Noninvasive prediction models of intra-amniotic infection in women with preterm labor
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...
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| Veröffentlicht in: | American journal of obstetrics and gynecology 2023-01, Vol.228 (1), p.78.e1-78.e13 |
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| container_title | American journal of obstetrics and gynecology |
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| creator | Cobo, Teresa Burgos-Artizzu, Xavier P. Collado, M. Carmen Andreu-Fernández, Vicente Sanchez-Garcia, Ana B. Filella, Xavier Marin, Silvia Cascante, Marta Bosch, Jordi Ferrero, Silvia Boada, David Murillo, Clara Rueda, Claudia Ponce, Júlia Palacio, Montse Gratacós, Eduard |
| description | 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 |
| doi_str_mv | 10.1016/j.ajog.2022.07.027 |
| format | Article |
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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.</description><identifier>ISSN: 0002-9378</identifier><identifier>EISSN: 1097-6868</identifier><identifier>DOI: 10.1016/j.ajog.2022.07.027</identifier><identifier>PMID: 35868419</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>amniocentesis ; Amniocentesis - methods ; Amniotic Fluid - microbiology ; Chorioamnionitis - microbiology ; Female ; Humans ; Infant, Newborn ; Inflammation - metabolism ; intra-amniotic infection ; multivariable prediction models ; Obstetric Labor, Premature - diagnosis ; Pregnancy ; preterm labor ; spontaneous preterm delivery</subject><ispartof>American journal of obstetrics and gynecology, 2023-01, Vol.228 (1), p.78.e1-78.e13</ispartof><rights>2022 Elsevier Inc.</rights><rights>Copyright © 2022 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-3873552afda53b7857bdfbd13416a01c5c3c9c4997661ce474a678b6f7a315653</citedby><cites>FETCH-LOGICAL-c356t-3873552afda53b7857bdfbd13416a01c5c3c9c4997661ce474a678b6f7a315653</cites><orcidid>0000-0003-3130-1829 ; 0000-0002-5869-4629</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ajog.2022.07.027$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35868419$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cobo, Teresa</creatorcontrib><creatorcontrib>Burgos-Artizzu, Xavier P.</creatorcontrib><creatorcontrib>Collado, M. Carmen</creatorcontrib><creatorcontrib>Andreu-Fernández, Vicente</creatorcontrib><creatorcontrib>Sanchez-Garcia, Ana B.</creatorcontrib><creatorcontrib>Filella, Xavier</creatorcontrib><creatorcontrib>Marin, Silvia</creatorcontrib><creatorcontrib>Cascante, Marta</creatorcontrib><creatorcontrib>Bosch, Jordi</creatorcontrib><creatorcontrib>Ferrero, Silvia</creatorcontrib><creatorcontrib>Boada, David</creatorcontrib><creatorcontrib>Murillo, Clara</creatorcontrib><creatorcontrib>Rueda, Claudia</creatorcontrib><creatorcontrib>Ponce, Júlia</creatorcontrib><creatorcontrib>Palacio, Montse</creatorcontrib><creatorcontrib>Gratacós, Eduard</creatorcontrib><title>Noninvasive prediction models of intra-amniotic infection in women with preterm labor</title><title>American journal of obstetrics and gynecology</title><addtitle>Am J Obstet Gynecol</addtitle><description>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.</description><subject>amniocentesis</subject><subject>Amniocentesis - methods</subject><subject>Amniotic Fluid - microbiology</subject><subject>Chorioamnionitis - microbiology</subject><subject>Female</subject><subject>Humans</subject><subject>Infant, Newborn</subject><subject>Inflammation - metabolism</subject><subject>intra-amniotic infection</subject><subject>multivariable prediction models</subject><subject>Obstetric Labor, Premature - diagnosis</subject><subject>Pregnancy</subject><subject>preterm labor</subject><subject>spontaneous preterm delivery</subject><issn>0002-9378</issn><issn>1097-6868</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE1LxDAQhoMo7rr6BzxIj15a89EkLXiRxS9Y9OKeQ5pONaVt1qS74r83ZVePXmYYeN4X5kHokuCMYCJu2ky37j2jmNIMywxTeYTmBJcyFYUojtEcY0zTkslihs5CaKeTlvQUzRiPQE7KOVq_uMEOOx3sDpKNh9qa0boh6V0NXUhck9hh9DrV_WDdaE08G9gjdki-XA9x2vFjyo7g-6TTlfPn6KTRXYCLw16g9cP92_IpXb0-Pi_vVqlhXIwpKyTjnOqm1pxVsuCyqpuqJiwnQmNiuGGmNHlZSiGIgVzmWsiiEo3UjHDB2QJd73s33n1uIYyqt8FA1-kB3DYoKuL3kmFRRJTuUeNdCB4atfG21_5bEawmnapVk0416VRYqqgzhq4O_duqh_ov8usvArd7IMqCnQWvgrEwmOjRR02qdva__h8vmobJ</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Cobo, Teresa</creator><creator>Burgos-Artizzu, Xavier P.</creator><creator>Collado, M. 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Carmen</creatorcontrib><creatorcontrib>Andreu-Fernández, Vicente</creatorcontrib><creatorcontrib>Sanchez-Garcia, Ana B.</creatorcontrib><creatorcontrib>Filella, Xavier</creatorcontrib><creatorcontrib>Marin, Silvia</creatorcontrib><creatorcontrib>Cascante, Marta</creatorcontrib><creatorcontrib>Bosch, Jordi</creatorcontrib><creatorcontrib>Ferrero, Silvia</creatorcontrib><creatorcontrib>Boada, David</creatorcontrib><creatorcontrib>Murillo, Clara</creatorcontrib><creatorcontrib>Rueda, Claudia</creatorcontrib><creatorcontrib>Ponce, Júlia</creatorcontrib><creatorcontrib>Palacio, Montse</creatorcontrib><creatorcontrib>Gratacós, Eduard</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>American journal of obstetrics and gynecology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cobo, Teresa</au><au>Burgos-Artizzu, Xavier P.</au><au>Collado, M. Carmen</au><au>Andreu-Fernández, Vicente</au><au>Sanchez-Garcia, Ana B.</au><au>Filella, Xavier</au><au>Marin, Silvia</au><au>Cascante, Marta</au><au>Bosch, Jordi</au><au>Ferrero, Silvia</au><au>Boada, David</au><au>Murillo, Clara</au><au>Rueda, Claudia</au><au>Ponce, Júlia</au><au>Palacio, Montse</au><au>Gratacós, Eduard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Noninvasive prediction models of intra-amniotic infection in women with preterm labor</atitle><jtitle>American journal of obstetrics and gynecology</jtitle><addtitle>Am J Obstet Gynecol</addtitle><date>2023-01</date><risdate>2023</risdate><volume>228</volume><issue>1</issue><spage>78.e1</spage><epage>78.e13</epage><pages>78.e1-78.e13</pages><issn>0002-9378</issn><eissn>1097-6868</eissn><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.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>35868419</pmid><doi>10.1016/j.ajog.2022.07.027</doi><orcidid>https://orcid.org/0000-0003-3130-1829</orcidid><orcidid>https://orcid.org/0000-0002-5869-4629</orcidid></addata></record> |
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| subjects | amniocentesis Amniocentesis - methods Amniotic Fluid - microbiology Chorioamnionitis - microbiology Female Humans Infant, Newborn Inflammation - metabolism intra-amniotic infection multivariable prediction models Obstetric Labor, Premature - diagnosis Pregnancy preterm labor spontaneous preterm delivery |
| title | Noninvasive prediction models of intra-amniotic infection in women with preterm labor |
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