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

Rebecca J Baer; Monica R McLemore; Nancy Adler; Scott P Oltman; Brittany D Chambers; Miriam Kuppermann; Matthew S Pantell; Elizabeth E Rogers; Kelli K Ryckman; Marina Sirota; Larry Rand; Laura L Jelliffe-Pawlowski

doi:10.1016/j.ejogrb.2018.11.004

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

Eur J Obstet Gynecol Reprod Biol. 2018 Dec:231:235-240. doi: 10.1016/j.ejogrb.2018.11.004. Epub 2018 Nov 5.

Authors

Affiliations

¹ Department of Pediatrics, University of California San Diego, La Jolla, CA, United States; California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, United States. Electronic address: rjbaer@ucsd.edu.
² California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, United States; Department of Family Health Care Nursing, University of California San Francisco School of Nursing, San Francisco, CA, United States.
³ Departments of Psychiatry and Pediatrics, Center for Health and Community, University of California San Francisco School of Medicine, San Francisco, CA, United States.
⁴ California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, United States; Department of Epidemiology & Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, United States.
⁵ California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, United States; Department of Epidemiology & Biostatistics, University of California San Francisco School of Medicine, San Francisco, CA, United States; Department of Obstetrics, Gynecology & Reproductive Sciences, University of California San Francisco School of Medicine, San Francisco, San Francisco, CA, United States.
⁶ Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco, CA, United States.
⁷ California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, United States; Department of Pediatrics, University of California San Francisco School of Medicine, San Francisco, CA, United States.
⁸ Departments of Epidemiology and Pediatrics, University of Iowa College of Public Health and Carver College of Medicine, Iowa City, IA, United States.
⁹ Institute for Computational Health Sciences University of California San Francisco, San Francisco, CA, United States.
¹⁰ California Preterm Birth Initiative, University of California San Francisco, San Francisco, CA, United States; Department of Obstetrics, Gynecology & Reproductive Sciences, University of California San Francisco School of Medicine, San Francisco, San Francisco, CA, United States.

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.

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

MeSH terms

Adolescent
Adult
California
Female
Humans
Infant, Newborn
Maternal Age
Pregnancy
Pregnancy Outcome
Pregnancy Trimester, First*
Pregnancy, High-Risk*
Premature Birth / etiology*
Prenatal Care*
Retrospective Studies
Risk Assessment
Risk Factors
Young Adult

Abstract

MeSH terms

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