Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
Preterm delivery is a common pregnancy complication that can result in significant neonatal morbidity and mortality. Limited tools exist to predict preterm birth, and none to predict neonatal morbidity, from early in pregnancy. The objective of this study was to determine if the progesterone metabol...
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| description | Preterm delivery is a common pregnancy complication that can result in significant neonatal morbidity and mortality. Limited tools exist to predict preterm birth, and none to predict neonatal morbidity, from early in pregnancy. The objective of this study was to determine if the progesterone metabolites 11-deoxycorticosterone (DOC) and 16-alpha hydroxyprogesterone (16α-OHP), when combined with patient demographic and obstetric history known during the pregnancy, are predictive of preterm delivery-associated neonatal morbidity, neonatal length of stay, and risk for spontaneous preterm delivery prior to 32 weeks' gestation.
We conducted a cohort study of pregnant women with plasma samples collected as part of Building Blocks of Pregnancy Biobank at the Indiana University School of Medicine. The progesterone metabolites, DOC and 16α-OHP, were quantified by mass spectroscopy from the plasma of 58 pregnant women collected in the late first trimester/early second trimester. Steroid levels were combined with patient demographic and obstetric history data in multivariable logistic regression models. The primary outcome was composite neonatal morbidity as measured by the Hassan scale. Secondary outcomes included neonatal length of stay and spontaneous preterm delivery prior to 32 weeks' gestation. The final neonatal morbidity model, which incorporated antenatal corticosteroid exposure and fetal sex, was able to predict high morbidity (Hassan score ≥ 2) with an area under the ROC curve (AUROC) of 0.975 (95% CI 0.932, 1.00), while the model without corticosteroid and fetal sex predictors demonstrated an AUROC of 0.927 (95% CI 0.824, 1.00). The Hassan score was highly correlated with neonatal length of stay (p |
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We conducted a cohort study of pregnant women with plasma samples collected as part of Building Blocks of Pregnancy Biobank at the Indiana University School of Medicine. The progesterone metabolites, DOC and 16α-OHP, were quantified by mass spectroscopy from the plasma of 58 pregnant women collected in the late first trimester/early second trimester. Steroid levels were combined with patient demographic and obstetric history data in multivariable logistic regression models. The primary outcome was composite neonatal morbidity as measured by the Hassan scale. Secondary outcomes included neonatal length of stay and spontaneous preterm delivery prior to 32 weeks' gestation. The final neonatal morbidity model, which incorporated antenatal corticosteroid exposure and fetal sex, was able to predict high morbidity (Hassan score ≥ 2) with an area under the ROC curve (AUROC) of 0.975 (95% CI 0.932, 1.00), while the model without corticosteroid and fetal sex predictors demonstrated an AUROC of 0.927 (95% CI 0.824, 1.00). The Hassan score was highly correlated with neonatal length of stay (p<0.001), allowing the neonatal morbidity model to also predict increased neonatal length of stay (53 [IQR 22, 76] days vs. 4.5 [2, 31] days, above and below the model cut point, respectively; p = 0.0017). Spontaneous preterm delivery prior to 32 weeks' gestation was also predicted with an AUROC of 0.94 (95% CI 0.869, 1.00).
Plasma levels of DOC and 16α-OHP in early gestation can be combined with patient demographic and clinical data to predict significant neonatal morbidity, neonatal length of stay, and risk for very preterm delivery, though validation studies are needed to verify these findings. Early identification of pregnancies at risk for preterm delivery and neonatal morbidity allows for timely implementation of multidisciplinary care to improve perinatal outcomes.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0243585</identifier><identifier>PMID: 33406107</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Amniotic fluid ; Biobanks ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Biomarkers - blood ; Correlation analysis ; Corticosteroids ; Demographics ; Female ; Fetuses ; Gestation ; Gestational age ; Gynecology ; Health aspects ; Health risks ; Humans ; Identification and classification ; Infant mortality ; Infant, Newborn ; Infants ; Laboratories ; Lung diseases ; Mass spectroscopy ; Medicine ; Medicine and Health Sciences ; Metabolites ; Morbidity ; Neonates ; Newborn babies ; Obstetrics ; Patient outcomes ; Phenotype ; Physical Sciences ; Plasma ; Plasma levels ; Pregnancy ; Premature birth ; Premature Birth - diagnosis ; Premature Birth - epidemiology ; Premature labor ; Progesterone ; Regression Analysis ; Regression models ; Risk ; Risk factors ; ROC Curve ; Sex ; Spectroscopy ; Steroids ; Steroids - blood ; Variables ; Young Adult</subject><ispartof>PloS one, 2021-01, Vol.16 (1), p.e0243585-e0243585</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Patil et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Patil et al 2021 Patil et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-6198458be14e957acd61f5002e5dc4f932853276dea830bdc328b78bd14b801a3</citedby><cites>FETCH-LOGICAL-c692t-6198458be14e957acd61f5002e5dc4f932853276dea830bdc328b78bd14b801a3</cites><orcidid>0000-0002-3511-7642 ; 0000-0002-8379-0743</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787372/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787372/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33406107$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Patil, Avinash S</creatorcontrib><creatorcontrib>Grotegut, Chad A</creatorcontrib><creatorcontrib>Gaikwad, Nilesh W</creatorcontrib><creatorcontrib>Dowden, Shelley D</creatorcontrib><creatorcontrib>Haas, David M</creatorcontrib><title>Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Preterm delivery is a common pregnancy complication that can result in significant neonatal morbidity and mortality. Limited tools exist to predict preterm birth, and none to predict neonatal morbidity, from early in pregnancy. The objective of this study was to determine if the progesterone metabolites 11-deoxycorticosterone (DOC) and 16-alpha hydroxyprogesterone (16α-OHP), when combined with patient demographic and obstetric history known during the pregnancy, are predictive of preterm delivery-associated neonatal morbidity, neonatal length of stay, and risk for spontaneous preterm delivery prior to 32 weeks' gestation.
We conducted a cohort study of pregnant women with plasma samples collected as part of Building Blocks of Pregnancy Biobank at the Indiana University School of Medicine. The progesterone metabolites, DOC and 16α-OHP, were quantified by mass spectroscopy from the plasma of 58 pregnant women collected in the late first trimester/early second trimester. Steroid levels were combined with patient demographic and obstetric history data in multivariable logistic regression models. The primary outcome was composite neonatal morbidity as measured by the Hassan scale. Secondary outcomes included neonatal length of stay and spontaneous preterm delivery prior to 32 weeks' gestation. The final neonatal morbidity model, which incorporated antenatal corticosteroid exposure and fetal sex, was able to predict high morbidity (Hassan score ≥ 2) with an area under the ROC curve (AUROC) of 0.975 (95% CI 0.932, 1.00), while the model without corticosteroid and fetal sex predictors demonstrated an AUROC of 0.927 (95% CI 0.824, 1.00). The Hassan score was highly correlated with neonatal length of stay (p<0.001), allowing the neonatal morbidity model to also predict increased neonatal length of stay (53 [IQR 22, 76] days vs. 4.5 [2, 31] days, above and below the model cut point, respectively; p = 0.0017). Spontaneous preterm delivery prior to 32 weeks' gestation was also predicted with an AUROC of 0.94 (95% CI 0.869, 1.00).
Plasma levels of DOC and 16α-OHP in early gestation can be combined with patient demographic and clinical data to predict significant neonatal morbidity, neonatal length of stay, and risk for very preterm delivery, though validation studies are needed to verify these findings. Early identification of pregnancies at risk for preterm delivery and neonatal morbidity allows for timely implementation of multidisciplinary care to improve perinatal outcomes.</description><subject>Adult</subject><subject>Amniotic fluid</subject><subject>Biobanks</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Correlation analysis</subject><subject>Corticosteroids</subject><subject>Demographics</subject><subject>Female</subject><subject>Fetuses</subject><subject>Gestation</subject><subject>Gestational age</subject><subject>Gynecology</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Humans</subject><subject>Identification and classification</subject><subject>Infant mortality</subject><subject>Infant, Newborn</subject><subject>Infants</subject><subject>Laboratories</subject><subject>Lung diseases</subject><subject>Mass spectroscopy</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Metabolites</subject><subject>Morbidity</subject><subject>Neonates</subject><subject>Newborn babies</subject><subject>Obstetrics</subject><subject>Patient outcomes</subject><subject>Phenotype</subject><subject>Physical Sciences</subject><subject>Plasma</subject><subject>Plasma levels</subject><subject>Pregnancy</subject><subject>Premature birth</subject><subject>Premature Birth - diagnosis</subject><subject>Premature Birth - epidemiology</subject><subject>Premature labor</subject><subject>Progesterone</subject><subject>Regression Analysis</subject><subject>Regression models</subject><subject>Risk</subject><subject>Risk factors</subject><subject>ROC Curve</subject><subject>Sex</subject><subject>Spectroscopy</subject><subject>Steroids</subject><subject>Steroids - blood</subject><subject>Variables</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1uL1DAUx4so7rr6DUQDgujDjEmTJumLsCxeBhZWvL2GNDmdydo2Y9Iuzrc3nekuU9kHyUPCye_8zyU5Wfac4CWhgry79kPodLPc-g6WOGe0kMWD7JSUNF_wHNOHR-eT7EmM1xgXVHL-ODuhlGFOsDjN_JcA1pne-Q75GnXgO93rBrU-VM66fod0Z9ENhB3aBughtMhC4_aGIbpujVqdrCkRFNPunUWV860OvyBE5DoEOjQ7tIbY6zHI0-xRrZsIz6b9LPvx8cP3i8-Ly6tPq4vzy4XhZd4vOCklK2QFhEFZCG0sJ3WBcQ6FNaxOdcmC5oJb0JLiyppkqISsLGGVxETTs-zlQXfb-KimXkWVM1FIyrgoErE6ENbra7UNLiW9U147tTf4sFY69M40oICIuoaKQokNYxUtS0lMTZmooKA1wUnr_RRtqFqwBro-6GYmOr_p3Eat_Y0SQgoq8iTwZhII_veQmqVaFw00jU4vMuzz5iRnZTHGevUPen91E7XWqQDX1T7FNaOoOudJjMoy54la3kOlZaF1Jn2s2iX7zOHtzCExPfzp13qIUa2-ff1_9urnnH19xG5AN_0m-mYYv0ycg-wAmuBjDFDfNZlgNc7FbTfUOBdqmovk9uL4ge6cbgeB_gWiuwnJ</recordid><startdate>20210106</startdate><enddate>20210106</enddate><creator>Patil, Avinash S</creator><creator>Grotegut, Chad A</creator><creator>Gaikwad, Nilesh W</creator><creator>Dowden, Shelley D</creator><creator>Haas, David M</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3511-7642</orcidid><orcidid>https://orcid.org/0000-0002-8379-0743</orcidid></search><sort><creationdate>20210106</creationdate><title>Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation</title><author>Patil, Avinash S ; Grotegut, Chad A ; Gaikwad, Nilesh W ; Dowden, Shelley D ; Haas, David M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-6198458be14e957acd61f5002e5dc4f932853276dea830bdc328b78bd14b801a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Amniotic fluid</topic><topic>Biobanks</topic><topic>Biological markers</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Correlation analysis</topic><topic>Corticosteroids</topic><topic>Demographics</topic><topic>Female</topic><topic>Fetuses</topic><topic>Gestation</topic><topic>Gestational age</topic><topic>Gynecology</topic><topic>Health aspects</topic><topic>Health risks</topic><topic>Humans</topic><topic>Identification and classification</topic><topic>Infant mortality</topic><topic>Infant, Newborn</topic><topic>Infants</topic><topic>Laboratories</topic><topic>Lung diseases</topic><topic>Mass spectroscopy</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Metabolites</topic><topic>Morbidity</topic><topic>Neonates</topic><topic>Newborn babies</topic><topic>Obstetrics</topic><topic>Patient outcomes</topic><topic>Phenotype</topic><topic>Physical Sciences</topic><topic>Plasma</topic><topic>Plasma levels</topic><topic>Pregnancy</topic><topic>Premature birth</topic><topic>Premature Birth - diagnosis</topic><topic>Premature Birth - epidemiology</topic><topic>Premature labor</topic><topic>Progesterone</topic><topic>Regression Analysis</topic><topic>Regression models</topic><topic>Risk</topic><topic>Risk factors</topic><topic>ROC Curve</topic><topic>Sex</topic><topic>Spectroscopy</topic><topic>Steroids</topic><topic>Steroids - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Patil, Avinash S</au><au>Grotegut, Chad A</au><au>Gaikwad, Nilesh W</au><au>Dowden, Shelley D</au><au>Haas, David M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2021-01-06</date><risdate>2021</risdate><volume>16</volume><issue>1</issue><spage>e0243585</spage><epage>e0243585</epage><pages>e0243585-e0243585</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Preterm delivery is a common pregnancy complication that can result in significant neonatal morbidity and mortality. Limited tools exist to predict preterm birth, and none to predict neonatal morbidity, from early in pregnancy. The objective of this study was to determine if the progesterone metabolites 11-deoxycorticosterone (DOC) and 16-alpha hydroxyprogesterone (16α-OHP), when combined with patient demographic and obstetric history known during the pregnancy, are predictive of preterm delivery-associated neonatal morbidity, neonatal length of stay, and risk for spontaneous preterm delivery prior to 32 weeks' gestation.
We conducted a cohort study of pregnant women with plasma samples collected as part of Building Blocks of Pregnancy Biobank at the Indiana University School of Medicine. The progesterone metabolites, DOC and 16α-OHP, were quantified by mass spectroscopy from the plasma of 58 pregnant women collected in the late first trimester/early second trimester. Steroid levels were combined with patient demographic and obstetric history data in multivariable logistic regression models. The primary outcome was composite neonatal morbidity as measured by the Hassan scale. Secondary outcomes included neonatal length of stay and spontaneous preterm delivery prior to 32 weeks' gestation. The final neonatal morbidity model, which incorporated antenatal corticosteroid exposure and fetal sex, was able to predict high morbidity (Hassan score ≥ 2) with an area under the ROC curve (AUROC) of 0.975 (95% CI 0.932, 1.00), while the model without corticosteroid and fetal sex predictors demonstrated an AUROC of 0.927 (95% CI 0.824, 1.00). The Hassan score was highly correlated with neonatal length of stay (p<0.001), allowing the neonatal morbidity model to also predict increased neonatal length of stay (53 [IQR 22, 76] days vs. 4.5 [2, 31] days, above and below the model cut point, respectively; p = 0.0017). Spontaneous preterm delivery prior to 32 weeks' gestation was also predicted with an AUROC of 0.94 (95% CI 0.869, 1.00).
Plasma levels of DOC and 16α-OHP in early gestation can be combined with patient demographic and clinical data to predict significant neonatal morbidity, neonatal length of stay, and risk for very preterm delivery, though validation studies are needed to verify these findings. Early identification of pregnancies at risk for preterm delivery and neonatal morbidity allows for timely implementation of multidisciplinary care to improve perinatal outcomes.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33406107</pmid><doi>10.1371/journal.pone.0243585</doi><tpages>e0243585</tpages><orcidid>https://orcid.org/0000-0002-3511-7642</orcidid><orcidid>https://orcid.org/0000-0002-8379-0743</orcidid><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_plos_journals_2475834675 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Adult Amniotic fluid Biobanks Biological markers Biology and Life Sciences Biomarkers Biomarkers - blood Correlation analysis Corticosteroids Demographics Female Fetuses Gestation Gestational age Gynecology Health aspects Health risks Humans Identification and classification Infant mortality Infant, Newborn Infants Laboratories Lung diseases Mass spectroscopy Medicine Medicine and Health Sciences Metabolites Morbidity Neonates Newborn babies Obstetrics Patient outcomes Phenotype Physical Sciences Plasma Plasma levels Pregnancy Premature birth Premature Birth - diagnosis Premature Birth - epidemiology Premature labor Progesterone Regression Analysis Regression models Risk Risk factors ROC Curve Sex Spectroscopy Steroids Steroids - blood Variables Young Adult |
| title | Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T14%3A08%3A06IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20neonatal%20morbidity%20and%20very%20preterm%20delivery%20using%20maternal%20steroid%20biomarkers%20in%20early%20gestation&rft.jtitle=PloS%20one&rft.au=Patil,%20Avinash%20S&rft.date=2021-01-06&rft.volume=16&rft.issue=1&rft.spage=e0243585&rft.epage=e0243585&rft.pages=e0243585-e0243585&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0243585&rft_dat=%3Cgale_plos_%3EA647638926%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2475834675&rft_id=info:pmid/33406107&rft_galeid=A647638926&rft_doaj_id=oai_doaj_org_article_e17ffeb3e90c44b39981cf347be53f10&rfr_iscdi=true |