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

Abstract

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.

Figure 1

Flowchart of the systematic review of prediction models for BPD in preterm infants (updated on 01-04-2012) and the possibility of external validation using the PreVILIG dataset.

Figure 2

Methodological quality of the observational cohorts according to the STROBE instrument. Per item in the STROBE instrument, the red colour represents high risk of bias (“No”), the blue colour represents unclear risk of bias (“Unclear”), and the green colour represents low risk of bias (“Yes”).

Figure 3

Calibration plot of prediction model as described by Sinkin[14]for the outcome BPD (panel A) and the combined outcome death or BPD at 36 weeks (panel B).

Figure 4

Calibration plot of prediction models as described by Palta[26]for the outcome BPD (panel A) and the combined outcome death or BPD at 36 weeks (panel B).

Figure 5

Calibration plot of prediction model as described by Kim[34]for the outcome BPD (panel A) and the combined outcome death or BPD at 36 weeks (panel B).

Figure 6

Calibration plot of prediction model as described by Ryan 1996[31]for the outcome BPD (panel A) and the combined outcome death or BPD at 36 weeks (panel B).

Figure 7

Calibration plot of prediction models as decribed by Laughon[40]for the outcome BPD (panel A) and the combined outcome death or BPD at 36 weeks (panel B).