Department of Neonatology, Emma Children's Hospital, Academic Medical Center, Amsterdam, the Netherlands.
BMC Pediatr. 2013 Dec 17;13:207. doi: 10.1186/1471-2431-13-207.
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.
支气管肺发育不良（BPD）是早产儿的常见并发症。已经开发了使用出生后早期临床参数预测 BPD 的非常不同的模型，但很少进行广泛的定量验证。本研究的目的是回顾和验证用于 BPD 的临床预测模型。
我们搜索了主要的电子数据库和会议摘要。使用 STROBE 仪器评估方法学质量。使用 3229 名有发生 BPD 风险的患者的个体患者数据集对检索到的模型进行外部验证。使用受试者工作特征曲线通过计算曲线下面积（AUC）来评估每个模型的区分度。通过视觉比较预测和观察的 BPD 概率来评估最佳区分模型的校准。
我们确定了 26 个用于 BPD 的临床预测模型。尽管 STROBE 仪器对质量的评价从中等到良好，但只有四个模型利用了外部验证，没有一个模型展示了预测值的校准。对于与我们数据集变量匹配的 19 个预测模型，BPD 结局的 AUC 范围为 0.50 至 0.76。仅两个五个最佳区分模型显示出良好的校准。
外部验证表明，除了两个有前途的模型外，大多数现有的临床预测模型对 BPD 的预测准确性较差或中等。为了提高预测准确性并确定未来旨在降低 BPD 风险的干预研究的早产儿，需要添加其他变量。随后，在临床实施之前，应该使用适当的影响分析对该模型进行外部验证。
