Download PDF
CC BY-NC-ND 4.0 · Am J Perinatol 2023; 40(10): 1071-1080
DOI: 10.1055/s-0041-1732339
Original Article

Prediction and Prevention of Preterm Birth: A Prospective, Randomized Intervention Trial

D. Ware Branch
1   Department of Obstetrics and Gynecology, Intermountain Healthcare Maternal-Fetal Medicine and University of Utah Health, Murray, Utah
,
John M. VanBuren
2   Division of Pediatric Critical Care, Department of Pediatrics, University of Utah Health, Murray, Utah
,
T. Flint Porter
1   Department of Obstetrics and Gynecology, Intermountain Healthcare Maternal-Fetal Medicine and University of Utah Health, Murray, Utah
,
Calla Holmgren
1   Department of Obstetrics and Gynecology, Intermountain Healthcare Maternal-Fetal Medicine and University of Utah Health, Murray, Utah
,
Richard Holubkov
2   Division of Pediatric Critical Care, Department of Pediatrics, University of Utah Health, Murray, Utah
,
Kent Page
2   Division of Pediatric Critical Care, Department of Pediatrics, University of Utah Health, Murray, Utah
,
Julja Burchard
3   Sera Prognostics, Inc., Department of Research & Development, Salt Lake City, Utah
,
Garrett K. Lam
4   High Risk Pregnancy Center, Las Vegas, Nevada
,
M. Sean Esplin
1   Department of Obstetrics and Gynecology, Intermountain Healthcare Maternal-Fetal Medicine and University of Utah Health, Murray, Utah
› Author Affiliations Funding This study was funded by Sera Prognostics, Inc., Salt Lake City, UT.
› Further Information
Also available at
   Permissions and Reprints

Abstract

Objective The study aimed to determine if a program of mid-trimester serum proteomics screening of women at low risk for spontaneous preterm birth (sPTB) and the use of a PTB risk-reduction protocol in those whose results indicated an increased risk of sPTB would reduce the likelihood of sPTB and its sequelae.

Study Design Prospective comparison of birth outcomes in singleton pregnancies with mid-trimester cervical length ≥2.5 cm and at otherwise low risk for sPTB randomized to undergo or not undergo mid-trimester serum proteomics screening for increased risk of sPTB (NCT 03530332). Screen-positive women were offered a group of interventions aimed at reducing the risk of spontaneous PTB. The primary outcome was the rate of sPTB <37 weeks, and secondary outcomes were gestational age at delivery, total length of neonatal stay, and NICU length of stay (LOS). Unscreened and screen-negative women received standard care. The adaptive study design targeted a sample size of 3,000 to 10,000 women to detect a reduction in sPTB from 6.4 to 4.7%. Due to limited resources, the trial was stopped early prior to data unblinding.

Results A total of 1,191 women were randomized. Screened and unscreened women were demographically similar. sPTB <37 weeks occurred in 2.7% of screened women and 3.5% of controls (p = 0.41). In the screened compared with the unscreened group, there were no between-group differences in the gestational age at delivery, total length of neonatal stay, and NICU LOS. However, the NICU LOS among infants admitted for sPTB was significantly shorter (median = 6.8 days, interquartile range [IQR]: 1.8–8.0 vs. 45.5 days, IQR: 34.6–79.0; p = 0.005).

Conclusion Mid-trimester serum proteomics screening of women at low risk for sPTB and the use of a sPTB risk-reduction protocol in screen-positive patients did not significantly reduce the rate of sPTB compared with women not screened, though the trial was underpowered thus limiting the interpretation of negative findings. Infants in the screened group had a significantly shorter NICU LOS, a difference likely due to a reduced number of infants in the screened group that delivered <35 weeks.

Key Points

  • Mid-trimester serum proteomics screening of women at low risk for sPTB and the use of a sPTB risk-reduction protocol in screen-positive patients did not significantly reduce the rate of sPTB, though the trial was underpowered.

  • NICU LOS following sPTB was significantly shortened among women who underwent screening and risk-reduction management.

  • The use of serum biomarkers may contribute to a practical strategy to reduce sPTB sequelae.

Keywords

spontaneous preterm birth - serum proteomics screening - neonatal length of stay

Supplementary Material



Publication History

Received: 22 December 2020

Accepted: 17 June 2021

Article published online:
16 August 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA

 

  • References

  • 1 Prediction and prevention of preterm birth. Practice bulletin no. 130. American College of Obstetricians and Gynecologists. Obstet Gynecol 2012; 120: 964-973
    CrossrefPubMedGoogle Scholar
  • 2 Manuck TA, Rice MM, Bailit JL. et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Preterm neonatal morbidity and mortality by gestational age: a contemporary cohort. Am J Obstet Gynecol 2016; 215 (01) 103.e1-103.e14
    CrossrefPubMedGoogle Scholar
  • 3 Boyle EM, Poulsen G, Field DJ. et al. Effects of gestational age at birth on health outcomes at 3 and 5 years of age: population based cohort study. BMJ 2012; 344: e896-e896
    CrossrefPubMedGoogle Scholar
  • 4 Iams JD. Clinical practice. Prevention of preterm parturition. N Engl J Med 2014; 370 (03) 254-261
    CrossrefPubMedGoogle Scholar
  • 5 Birth P. Causes, consequences, and prevention. In: Behrman RE, Bulter AS. eds. Institute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes. Washington, DC: National Academies Press; 2007
    Google Scholar
  • 6 Ferrero DM, Larson J, Jacobsson B. et al. Cross-country individual participant analysis of 4.1 million singleton births in 5 countries with very high human development index confirms known associations but provides no biologic explanation for 2/3 of all preterm births. PLoS One 2016; 11 (09) e0162506
    CrossrefPubMedGoogle Scholar
  • 7 Salihu H, Mbah AK, Alio AP. et al. Nulliparity and preterm birth in the era of obesity epidemic. J Matern Fetal Neonatal Med 2010; 23 (12) 1444-1450
    CrossrefPubMedGoogle Scholar
  • 8 Esplin MS, Elovitz MA, Iams JD. et al; nuMoM2b Network. Predictive accuracy of serial transvaginal cervical lengths and quantitative vaginal fetal fibronectin levels for spontaneous preterm birth among nulliparous women. JAMA 2017; 317 (10) 1047-1056
    CrossrefPubMedGoogle Scholar
  • 9 Goldenberg RL, Culhane JF, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet 2008; 371 (9606): 75-84
    CrossrefPubMedGoogle Scholar
  • 10 Smith GCS, Shah I, White IR, Pell JP, Crossley JA, Dobbie R. Maternal and biochemical predictors of spontaneous preterm birth among nulliparous women: a systematic analysis in relation to the degree of prematurity. Int J Epidemiol 2006; 35 (05) 1169-1177
    CrossrefPubMedGoogle Scholar
  • 11 Dekker GA, Lee SY, North RA, McCowan LM, Simpson NAB, Roberts CT. Risk factors for preterm birth in an international prospective cohort of nulliparous women. PLoS One 2012; 7 (07) e39154
    CrossrefPubMedGoogle Scholar
  • 12 Bastek JA, Elovitz MA. The role and challenges of biomarkers in spontaneous preterm birth and preeclampsia. Fertil Steril 2013; 99 (04) 1117-1123
    CrossrefPubMedGoogle Scholar
  • 13 Mercer BM, Goldenberg RL, Das A. et al. The preterm prediction study: a clinical risk assessment system. Am J Obstet Gynecol 1996; 174 (06) 1885-1893 , discussion 1893–1895
    CrossrefPubMedGoogle Scholar
  • 14 Schaaf JM, Ravelli ACJ, Mol BWJ, Abu-Hanna A. Development of a prognostic model for predicting spontaneous singleton preterm birth. Eur J Obstet Gynecol Reprod Biol 2012; 164 (02) 150-155
    CrossrefPubMedGoogle Scholar
  • 15 Saade GR, Boggess KA, Sullivan SA. et al. Development and validation of a spontaneous preterm delivery predictor in asymptomatic women. Am J Obstet Gynecol 2016; 214 (05) 633.e1-633.e24
    CrossrefPubMedGoogle Scholar
  • 16 Hassan SS, Romero R, Vidyadhari D. et al; PREGNANT Trial. Vaginal progesterone reduces the rate of preterm birth in women with a sonographic short cervix: a multicenter, randomized, double-blind, placebo-controlled trial. Ultrasound Obstet Gynecol 2011; 38 (01) 18-31
    CrossrefPubMedGoogle Scholar
  • 17 Markenson GR, Saade GR, Laurent LC. et al. Performance of a proteomic preterm delivery predictor in a large independent prospective cohort. Am J Obstet Gynecol MFM 2020; 2 (03) 100140
    CrossrefPubMedGoogle Scholar
  • 18 American College of Obstetricians and Gynecologists. Committee opinion no 700: Methods for estimating the due date. Obstet Gynecol 2017; 129 (05) e150-e154
    Google Scholar
  • 19 Broglio KR, Connor JT, Berry SM. Not too big, not too small: a goldilocks approach to sample size selection. J Biopharm Stat 2014; 24 (03) 685-705
    CrossrefPubMedGoogle Scholar
  • 20 Fay MP, Proschan MA, Brittain E. Combining one-sample confidence procedures for inference in the two-sample case. Biometrics 2015; 71 (01) 146-156
    CrossrefPubMedGoogle Scholar
  • 21 R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Accessed 2013 at: http://www.R-project.org/
    PubMedGoogle Scholar
  • 22 Chan ISF, Zhang Z. Test-based exact confidence intervals for the difference of two binomial proportions. Biometrics 1999; 55 (04) 1202-1209
    CrossrefPubMedGoogle Scholar
  • 23 Carroll AE. The high costs of unnecessary care. JAMA 2017; 318 (18) 1748-1749
    CrossrefPubMedGoogle Scholar