Cross-validated prediction model for severe adverse neonatal outcomes in a term, non-anomalous, singleton cohort. Issue 1 (15th March 2019)

• Record Type: Journal Article
• Authors: Flatley, Christopher; Gibbons, Kristen; Hurst, Cameron; Flenady, Vicki; Kumar, Sailesh
• Is Part Of: BMJ paediatrics open. Volume 3:Issue 1(2019)
• Publication Date: 2019-03-15
• Subjects: fetal medicine -- imaging -- statistics -- epidemiology; Pediatrics -- Periodicals; Children -- Health and hygiene -- Periodicals; 618.920005
• DOI: 10.1136/bmjpo-2018-000424
• Languages: English
• ISSNs: 2399-9772

Abstract

Abstract : Objective: The aim of this study was to develop a predictive model using maternal, intrapartum and ultrasound variables for a composite of severe adverse neonatal outcomes (SANO) in term infants. Design: Prospectively collected observational study. Mixed effects generalised linear models were used for modelling. Internal validation was performed using the K-fold cross-validation technique. Setting: This was a study of women that birthed at the Mater Mother's Hospital in Brisbane, Australia between January 2010 and April 2017. Patients: We included all term, non-anomalous singleton pregnancies that had an ultrasound performed between 36 and 38 weeks gestation and had recordings for the umbilical artery pulsatility index, middle cerebral artery pulsatility index and the estimated fetal weight (EFW). Main outcome measures: The components of the SANO were: severe acidosis arterial, admission to the neonatal intensive care unit, Apgar score of ≤3 at 5 min or perinatal death. Results: There were 5439 women identified during the study period that met the inclusion criteria, with 11.7% of this cohort having SANO. The final generalised linear mixed model consisted of the following variables: maternal ethnicity, socioeconomic score, nulliparity, induction of labour, method of birth and z-scores for EFW and cerebroplacental ratio. The final model had an area under the receiver operating characteristic curve of 0.71. Conclusions: The results of this study demonstrate it is possible to predict infants that are at risk of SANO at term with moderate accuracy using a combination of maternal, intrapartum and ultrasound variables. Cross-validation analysis suggests a high calibration of the model.