Literature DB >> 30439652

Pre-pregnancy or first-trimester risk scoring to identify women at high risk of preterm birth.

Rebecca J Baer1, Monica R McLemore2, Nancy Adler3, Scott P Oltman4, Brittany D Chambers4, Miriam Kuppermann5, Matthew S Pantell6, Elizabeth E Rogers7, Kelli K Ryckman8, Marina Sirota9, Larry Rand10, Laura L Jelliffe-Pawlowski4.   

Abstract

Objective To develop a pre-pregnancy or first-trimester risk score to identify women at high risk of preterm birth. Study design In this retrospective cohort analysis, the sample was drawn from California singleton livebirths from 2007 to 2012 with linked birth certificate and hospital discharge records. The dataset was divided into a training (2/3 of sample) and a testing (1/3 of sample) set for discovery and validation. Predictive models for preterm birth using pre-pregnancy or first-trimester maternal factors were developed using backward stepwise logistic regression on a training dataset. A risk score for preterm birth was created for each pregnancy using beta-coefficients for each maternal factor remaining in the final multivariable model. Risk score utility was replicated in a testing dataset and by race/ethnicity and payer for prenatal care. Results The sample included 2,339,696 pregnancies divided into training and testing datasets. Twenty-three maternal risk factors were identified including several that were associated with a two or more increased odds of preterm birth (preexisting diabetes, preexisting hypertension, sickle cell anemia, and previous preterm birth). Approximately 40% of women with a risk score ≥ 3.0 in the training and testing samples delivered preterm (40.6% and 40.8%, respectively) compared to 3.1-3.3% of women with a risk score of 0.0 [odds ratio (OR) 13.0, 95% confidence interval (CI) 10.7-15.8, training; OR 12.2, 95% CI 9.4-15.9, testing). Additionally, over 18% of women with a risk score ≥ 3.0 had an adverse outcome other than preterm birth. Conclusion Maternal factors that are identifiable prior to pregnancy or during the first-trimester can be used create a cumulative risk score to identify women at the lowest and highest risk for preterm birth regardless of race/ethnicity or socioeconomic status. Further, we found that this cumulative risk score could also identify women at risk for other adverse outcomes who did not have a preterm birth. The risk score is not an effective screening test, but does identify women at very high risk of a preterm birth.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Beta-coefficient; Cumulative risk; First-trimester; Preterm birth; Risk score

Mesh:

Year:  2018        PMID: 30439652      PMCID: PMC6697157          DOI: 10.1016/j.ejogrb.2018.11.004

Source DB:  PubMed          Journal:  Eur J Obstet Gynecol Reprod Biol        ISSN: 0301-2115            Impact factor:   2.435


  30 in total

1.  Predictive score for early preterm birth in decisions about emergency cervical cerclage in singleton pregnancies.

Authors:  Florent Fuchs; Marie-Victoire Senat; Hervé Fernandez; Amélie Gervaise; René Frydman; Jean Bouyer
Journal:  Acta Obstet Gynecol Scand       Date:  2012-05-01       Impact factor: 3.636

2.  Proteomic identification of serum peptides predicting subsequent spontaneous preterm birth.

Authors:  M Sean Esplin; Karen Merrell; Robert Goldenberg; Yinglei Lai; Jay D Iams; Brian Mercer; Catherine Y Spong; Menachem Miodovnik; Hygriv N Simhan; Peter van Dorsten; Mitchell Dombrowski
Journal:  Am J Obstet Gynecol       Date:  2010-11-11       Impact factor: 8.661

3.  Generating genetic risk scores from intermediate phenotypes for use in association studies of clinically significant endpoints.

Authors:  B D Horne; J L Anderson; J F Carlquist; J B Muhlestein; D G Renlund; T L Bair; R R Pearson; N J Camp
Journal:  Ann Hum Genet       Date:  2005-03       Impact factor: 1.670

4.  Development of a prognostic model for predicting spontaneous singleton preterm birth.

Authors:  Jelle M Schaaf; Anita C J Ravelli; Ben Willem J Mol; Ameen Abu-Hanna
Journal:  Eur J Obstet Gynecol Reprod Biol       Date:  2012-07-21       Impact factor: 2.435

5.  Progesterone supplementation and the prevention of preterm birth.

Authors:  Errol R Norwitz; Aaron B Caughey
Journal:  Rev Obstet Gynecol       Date:  2011

6.  National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications.

Authors:  Hannah Blencowe; Simon Cousens; Mikkel Z Oestergaard; Doris Chou; Ann-Beth Moller; Rajesh Narwal; Alma Adler; Claudia Vera Garcia; Sarah Rohde; Lale Say; Joy E Lawn
Journal:  Lancet       Date:  2012-06-09       Impact factor: 79.321

7.  The Preterm Prediction Study: toward a multiple-marker test for spontaneous preterm birth.

Authors:  R L Goldenberg; J D Iams; B M Mercer; P J Meis; A Moawad; A Das; M Miodovnik; P J Vandorsten; S N Caritis; G Thurnau; M P Dombrowski
Journal:  Am J Obstet Gynecol       Date:  2001-09       Impact factor: 8.661

8.  Prediction of spontaneous preterm delivery from maternal factors, obstetric history and placental perfusion and function at 11-13 weeks.

Authors:  Jarek Beta; Ranjit Akolekar; Walter Ventura; Argyro Syngelaki; Kypros H Nicolaides
Journal:  Prenat Diagn       Date:  2011-01       Impact factor: 3.050

9.  First-trimester prediction of preterm birth using ADAM12, PAPP-A, uterine artery Doppler, and maternal characteristics.

Authors:  Katherine R Goetzinger; Alison G Cahill; Janet Kemna; Linda Odibo; George A Macones; Anthony O Odibo
Journal:  Prenat Diagn       Date:  2012-07-31       Impact factor: 3.050

10.  First trimester serum analytes, maternal characteristics and ultrasound markers to predict pregnancies at risk for preterm birth.

Authors:  M J Stout; K R Goetzinger; M G Tuuli; A G Cahill; G A Macones; A O Odibo
Journal:  Placenta       Date:  2012-11-28       Impact factor: 3.481

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  8 in total

1.  Patient and provider perspectives on preterm birth risk assessment and communication.

Authors:  Martha A Tesfalul; Sky K Feuer; Esperanza Castillo; Kimberly Coleman-Phox; Allison O'Leary; Miriam Kuppermann
Journal:  Patient Educ Couns       Date:  2021-04-01

2.  Risk and Protective Factors for Preterm Birth Among Black Women in Oakland, California.

Authors:  Monica R McLemore; Rachel L Berkowitz; Scott P Oltman; Rebecca J Baer; Linda Franck; Jonathan Fuchs; Deborah A Karasek; Miriam Kuppermann; Safyer McKenzie-Sampson; Daphina Melbourne; Briane Taylor; Shanell Williams; Larry Rand; Brittany D Chambers; Karen Scott; Laura L Jelliffe-Pawlowski
Journal:  J Racial Ethn Health Disparities       Date:  2020-10-09

3.  Cervicovaginal fluid cytokines as predictive markers of preterm birth in symptomatic women.

Authors:  Sunwha Park; Young-Ah You; Hayoung Yun; Suk-Joo Choi; Han-Sung Hwang; Sae-Kyung Choi; Seung Mi Lee; Young Ju Kim
Journal:  Obstet Gynecol Sci       Date:  2020-06-19

4.  Maternal dyslipidemia and risk for preterm birth.

Authors:  Caitlin J Smith; Rebecca J Baer; Scott P Oltman; Patrick J Breheny; Wei Bao; Jennifer G Robinson; John M Dagle; Liang Liang; Sky K Feuer; Christina D Chambers; Laura L Jelliffe-Pawlowski; Kelli K Ryckman
Journal:  PLoS One       Date:  2018-12-21       Impact factor: 3.240

5.  PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks.

Authors:  Rawan AlSaad; Qutaibah Malluhi; Sabri Boughorbel
Journal:  BioData Min       Date:  2022-02-14       Impact factor: 2.522

6.  Association of Alcohol Use Diagnostic Codes in Pregnancy and Offspring Conotruncal and Endocardial Cushion Heart Defects.

Authors:  Drayton C Harvey; Rebecca J Baer; Gretchen Bandoli; Christina D Chambers; Laura L Jelliffe-Pawlowski; S Ram Kumar
Journal:  J Am Heart Assoc       Date:  2022-01-11       Impact factor: 6.106

7.  Dense phenotyping from electronic health records enables machine learning-based prediction of preterm birth.

Authors:  Abin Abraham; Brian Le; Idit Kosti; Peter Straub; Digna R Velez-Edwards; Lea K Davis; J M Newton; Louis J Muglia; Antonis Rokas; Cosmin A Bejan; Marina Sirota; John A Capra
Journal:  BMC Med       Date:  2022-09-28       Impact factor: 11.150

8.  Association between maternal prepregnancy body mass index and risk of preterm birth in more than 1 million Asian American mothers.

Authors:  Rui Gao; Buyun Liu; Wenhan Yang; Yuxiao Wu; Linda G Snetselaar; Mark K Santillan; Wei Bao
Journal:  J Diabetes       Date:  2020-10-19       Impact factor: 4.006

  8 in total

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