Decision tree-based rules outperform risk scores for childhood asthma prognosis

There are no widely accepted prognostic tools for childhood asthma; this is in part due to the multifactorial and time-dependent nature of mechanisms and risk factors that contribute to asthma development. Our study objective was to develop and evaluate the prognostic performance of conditional inference decision tree-based rules using the Pediatric Asthma Risk Score (PARS) predictors as an alternative to the existing logistic regression-based risk score for childhood asthma prediction at 7 years in a high-risk population.

The Canadian Asthma Primary Prevention Study data were used to develop, compare, and contrast the prognostic performance (area under the curve [AUC], sensitivity, and specificity) of conditional inference tree-based decision rules to the pediatric asthma risk score for the prediction of childhood asthma at 7 years.

Conditional inference decision tree-based rules have higher prognostic performance (AUC: 0.85; 95% CI: 0.81, 0.88; sensitivity = 47%; specificity = 93%) than the pediatric asthma risk score at an optimal cutoff of ≥6 (AUC: 0.71; 95% CI: 0.67, 0.76; sensitivity = 60%; specificity = 74%). Moreover, the pediatric asthma risk score is not linearly related to asthma risk, and at any given pediatric asthma risk score value, different combinations of its pediatric asthma risk score clinical variables differentially predict asthma risk.

Conditional inference tree-based decision rules could be a useful childhood asthma prognostic tool, providing an alternative way to identify unique subgroups of at-risk children, and insights into associations and effect mechanisms that are suggestive of appropriate tailored preventive interventions. However, the feasibility and effectiveness of such decision rules in clinical practice is warranted.