Abstract
Objective Preterm birth is a leading cause of perinatal morbidity and mortality. Prevention strategies rarely focus on preconception care. We sought to create a preconception nomogram that identifies nonpregnant women at highest risk for preterm birth using the Pregnancy Risk Assessment Monitoring System (PRAMS) surveillance data. Methods We used PRAMS data from 2004 to 2009. The odds ratios (ORs) of preterm birth for each preconception variable was estimated and adjusted analyses were conducted. We created a validated nomogram predicting the probability of preterm birth using multivariate logistic regression coefficients. Results 192,208 cases met inclusion criteria. Demographic/maternal health characteristics and associations with preterm birth and ORs are reported. After validation, we identified the following significant predictors of preterm birth: prior history of preterm birth or low birth weight baby, prior spontaneous or elective abortion, maternal diabetes prior to conception, maternal race (e.g., non-Hispanic black), intention to get pregnant prior to conception (i.e., did not want or wanted it sooner), and smoking prior to conception (p < 0.05). Overall, our preconception preterm risk model correctly classified 76.1 % of preterm cases with a negative predictive value (NPV) of 76.7 %. A nomogram using a 0–100 scale illustrates our final preconception model for predicting preterm birth. Conclusion This preconception nomogram can be used by clinicians in multiple settings as a tool to help predict a woman’s individual preterm birth risk and to triage high-risk non-pregnant women to preconception care. Future studies are needed to validate the nomogram in a clinical setting.
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Acknowledgments
PRAMS Working Group: Alabama—Qun Zheng, M.S. Alaska—Kathy Perham-Hester, M.S., M.P.H. Arkansas— Mary McGehee, Ph.D. Colorado—Alyson Shupe, Ph.D. Connecticut—Jennifer Morin, M.P.H. Delaware— George Yocher, M.S. Florida— Kelsi E. Williams Georgia— Chinelo Ogbuanu, M.D., M.P.H., Ph.D. Hawaii— Jane Awakuni Illinois—Theresa Sandidge, MA Iowa —Sarah Mauch, M.P.H. Louisiana— Amy Zapata, M.P.H. Maine—Tom Patenaude, M.P.H. Maryland—Diana Cheng, M.D. Massachusetts— Emily Lu, M.P.H. Michigan— Patricia McKane Minnesota—Judy Punyko, Ph.D., M.P.H. Mississippi— Brenda Hughes, M.P.P.A. Missouri—Venkata Garikapaty, M.Sc., M.S., Ph.D., M.P.H. Montana—JoAnn Dotson Nebraska—Brenda CoufalI New Hampshire—David J. Laflamme, Ph.D., M.P.H. New Jersey—Ingrid M. Morton, M.S. New Mexico—Eirian Coronado, M.P.H. New York State—Anne Radigan-Garcia New York City—Candace Mulready-Ward, M.P.H. North Carolina— Kathleen Jones-Vessey, M.S. North Dakota—Sandra Anseth Ohio—Connie Geidenberger Ph.D. Oklahoma—Alicia Lincoln, M.S.W., M.S.P.H. Oregon—Kenneth Rosenberg, M.D., M.P.H. Pennsylvania—Tony Norwood Rhode Island—Sam Viner-Brown, Ph.D. South Carolina—Mike Smith, MSPH Texas— Tanya Guthrie, Ph.D. Tennessee—Ramona Lainhart, Ph.D. Utah—Laurie Baksh, M.P.H. Vermont—Peggy Brozicevic Virginia—Christopher Hill, M.P.H., C.P.H. Washington—Linda Lohdefinck West Virginia—Melissa Baker, M.A. Wisconsin—Katherine Kvale, Ph.D. Wyoming—Amy Spieker, M.P.H. CDC PRAMS Team, Applied Sciences Branch, Division of Reproductive Health.
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Mehta-Lee, S.S., Palma, A., Bernstein, P.S. et al. A Preconception Nomogram to Predict Preterm Delivery. Matern Child Health J 21, 118–127 (2017). https://doi.org/10.1007/s10995-016-2100-3
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DOI: https://doi.org/10.1007/s10995-016-2100-3