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Preterm birth is a leading cause of perinatal morbidity & mortality. Prevention strategies rarely focus on preconception care. We sought to create a preconception nomogram identifying women at highest risk for preterm birth using the Pregnancy Risk Assessment Monitoring System (PRAMS) surveillance data.
PRAMS data from 2004-2009 was utilized. Odds ratios (OR) of preterm birth for each preconception variable were estimated and adjusted analyses were conducted. A validated nomogram predicting the probability of preterm birth was created using multivariate logistic regression model coefficients.
192,208 cases met inclusion criteria. Demographic/maternal health characteristics and associations with preterm birth and ORs are reported (Table). After validation, significant predictors of preterm birth among all women, were prior history of preterm birth or low birthweight baby, prior SAB/TAB, maternal diabetes prior to conception, maternal race (e.g., NH 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 (Figure).
1Sample characteristics–Estimated preterm birth prevalences by subgroup
This preconception nomogram will give providers a tool to assist in predicting a woman's individual preterm birth risk and to triage high-risk women to preconception care. Future studies are needed to validate the nomogram in the clinical setting.