Bernat Serra1, Manel Mendoza2, Elena Scazzocchio3, Eva Meler4, Martí Nolla5, Enric Sabrià6, Ignacio Rodríguez7, Elena Carreras2. 1. Department of Obstetrics, Gynecology, and Reproductive Medicine, Dexeus University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain. Electronic address: berser@dexeus.com. 2. Department of Obstetrics and Reproductive Medicine, Maternal-Fetal Medicine Unit, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain; Atenció a la Salut Sexual I Reproductiva (ASSIR) de Barcelona, Institut Català de la Salut, Barcelona, Spain. 3. Department of Obstetrics, Gynecology, and Reproductive Medicine, Dexeus University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain; Atenció a la Salut Sexual I Reproductiva (ASSIR) de Barcelona, Institut Català de la Salut, Barcelona, Spain. 4. Department of Obstetrics, Gynecology, and Reproductive Medicine, Dexeus University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain; Fetal i + D Fetal Medicine Research Center, BCNatal-Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), Institut d'Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain. 5. Epidemiology and Quality Controls, SBP Soft, Girona, Spain. 6. Department of Obstetrics and Gynecology, Hospital-Residència Sant Camil (Consorci Sanitari del Garraf), Sant Pere de Ribes, Barcelona, Spain. 7. Department of Obstetrics, Gynecology, and Reproductive Medicine, Dexeus University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain.
Abstract
BACKGROUND: Early identification of women with an increased risk for preeclampsia is of utmost importance to minimize adverse perinatal events. Models developed until now (mainly multiparametric algorithms) are thought to be overfitted to the derivation population, which may affect their reliability when applied to other populations. Options allowing adaptation to a variety of populations are needed. OBJECTIVE: The objective of the study was to assess the performance of a first-trimester multivariate Gaussian distribution model including maternal characteristics and biophysical/biochemical parameters for screening of early-onset preeclampsia (delivery <34 weeks of gestation) in a routine care low-risk setting. STUDY DESIGN: Early-onset preeclampsia screening was undertaken in a prospective cohort of singleton pregnancies undergoing routine first-trimester screening (8 weeks 0/7 days to 13 weeks 6/7 days of gestation), mainly using a 2-step scheme, at 2 hospitals from March 2014 to September 2017. A multivariate Gaussian distribution model including maternal characteristics (a priori risk), serum pregnancy-associated plasma protein-A and placental growth factor assessed at 8 weeks 0/7 days to 13 weeks 6/7 days and mean arterial pressure and uterine artery pulsatility index measured at 11.0-13.6 weeks was used. RESULTS: A total of 7908 pregnancies underwent examination, of which 6893 were included in the analysis. Incidence of global preeclampsia was 2.3% (n = 161), while of early-onset preeclampsia was 0.2% (n = 17). The combination of maternal characteristics, biophysical parameters, and placental growth factor showed the best detection rate, which was 59% for a 5% false-positive rate and 94% for a 10% false-positive rate (area under the curve, 0.96, 95% confidence interval, 0.94-0.98). The addition of placental growth factor to biophysical markers significantly improved the detection rate from 59% to 94%. CONCLUSION: The multivariate Gaussian distribution model including maternal factors, early placental growth factor determination (at 8 weeks 0/7 days to 13 weeks 6/7 days), and biophysical variables (mean arterial pressure and uterine artery pulsatility index) at 11 weeks 0/7 days to 13 weeks 6/7 days is a feasible tool for early-onset preeclampsia screening in the routine care setting. Performance of this model should be compared with predicting models based on regression analysis.
BACKGROUND: Early identification of women with an increased risk for preeclampsia is of utmost importance to minimize adverse perinatal events. Models developed until now (mainly multiparametric algorithms) are thought to be overfitted to the derivation population, which may affect their reliability when applied to other populations. Options allowing adaptation to a variety of populations are needed. OBJECTIVE: The objective of the study was to assess the performance of a first-trimester multivariate Gaussian distribution model including maternal characteristics and biophysical/biochemical parameters for screening of early-onset preeclampsia (delivery <34 weeks of gestation) in a routine care low-risk setting. STUDY DESIGN: Early-onset preeclampsia screening was undertaken in a prospective cohort of singleton pregnancies undergoing routine first-trimester screening (8 weeks 0/7 days to 13 weeks 6/7 days of gestation), mainly using a 2-step scheme, at 2 hospitals from March 2014 to September 2017. A multivariate Gaussian distribution model including maternal characteristics (a priori risk), serum pregnancy-associated plasma protein-A and placental growth factor assessed at 8 weeks 0/7 days to 13 weeks 6/7 days and mean arterial pressure and uterine artery pulsatility index measured at 11.0-13.6 weeks was used. RESULTS: A total of 7908 pregnancies underwent examination, of which 6893 were included in the analysis. Incidence of global preeclampsia was 2.3% (n = 161), while of early-onset preeclampsia was 0.2% (n = 17). The combination of maternal characteristics, biophysical parameters, and placental growth factor showed the best detection rate, which was 59% for a 5% false-positive rate and 94% for a 10% false-positive rate (area under the curve, 0.96, 95% confidence interval, 0.94-0.98). The addition of placental growth factor to biophysical markers significantly improved the detection rate from 59% to 94%. CONCLUSION: The multivariate Gaussian distribution model including maternal factors, early placental growth factor determination (at 8 weeks 0/7 days to 13 weeks 6/7 days), and biophysical variables (mean arterial pressure and uterine artery pulsatility index) at 11 weeks 0/7 days to 13 weeks 6/7 days is a feasible tool for early-onset preeclampsia screening in the routine care setting. Performance of this model should be compared with predicting models based on regression analysis.
Authors: Samuel Parry; Benjamin A Carper; William A Grobman; Ronald J Wapner; Judith H Chung; David M Haas; Brian Mercer; Robert M Silver; Hyagriv N Simhan; George R Saade; Uma M Reddy; Corette B Parker Journal: Am J Obstet Gynecol Date: 2022-04-26 Impact factor: 10.693
Authors: Shilong Li; Zichen Wang; Luciana A Vieira; Amanda B Zheutlin; Boshu Ru; Emilio Schadt; Pei Wang; Alan B Copperman; Joanne L Stone; Susan J Gross; Yu-Han Kao; Yan Kwan Lau; Siobhan M Dolan; Eric E Schadt; Li Li Journal: NPJ Digit Med Date: 2022-06-06
Authors: M Mendoza; P Garcia-Manau; S Arévalo; M Avilés; B Serrano; M Á Sánchez-Durán; I Garcia-Ruiz; E Bonacina; E Carreras Journal: Ultrasound Obstet Gynecol Date: 2021-01 Impact factor: 7.299
Authors: Berta Serrano; Manel Mendoza; Paula Garcia-Aguilar; Erika Bonacina; Itziar Garcia-Ruiz; Pablo Garcia-Manau; Judit Gil; Mireia Armengol-Alsina; Nuria Fernandez-Hidalgo; Elena Sulleiro; Rosa Maria Lopez-Martinez; Marta Ricart; Lourdes Martin; Eva Lopez-Quesada; Angels Vives; Anna Maroto; Nerea Maiz; Anna Suy; Elena Carreras Journal: Acta Obstet Gynecol Scand Date: 2022-05-03 Impact factor: 4.544