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Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation

BACKGROUND: 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 progeste...

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Autores principales: Patil, Avinash S., Grotegut, Chad A., Gaikwad, Nilesh W., Dowden, Shelley D., Haas, David M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787372/
https://www.ncbi.nlm.nih.gov/pubmed/33406107
http://dx.doi.org/10.1371/journal.pone.0243585
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author Patil, Avinash S.
Grotegut, Chad A.
Gaikwad, Nilesh W.
Dowden, Shelley D.
Haas, David M.
author_facet Patil, Avinash S.
Grotegut, Chad A.
Gaikwad, Nilesh W.
Dowden, Shelley D.
Haas, David M.
author_sort Patil, Avinash S.
collection PubMed
description BACKGROUND: 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. METHODS AND FINDINGS: 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). CONCLUSIONS: 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.
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spelling pubmed-77873722021-01-13 Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation Patil, Avinash S. Grotegut, Chad A. Gaikwad, Nilesh W. Dowden, Shelley D. Haas, David M. PLoS One Research Article BACKGROUND: 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. METHODS AND FINDINGS: 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). CONCLUSIONS: 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. Public Library of Science 2021-01-06 /pmc/articles/PMC7787372/ /pubmed/33406107 http://dx.doi.org/10.1371/journal.pone.0243585 Text en © 2021 Patil et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Patil, Avinash S.
Grotegut, Chad A.
Gaikwad, Nilesh W.
Dowden, Shelley D.
Haas, David M.
Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
title Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
title_full Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
title_fullStr Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
title_full_unstemmed Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
title_short Prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
title_sort prediction of neonatal morbidity and very preterm delivery using maternal steroid biomarkers in early gestation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787372/
https://www.ncbi.nlm.nih.gov/pubmed/33406107
http://dx.doi.org/10.1371/journal.pone.0243585
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