Abstract
AbstractChronic respiratory diseases are often difficult to cure and are likely to originate early in life. Therefore, early identification of such diseases is of interest for early prevention.We explored the potential to predict these almost from birth; using data at 1 month of age, we attempted to predict disease occurrence 4 years later in life. Our data came from the Barwon Infant Study; after cleaning and processing, we had measurements on 41 variables from 401 participants.We considered three respiratory diseases: asthma, wheeze and hay fever. As predictors, we used a variety of information that would be available in a clinical setting. Of particular interest to our investigation was whether lung function measurements (newly available at such an early age) would helpfully improve predictive accuracy. We also investigated whether maternal smoking (previously associated with respiratory illnesses) is a helpful predictor.Our methods included logistic regression as the main model, multiple imputation to deal with missing values, stepwise selection and LASSO to select variables, and cross-validation to assess performance. We measured predictive performance using AUC (area under the receiver operating characteristic curve), sensitivity and specificity.Broadly, we found that the best models had only modest predictive power for each disease. For example, for asthma we achieved an AUC of 0.67, a sensitivity of 68% and a corresponding specificity of 63%. Performance for the other two diseases was similar.We also found that our lung function measurements did not improve predictive performance; some-what surprisingly, this was also true for maternal smoking. The most useful predictors included, among others, family history of these diseases and variables relating to the size of the infants.Given the modest performance of these models, our findings suggest that very early prediction of respiratory illnesses is still a challenging task.
Publisher
Cold Spring Harbor Laboratory