Simões Eric A F, Carbonell-Estrany Xavier, Fullarton John R, Liese Johannes G, Figueras-Aloy Jose, Doering Gunther, Guzman Juana
Department of Pediatrics, The University of Colorado School of Medicine and The Children's Hospital, Denver, USA.
Respir Res. 2008 Dec 8;9(1):78. doi: 10.1186/1465-9921-9-78.
The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33-35 weeks' gestational age (GA).
The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33-35 weeks' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.
An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth +/- 10 weeks of start of season, birth weight, breast feeding for < or = 2 months, siblings > or = 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768-0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5-13.6).
A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33-35 weeks' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe.
这项在欧洲开展的研究旨在开发一种经过验证的基于风险因素的模型，以预测孕龄33 - 35周的早产儿呼吸道合胞病毒（RSV）相关住院情况。
利用西班牙FLIP数据集中收集的风险因素开发预测模型，该数据集是一项病例对照研究，纳入了183例孕龄33 - 35周因RSV住院的早产儿以及371例年龄匹配的对照。通过100次重复抽样进行内部验证。采用判别函数分析来分析预测RSV住院的风险因素组合。选择对住院和非住院婴儿有最高判别概率的连续模型。绘制受试者工作特征（ROC）曲线。
最初生成了一个包含15个变量的模型，判别函数为72%，ROC曲线下面积为0.795。经过逐步简化以及对一些变量的重新计算，得到了一个最终模型，包含7个变量：出生时间（相对于季节开始时间±10周）、出生体重、母乳喂养≤2个月、兄弟姐妹≥2岁、有特应性疾病的家庭成员、有喘息症状的家庭成员以及性别。该模型的判别率为71%，ROC曲线下面积为0.791。在敏感性截距为0.75时，假阳性率为0.33。100次重复抽样结果显示，判别函数的均值为72%（标准差：2.18），ROC曲线下面积的中位数为0.785（范围：0.768 - 0.790），表明内部验证良好。计算得出，为所有有风险的患者提供75%保护水平的干预措施的需治数（NNT）为11.7（95%置信区间：9.5 - 13.
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