Clinical prediction models for bronchopulmonary dysplasia: a systematic review and external validation study

Springer Science and Business Media LLC - Tập 13 - Trang 1-20 - 2013
Wes Onland1, Thomas P Debray2, Matthew M Laughon3, Martijn Miedema1, Filip Cools4, Lisa M Askie5, Jeanette M Asselin6, Sandra A Calvert7, Sherry E Courtney8, Carlo Dani9, David J Durand6, Neil Marlow10, Janet L Peacock11, J Jane Pillow12, Roger F Soll13, Ulrich H Thome14, Patrick Truffert15, Michael D Schreiber16, Patrick Van Reempts17, Valentina Vendettuoli18, Giovanni Vento19, Anton H van Kaam1, Karel G Moons2, Martin Offringa1,20
1Department of Neonatology, Emma Children's Hospital, Academic Medical Center, Amsterdam, The Netherlands
2Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
3Department of Pediatrics, University of North Carolina, Chapel Hill, USA.
4Department of Neonatology, Universitair Ziekenhuis Brussel, Brussel, Belgium
5NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
6Division of Neonatology, Children's Hospital and Research Center Oakland, Oakland, USA
7Neonatal Unit—Department of Child Health, St George's Hospital, London, UK
8Department of Neonatology, University of Arkansas for Medical Sciences, Little Rock, USA
9Department of Surgical and Medical Critical Care, University of Florence, Florence, Italy
10UCL Institute of Women’s Health, University College London, London, UK
11Health and Social Care Research, King’s College London, London, UK
12Centre for Neonatal Research and Education, Schools of Anatomy, Physiology and Human Biology and Paediatrics and Child Health, University of Western Australia, Subiaco, Australia
13Department of Pediatrics, University of Vermont College of Medicine, Burlington, USA
14Division of Neonatology, University Hospital for Children and Adolescents, Women's and Children's Hospital, Leipzig, Germany
15Department of Neonatal Medicine, Hospital Jeanne of Flanders, University hospital of Lille, Lille, France
16Department of Pediatrics, University of Chicago Medical Center, Chicago, USA
17University of Antwerp and Antwerp University Hospital, Edegem (Antwerp), Belgium
18NICU, Department of Clinical Sciences and Community Health, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy
19Division of Neonatology–Department of Paediatrics, Policlinico “A. Gemelli”-Università Cattolica S. Cuore, Rome, Italy
20Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, University of Toronto, Toronto, Canada

Tóm tắt

Bronchopulmonary dysplasia (BPD) is a common complication of preterm birth. Very different models using clinical parameters at an early postnatal age to predict BPD have been developed with little extensive quantitative validation. The objective of this study is to review and validate clinical prediction models for BPD. We searched the main electronic databases and abstracts from annual meetings. The STROBE instrument was used to assess the methodological quality. External validation of the retrieved models was performed using an individual patient dataset of 3229 patients at risk for BPD. Receiver operating characteristic curves were used to assess discrimination for each model by calculating the area under the curve (AUC). Calibration was assessed for the best discriminating models by visually comparing predicted and observed BPD probabilities. We identified 26 clinical prediction models for BPD. Although the STROBE instrument judged the quality from moderate to excellent, only four models utilised external validation and none presented calibration of the predictive value. For 19 prediction models with variables matched to our dataset, the AUCs ranged from 0.50 to 0.76 for the outcome BPD. Only two of the five best discriminating models showed good calibration. External validation demonstrates that, except for two promising models, most existing clinical prediction models are poor to moderate predictors for BPD. To improve the predictive accuracy and identify preterm infants for future intervention studies aiming to reduce the risk of BPD, additional variables are required. Subsequently, that model should be externally validated using a proper impact analysis before its clinical implementation.

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The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2431/13/207/prepub

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