01), with an area under the curve of 0.69±0.03.
CONCLUSIONS: Accurate identification of women at risk for spontaneous preterm birth solely through historical clinical features has been proven to be difficult. In this study, a history of preterm birth was the most significant historical clinical predictor of preterm birth risk, but the historical clinical predictive model performance was not statistically significantly better than the no-skill level. According to our study results, including quantitative ultrasound yields a statistically significant improvement in risk prediction as the pregnancy progresses.
OBJECTIVE: This study aimed to investigate the efficiency of quantitative ultrasound measurements at relatively early stages of pregnancy to enhance identification of women who might be at risk for spontaneous preterm birth.
METHODS: 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 2 research screenings conducted at 20±2 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 a likelihood ratio test of quantitative ultrasound features, cross-validated receiver operating characteristic curve analysis, sensitivity, and specificity.
RESULTS: On the basis of 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 the area under the curve (0.63±0.03) relative to 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<.01), with an area under the curve of 0.69±0.03.
CONCLUSIONS: Accurate identification of women at risk for spontaneous preterm birth solely through historical clinical features has been proven to be difficult. In this study, a history of preterm birth was the most significant historical clinical predictor of preterm birth risk, but the historical clinical predictive model performance was not statistically significantly better than the no-skill level. According to our study results, including quantitative ultrasound yields a statistically significant improvement in risk prediction as the pregnancy progresses.
摘要：
背景：历史上，临床医生依靠医学危险因素和临床症状进行早产风险评估.在未产妇女中，临床医生可能仅依靠报告的症状来评估早产风险.怀孕期间常规使用超声提供了结合宫颈定量超声扫描的机会，以潜在地改善对早产风险的评估。
目的：本研究旨在探讨在妊娠早期定量超声测量的效率，以增强对可能有自发性早产风险的妇女的识别。
方法：对接受芝加哥伊利诺伊大学医院护理的志愿者参与者进行了一项对孕妇的前瞻性队列研究。伊利诺伊州。参与者接受标准临床筛查，然后在20±2和24±2周进行2次研究筛查。在研究筛查期间，由注册的诊断医学超声医师使用标准的宫颈长度方法进行了定量超声扫描。从校准的原始射频反向散射信号计算定量超声特征。分析包括足月分娩结局和自发性早产结局。医学上表明的早产被排除在分析之外。使用每次访问的数据，采用Akaike信息标准特征选择进行logistic回归,根据历史临床和定量超声特征得出每个时间范围的预测模型.模型评估包括定量超声特征的似然比测试，交叉验证的接收器工作特性曲线分析，灵敏度,和特异性。
结果：仅根据历史临床特征，最佳预测模型的估计曲线下接收器工作特征面积为0.56±0.03。到第1次就诊的时间范围,相对于仅使用历史临床特征的预测模型,使用历史临床和定量超声特征的预测模型提供了曲线下面积(0.63±0.03)的适度改善。在第2次访视的时间范围内，使用历史临床和定量超声特征的预测模型提供了显着改善（似然比检验，P<.01),曲线下面积为0.69±0.03。
结论：仅通过历史临床特征准确识别有自发性早产风险的妇女已被证明是困难的。在这项研究中,早产史是早产风险的最重要的历史临床预测指标，但历史临床预测模型表现在统计学上并不显著优于无技能水平.根据我们的研究结果,随着妊娠的进展，包括定量超声在风险预测方面具有统计学上的显着改善。
目的：本研究旨在探讨在妊娠早期定量超声测量的效率，以增强对可能有自发性早产风险的妇女的识别。
方法：对接受芝加哥伊利诺伊大学医院护理的志愿者参与者进行了一项对孕妇的前瞻性队列研究。伊利诺伊州。参与者接受标准临床筛查，然后在20±2和24±2周进行2次研究筛查。在研究筛查期间，由注册的诊断医学超声医师使用标准的宫颈长度方法进行了定量超声扫描。从校准的原始射频反向散射信号计算定量超声特征。分析包括足月分娩结局和自发性早产结局。医学上表明的早产被排除在分析之外。使用每次访问的数据，采用Akaike信息标准特征选择进行logistic回归,根据历史临床和定量超声特征得出每个时间范围的预测模型.模型评估包括定量超声特征的似然比测试，交叉验证的接收器工作特性曲线分析，灵敏度,和特异性。
结果：仅根据历史临床特征，最佳预测模型的估计曲线下接收器工作特征面积为0.56±0.03。到第1次就诊的时间范围,相对于仅使用历史临床特征的预测模型,使用历史临床和定量超声特征的预测模型提供了曲线下面积(0.63±0.03)的适度改善。在第2次访视的时间范围内，使用历史临床和定量超声特征的预测模型提供了显着改善（似然比检验，P<.01),曲线下面积为0.69±0.03。
结论：仅通过历史临床特征准确识别有自发性早产风险的妇女已被证明是困难的。在这项研究中,早产史是早产风险的最重要的历史临床预测指标，但历史临床预测模型表现在统计学上并不显著优于无技能水平.根据我们的研究结果,随着妊娠的进展，包括定量超声在风险预测方面具有统计学上的显着改善。
