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

Autor: Sherry E. Courtney, Carlo Dani, Patrick Truffert, Lisa M. Askie, Sandra Calvert, Michael D. Schreiber, Jeanette M. Asselin, Martijn Miedema, J. Jane Pillow, Giovanni Vento, Roger F. Soll, Neil Marlow, Matthew M. Laughon, Thomas P. A. Debray, Ulrich Thome, David J. Durand, Valentina Vendettuoli, Wes Onland, Patrick Van Reempts, Anton H. van Kaam, Janet L. Peacock, Filip Cools, Martin Offringa, Karel G.M. Moons
Jazyk: angličtina
Rok vydání: 2013
Předmět:
Male
Pediatrics
medicine.medical_specialty
Prognostic models
Calibration (statistics)
Gestational Age
behavioral disciplines and activities
Prediction rules
Physical medicine and rehabilitation
Bias
Predictive Value of Tests
Weight Loss
Discrimination
mental disorders
medicine
Birth Weight
Humans
Infant
Very Low Birth Weight
Pediatrics
Perinatology
and Child Health
Hypoxia
Methodological quality
Bronchopulmonary Dysplasia
Receiver operating characteristic
business.industry
Infant
Newborn
External validation
Infant
Preterm infants
Infant
Low Birth Weight
Models
Theoretical
medicine.disease
Intervention studies
Predictive value
Diuresis
Observational Studies as Topic
Early Diagnosis
ROC Curve
Chronic lung disease
Bronchopulmonary dysplasia
Area Under Curve
Calibration
Pediatrics
Perinatology and Child Health
Female
Human medicine
business
Infant
Premature
Predictive modelling
Research Article
Zdroj: BMC pediatrics
BMC Pediatrics
ISSN: 1471-2431
Popis: 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.
Databáze: OpenAIRE
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