OBJECTIVE: To demonstrate that individualized optimal fetal growth norms, accounting for physiologic and pathologic determinants of fetal growth, better identify normal and abnormal outcomes of pregnancy than existing methods. METHODS: In a prospective cohort of 38,033 singleton pregnancies, we identified 9,818 women with a completely normal outcome of pregnancy and characterized the physiologic factors affecting birth weight using multivariable regression. We used those physiologic factors to individually predict optimal growth trajectory and its variation, growth potential, for each fetus in the entire cohort. By comparing actual birth weight with growth potential, population, ultrasound, and customized norms, we calculated for each fetus achieved percentiles, by each norm. We then compared proportions of pregnancies classified as normally grown, between 10th and 90th percentile, or aberrantly grown, outside this interval, by growth potential and traditional norms, in 14,229 complicated pregnancies, 1,518 pregnancies with diabetes or hypertensive disorders, and 1,347 pregnancies with neonatal complications. RESULTS: Nineteen physiologic factors, associated with maternal characteristics and early placental function, were identified. Growth potential norms correctly classified significantly more pregnancies than population, ultrasound, or customized norms in complicated pregnancies (26.4% compared with 18.3%, 18.7%, 22.8%, respectively, all P<.05), pregnancies with diabetes or hypertensive disorders (37.3% compared with 23.0%, 28.0%, 34.0%, respectively, all P<.05) and neonatal complications (33.3% compared with 19.7%, 24.9%, 29.8%, respectively, all P<.05). CONCLUSION: Growth potential norms based on the physiologic determinants of birth weight are a better discriminator of aberrations of fetal growth than traditional norms. LEVEL OF EVIDENCE: II.
OBJECTIVE: To demonstrate that individualized optimal fetal growth norms, accounting for physiologic and pathologic determinants of fetal growth, better identify normal and abnormal outcomes of pregnancy than existing methods. METHODS: In a prospective cohort of 38,033 singleton pregnancies, we identified 9,818 women with a completely normal outcome of pregnancy and characterized the physiologic factors affecting birth weight using multivariable regression. We used those physiologic factors to individually predict optimal growth trajectory and its variation, growth potential, for each fetus in the entire cohort. By comparing actual birth weight with growth potential, population, ultrasound, and customized norms, we calculated for each fetus achieved percentiles, by each norm. We then compared proportions of pregnancies classified as normally grown, between 10th and 90th percentile, or aberrantly grown, outside this interval, by growth potential and traditional norms, in 14,229 complicated pregnancies, 1,518 pregnancies with diabetes or hypertensive disorders, and 1,347 pregnancies with neonatal complications. RESULTS: Nineteen physiologic factors, associated with maternal characteristics and early placental function, were identified. Growth potential norms correctly classified significantly more pregnancies than population, ultrasound, or customized norms in complicated pregnancies (26.4% compared with 18.3%, 18.7%, 22.8%, respectively, all P<.05), pregnancies with diabetes or hypertensive disorders (37.3% compared with 23.0%, 28.0%, 34.0%, respectively, all P<.05) and neonatal complications (33.3% compared with 19.7%, 24.9%, 29.8%, respectively, all P<.05). CONCLUSION: Growth potential norms based on the physiologic determinants of birth weight are a better discriminator of aberrations of fetal growth than traditional norms. LEVEL OF EVIDENCE: II.
Authors: Maged M Costantine; Lisa Mele; Mark B Landon; Catherine Y Spong; Susan M Ramin; Brian Casey; Ronald J Wapner; Michael W Varner; Dwight J Rouse; John M Thorp; Anthony Sciscione; Patrick Catalano; Steve N Caritis; Yoram Sorokin; Alan M Peaceman; Jorge E Tolosa; Garland D Anderson Journal: Am J Perinatol Date: 2012-11-12 Impact factor: 1.862
Authors: Alexa A Freedman; Robert M Silver; Karen J Gibbins; Carol J Hogue; Robert L Goldenberg; Donald J Dudley; Halit Pinar; Carolyn Drews-Botsch Journal: Paediatr Perinat Epidemiol Date: 2019-07 Impact factor: 3.980
Authors: Russell L Deter; Wesley Lee; Haleh Sangi-Haghpeykar; Adi L Tarca; Lami Yeo; Roberto Romero Journal: J Matern Fetal Neonatal Med Date: 2014-07-11
Authors: Alexa A Freedman; Carol J Hogue; Carmen J Marsit; Augustine Rajakumar; Alicia K Smith; Robert L Goldenberg; Donald J Dudley; George R Saade; Robert M Silver; Karen J Gibbins; Barbara J Stoll; Radek Bukowski; Carolyn Drews-Botsch Journal: Pediatr Dev Pathol Date: 2018-07-16
Authors: Radek Bukowski; Rowan T Chlebowski; Inger Thune; Anne-Sofie Furberg; Gary D V Hankins; Fergal D Malone; Mary E D'Alton Journal: PLoS One Date: 2012-07-17 Impact factor: 3.240