Research

A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age, based on data from the Spanish FLIP study

Eric AF Simões¹ , Xavier Carbonell-Estrany² , John R Fullarton³ , Johannes G Liese⁴ , Jose Figueras-Aloy⁵ , Gunther Doering⁶ , Juana Guzman⁷ and European RSV Risk Factor Study Group

¹  Professor of Pediatrics, Department of Pediatrics, Section of Infectious Diseases, The University of Colorado School of Medicine and The Children's Hospital, Denver, Colorado, USA

²  Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona,Spain

³  Analyst, Strategen Limited, Basingstoke, Hampshire, UK

⁴  Dr. von Hauner Children's Hospital, Ludwig-Maximilians-University, Munich, Germany

⁵  Neonatology Service, Hospital Clínic, Institut Clínic de Ginecologia Obstetricia i Neonatologia, Agrupació Sanitaria Hospital Clínic-Hospital SJ Deu, Universitat de Barcelona, Barcelona, Spain

⁶  Munich University of Technology, Department of Pediatrics, Munich, Germany

⁷  Hospital Reina Sofía, Córdoba, Spain

author email corresponding author email

Respiratory Research 2008, 9:78doi:10.1186/1465-9921-9-78

Published:	8 December 2008

Abstract

Background

The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks' gestational age (GA).

Methods

The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.

Results

An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6).

Conclusion

A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe.