Methods: Published risk factors were assessed for predictive accuracy (area under the receiver operating characteristic curve [ROC AUC]) and for number needed to treat (NNT).
Results: Key risk factors included: proximity of birth to the RSV season; having siblings; crowding at home; day care; smoking; breast feeding; small for GA; male gender; and familial wheezing/eczema. Proximity of birth to the RSV season appeared the most predictive. Risk factors models from Europe and Canada were found to have a high level of predictive accuracy (ROC AUC both >0.75; NNT for European model 9.5). A model optimised for three risk factors (birth ±10 weeks from start of RSV season, number of siblings ≥2 years and breast feeding for ≤2 months) had a similar level of prediction (ROC AUC: 0.776; NNT: 10.2). An example two-risk factor model (day care attendance and living with ≥2 siblings <5 years old) had a lower level of predictive accuracy (ROC AUC: 0.55; NNT: 26).
Conclusions: An optimised combination of risk factors has the potential to improve the identification of 32–35 wGA infants at heightened risk of RSV hospitalisation. 相似文献