Abstract：
Among women with preterm labor, those with intra-amniotic infection present the highest risk of early delivery and the most adverse outcomes. The identification of intra-amniotic infection requires amniocentesis, perceived as too invasive by women and physicians. Noninvasive methods for identifying intra-amniotic infection and/or early delivery are crucial to focus early efforts on high-risk preterm labor women while avoiding unnecessary interventions in low-risk preterm labor women.
This study modeled the best performing models, integrating biochemical data with clinical and ultrasound information to predict a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days.
From 2015 to 2020, data from a cohort of women, who underwent amniocentesis to rule in or rule out intra-amniotic infection or inflammation, admitted with a diagnosis of preterm labor at <34 weeks of gestation at the Hospital Clinic and Hospital Sant Joan de Déu, Barcelona, Spain, were used. At admission, transvaginal ultrasound was performed, and maternal blood and vaginal samples were collected. Using high-dimensional biology, vaginal proteins (using multiplex immunoassay), amino acids (using high-performance liquid chromatography), and bacteria (using 16S ribosomal RNA gene amplicon sequencing) were explored to predict the composite outcome. We selected ultrasound, maternal blood, and vaginal predictors that could be tested with rapid diagnostic techniques and developed prediction models employing machine learning that was applied in a validation cohort.
A cohort of 288 women with preterm labor at <34 weeks of gestation, of which 103 (35%) had a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days, were included in this study. The sample was divided into derivation (n=116) and validation (n=172) cohorts. Of note, 4 prediction models were proposed, including ultrasound transvaginal cervical length, maternal C-reactive protein, vaginal interleukin 6 (using an automated immunoanalyzer), vaginal pH (using a pH meter), vaginal lactic acid (using a reflectometer), and vaginal Lactobacillus genus (using quantitative polymerase chain reaction), with areas under the receiving operating characteristic curve ranging from 82.2% (95% confidence interval, ±3.1%) to 85.2% (95% confidence interval, ±3.1%), sensitivities ranging from 76.1% to 85.9%, and specificities ranging from 75.2% to 85.1%.
The study results have provided proof of principle of how noninvasive methods suitable for point-of-care systems can select high-risk cases among women with preterm labor and might substantially aid in clinical management and outcomes while improving the use of resources and patient experience.
This study modeled the best performing models, integrating biochemical data with clinical and ultrasound information to predict a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days.
From 2015 to 2020, data from a cohort of women, who underwent amniocentesis to rule in or rule out intra-amniotic infection or inflammation, admitted with a diagnosis of preterm labor at <34 weeks of gestation at the Hospital Clinic and Hospital Sant Joan de Déu, Barcelona, Spain, were used. At admission, transvaginal ultrasound was performed, and maternal blood and vaginal samples were collected. Using high-dimensional biology, vaginal proteins (using multiplex immunoassay), amino acids (using high-performance liquid chromatography), and bacteria (using 16S ribosomal RNA gene amplicon sequencing) were explored to predict the composite outcome. We selected ultrasound, maternal blood, and vaginal predictors that could be tested with rapid diagnostic techniques and developed prediction models employing machine learning that was applied in a validation cohort.
A cohort of 288 women with preterm labor at <34 weeks of gestation, of which 103 (35%) had a composite outcome of intra-amniotic infection and/or spontaneous delivery within 7 days, were included in this study. The sample was divided into derivation (n=116) and validation (n=172) cohorts. Of note, 4 prediction models were proposed, including ultrasound transvaginal cervical length, maternal C-reactive protein, vaginal interleukin 6 (using an automated immunoanalyzer), vaginal pH (using a pH meter), vaginal lactic acid (using a reflectometer), and vaginal Lactobacillus genus (using quantitative polymerase chain reaction), with areas under the receiving operating characteristic curve ranging from 82.2% (95% confidence interval, ±3.1%) to 85.2% (95% confidence interval, ±3.1%), sensitivities ranging from 76.1% to 85.9%, and specificities ranging from 75.2% to 85.1%.
The study results have provided proof of principle of how noninvasive methods suitable for point-of-care systems can select high-risk cases among women with preterm labor and might substantially aid in clinical management and outcomes while improving the use of resources and patient experience.
摘要：
背景：在早产妇女中，羊膜腔内感染患者的早期分娩风险最高,不良结局也最多.羊膜腔内感染的鉴定需要羊膜腔穿刺术，被女性和医生认为太有侵略性了。确定羊膜腔内感染和/或早期分娩的非侵入性方法对于将早期工作集中在高风险早产妇女上，同时避免对低风险早产妇女进行不必要的干预至关重要。
目的：本研究模拟了性能最好的模型，将生化数据与临床和超声信息相结合，以预测7天内羊膜腔内感染和/或自发分娩的复合结局。
方法：从2015年到2020年，来自一组女性的数据，进行羊膜穿刺术以排除或排除羊膜腔内感染或炎症，在医院诊所和SantJoandeDéu医院妊娠34周时被诊断为早产，巴塞罗那,西班牙,被使用。入院时,经阴道超声检查,收集母体血液和阴道样本。利用高维生物学，阴道蛋白（使用多重免疫测定法），氨基酸（使用高效液相色谱法），和细菌（使用16S核糖体RNA基因扩增子测序）进行探索以预测复合结果。我们选择了超声波,母亲的血,以及可使用快速诊断技术进行测试的阴道预测因子，并使用机器学习开发了应用于验证队列的预测模型。
结果：288名孕妇在妊娠34周时早产，其中103（35％）在7天内具有羊膜腔内感染和/或自发分娩的复合结局，包括在这项研究中。样本分为衍生组(n=116)和验证组(n=172)。值得注意的是，提出了4种预测模型，包括超声经阴道宫颈长度，母体C反应蛋白，阴道白细胞介素6（使用自动免疫分析仪），阴道pH值（使用pH计），阴道乳酸（使用反射计），和阴道乳杆菌属（使用定量聚合酶链反应），接收工作特性曲线下的面积范围为82.2%(95%置信区间，±3.1%)至85.2%(95%置信区间，±3.1%)，敏感度从76.1%到85.9%，特异性从75.2%到85.1%不等。
结论：研究结果提供了原理证据，证明了适用于即时护理系统的非侵入性方法如何能够在早产妇女中选择高风险病例，并可能在改善资源使用和患者体验的同时对临床管理和结果有很大帮助。
目的：本研究模拟了性能最好的模型，将生化数据与临床和超声信息相结合，以预测7天内羊膜腔内感染和/或自发分娩的复合结局。
方法：从2015年到2020年，来自一组女性的数据，进行羊膜穿刺术以排除或排除羊膜腔内感染或炎症，在医院诊所和SantJoandeDéu医院妊娠34周时被诊断为早产，巴塞罗那,西班牙,被使用。入院时,经阴道超声检查,收集母体血液和阴道样本。利用高维生物学，阴道蛋白（使用多重免疫测定法），氨基酸（使用高效液相色谱法），和细菌（使用16S核糖体RNA基因扩增子测序）进行探索以预测复合结果。我们选择了超声波,母亲的血,以及可使用快速诊断技术进行测试的阴道预测因子，并使用机器学习开发了应用于验证队列的预测模型。
结果：288名孕妇在妊娠34周时早产，其中103（35％）在7天内具有羊膜腔内感染和/或自发分娩的复合结局，包括在这项研究中。样本分为衍生组(n=116)和验证组(n=172)。值得注意的是，提出了4种预测模型，包括超声经阴道宫颈长度，母体C反应蛋白，阴道白细胞介素6（使用自动免疫分析仪），阴道pH值（使用pH计），阴道乳酸（使用反射计），和阴道乳杆菌属（使用定量聚合酶链反应），接收工作特性曲线下的面积范围为82.2%(95%置信区间，±3.1%)至85.2%(95%置信区间，±3.1%)，敏感度从76.1%到85.9%，特异性从75.2%到85.1%不等。
结论：研究结果提供了原理证据，证明了适用于即时护理系统的非侵入性方法如何能够在早产妇女中选择高风险病例，并可能在改善资源使用和患者体验的同时对临床管理和结果有很大帮助。
