G171 Survival of Preterm Infants Admitted to Neonatal Care in England: A Population-Based Study Using NHS Electronic Clinical Records. (4th June 2013)

• Record Type: Journal Article
• Authors: Santhakumaran, S; Watson, S; Statnikov, Y; Ashby, D; Modi, N
• Is Part Of: Archives of disease in childhood. Volume 98:Supplement 1(2013)
• Page Start: A78
• Page End: A79
• Publication Date: 2013-06-04
• Subjects: Children -- Diseases -- Periodicals; Infants -- Diseases -- Periodicals; 618.920005
• DOI: 10.1136/archdischild-2013-304107.183
• Languages: English
• ISSNs: 0003-9888

Abstract

Abstract : Aims: The survival of preterm infants is a matter of wide public interest. Survival prediction is important for clinicians when advising parents and for risk adjustment when comparing providers. Prediction models are generally based on historical data, often from hospital rather than population-based cohorts. Here we demonstrate the use of near-contemporaneous electronic National Health Service (NHS) clinical data to provide a practical, up-to-date, web-based survival prediction tool for preterm infants admitted to neonatal units in England. We compared this with existing UK and US predictors and evaluated the change in survival over recent years for extremely preterm babies from 22 +0 –25 +6 week gestational age (GA) admitted to neonatal care, in comparison with previous published data. Methods: Data for infants born ≤31+6 weeks GA that died or were discharged in 2009–2011 were received with Caldicott Guardian permission from English neonatal units in the UK Neonatal Collaborative. A multivariable logistic regression model was developed using known predictors. Discrimination and calibration were evaluated internally and on independent data. A web-based tool was written in Javascript. Survival was compared against data from the EPICure 2 study in 2006. Results: There were 17, 491 infants included in the cohort, of whom 16, 164 (92%) survived. Birth weight, GA, sex, antenatal steroids, and multiple birth were factors included in the final model. The interactive tool is available online for open access. The model showed good discrimination internally (area under ROC curve (AUC) = 0.89, 95% CI 0.88 to 0.90) and on independent data (AUC = 0.87, 95% CI 0.82 to 0.91). Predictive performance was similar to previous UK models and improved over a US model. There has been no statistically significant increase in survival to discharge of admitted infants born at 22+0–25+6 weeks gestation in England since 2006 (Relative Risk 1.10, 95% CI 0.98 to 1.22, p = 0.11). Conclusions: We have established the feasibility of employing contemporaneous, population-based, routinely collected, electronic NHS data for survival modelling for preterm babies admitted to neonatal care. The model is available in a web tool readily accessible to clinicians, parents, and healthcare managers and can be regularly updated to readily assess population changes in neonatal survival.