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

BMC Pediatrics

Martin Offringa^1,10  Karel G Moons^1,15  Anton H van Kaam⁴  Giovanni Vento⁶  Valentina Vendettuoli^1,11  Patrick Van Reempts^1,16  Michael D Schreiber²  Patrick Truffert^1,18  Ulrich H Thome⁵  Roger F Soll^1,12  J Jane Pillow⁸  Janet L Peacock^2,20  Neil Marlow^1,17  David J Durand⁷  Carlo Dani¹  Sherry E Courtney³  Sandra A Calvert⁹  Jeanette M Asselin⁷  Lisa M Askie^1,14  Filip Cools^1,19  Martijn Miedema⁴  Matthew M Laughon^1,13  Thomas P Debray^1,15  Wes Onland⁴ 

[1]Department of Surgical and Medical Critical Care, University of Florence, Florence, Italy

[2]Department of Pediatrics, University of Chicago Medical Center, Chicago, IL, USA

[3]Department of Neonatology, University of Arkansas for Medical Sciences, Little Rock, AR, USA

[4]Department of Neonatology, Emma Children’s Hospital, Academic Medical Center, Amsterdam, the Netherlands

[5]Division of Neonatology, University Hospital for Children and Adolescents, Women's and Children's Hospital, Leipzig, Germany

[6]Division of Neonatology–Department of Paediatrics, Policlinico “A. Gemelli”-Università Cattolica S. Cuore, Rome, Italy

[7]Division of Neonatology, Children's Hospital and Research Center Oakland, Oakland, CA, USA

[8]Centre for Neonatal Research and Education, Schools of Anatomy, Physiology and Human Biology and Paediatrics and Child Health, University of Western Australia, Subiaco, Australia

[9]Neonatal Unit—Department of Child Health, St George's Hospital, London, UK

[10]Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, University of Toronto, Toronto, Canada

[11]NICU, Department of Clinical Sciences and Community Health, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Università degli Studi di Milano, Milan, Italy

[12]Department of Pediatrics, University of Vermont College of Medicine, Burlington, VT, USA

[13]Department of Pediatrics, University of North Carolina, Chapel Hill, North Carolina, USA

[14]NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia

[15]Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, the Netherlands

[16]University of Antwerp and Antwerp University Hospital, Edegem (Antwerp), Belgium

[17]UCL Institute of Women’s Health, University College London, London, UK

[18]Department of Neonatal Medicine, Hospital Jeanne of Flanders, University hospital of Lille, Lille, France

[19]Department of Neonatology, Universitair Ziekenhuis Brussel, Brussel, Belgium

[20]Health and Social Care Research, King’s College London, London, UK

关键词: Chronic lung disease;    Preterm infants;    Discrimination;    Calibration;    Prognostic models;    Prediction rules;   

Others  :  1144037
DOI  :  10.1186/1471-2431-13-207

 received in 2013-06-03, accepted in 2013-12-12,  发布年份 2013

【 摘 要 】

Background

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.

Methods

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2013 Onland et al.; licensee BioMed Central Ltd.

【 图 表 】

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