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Development and validation of a spontaneous preterm delivery predictor in asymptomatic women
Preterm delivery remains the leading cause of perinatal mortality. Risk factors and biomarkers have traditionally failed to identify the majority of preterm deliveries.
To develop and validate a mass spectrometry–based serum test to predict spontaneous preterm delivery in asymptomatic pregnant women.
A total of 5501 pregnant women were enrolled between 170/7 and 286/7 weeks gestational age in the prospective Proteomic Assessment of Preterm Risk study at 11 sites in the United States between 2011 and 2013. Maternal blood was collected at enrollment and outcomes collected following delivery. Maternal serum was processed by a proteomic workflow, and proteins were quantified by multiple reaction monitoring mass spectrometry. The discovery and verification process identified 2 serum proteins, insulin-like growth factor–binding protein 4 (IBP4) and sex hormone–binding globulin (SHBG), as predictors of spontaneous preterm delivery. We evaluated a predictor using the log ratio of the measures of IBP4 and SHBG (IBP4/SHBG) in a clinical validation study to classify spontaneous preterm delivery cases (<370/7 weeks gestational age) in a nested case-control cohort different from subjects used in discovery and verification. Strict blinding and independent statistical analyses were employed.
The predictor had an area under the receiver operating characteristic curve value of 0.75 and sensitivity and specificity of 0.75 and 0.74, respectively. The IBP4/SHBG predictor at this sensitivity and specificity had an odds ratio of 5.04 for spontaneous preterm delivery. Accuracy of the IBP4/SHBG predictor increased using earlier case-vs-control gestational age cutoffs (eg, <350/7 vs ≥350/7 weeks gestational age). Importantly, higher-risk subjects defined by the IBP4/SHBG predictor score generally gave birth earlier than lower-risk subjects.
A serum-based molecular predictor identifies asymptomatic pregnant women at risk of spontaneous preterm delivery, which may provide utility in identifying women at risk at an early stage of pregnancy to allow for clinical intervention. This early detection would guide enhanced levels of care and accelerate development of clinical strategies to prevent preterm delivery.
Key words: biomarker, pregnancy, preterm birth, proteomics, IGFBP4, IBP4, SHBG.
a Department of Obstetrics & Gynecology, The University of Texas Medical Branch, Galveston, Texas
b Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of North Carolina, Chapel Hill, North Carolina
c Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina
d Maternal Fetal Medicine, Baystate Medical Center, Springfield, Massachusetts
e Department of Obstetrics & Gynecology, The Ohio State University, Columbus, Ohio
f Department of Obstetrics and Gynecology, Maricopa Integrated Health System/District Medical Group, Phoenix, Arizona
g Division of Maternal-Fetal Medicine, Oregon Health & Science University, Portland, Oregon
h Division of Maternal Fetal Medicine, Intermountain Healthcare, Murray, Utah
i San Diego Perinatal Center, Maternal Fetal Medicine Division, Rady Children’s Specialists of San Diego, San Diego, California
j Regional Obstetrical Consultants, Chattanooga, Tennessee
k Department of Obstetrics & Gynecology, Christiana Care Health System, Newark, Delaware
l Sera Prognostics, Inc, Salt Lake City, Utah
m Dana Farber Cancer Institute, Department of Biostatistics and Computational Biology, Boston, Massachusetts
n Integrated Diagnostics, Inc, Seattle, Washington
∗ Corresponding author: George R. Saade, MD.
Dr Markenson is currently affiliated with Maternal Fetal Medicine, Boston Medical Center, Boston, Massachusetts. Dr Coonrod is currently affiliated with the Department of Obstetrics and Gynecology, University of Arizona College of Medicine, Tucson, Arizona. Dr Esplin is currently affiliated with the University of Utah, Maternal Fetal Medicine, Salt Lake City, Utah. Dr Lam is currently affiliated with the Department of Obstetrics and Gynecology, UT College of Medicine Chattanooga, Chattanooga, Tennessee.
The following authors of this manuscript disclose that their institutions received money from Sera Prognostics to cover the costs of the study: G.R.S., K.A.B., S.A.S., G.R.M., J.D.I., D.V.C., L.M.P., M.S.E., L.M.C., G.K.L., M.K.H. The remaining authors (R.D.S., T.P., J.S.F., A.C.F., A.J.L., S.R.R., C.T.H., M.T.D., C.L.B., M.S.E., I.E.I., T.C.F., A.D.P., G.C.C., J.J.B., D.E.H., E.M., P.E.K.) are either employees or consultants for the study sponsor, Sera Prognostics.
The study described in this manuscript was supported in full by Sera Prognostics, Inc. The sponsor collaborated with site principal investigators and outside consultants regarding study design, data analysis and interpretation, manuscript writing, and the decision to submit the paper for publication. The sponsor was not involved with specimen collection, specimen processing, or data collection at the clinical sites. The sponsor conducted proteomic analysis of the specimens.
Cite this article as: 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;214:633.e1-24.
© 2016 Published by Elsevier B.V.