Introduction
The aim of personalized medicine is to provide a target therapy for each
individual or phenotype, based on the corresponding syndrome or
disease1. Even though machine learning techniques have
identified a number of structures and/or phenotypes in asthma, one has
to be careful in clinical interpretation of these structures, as they
may not represent true endotypes (underlying immunopathological
mechanisms)2. Overlaps in endotypes as well as
clinical presentation of the disease make the delivery of personalized
asthma treatment quite elusive3,4. Furthermore, the
same pattern of symptoms does not necessarily indicate the same
underlying mechanism and moreover, different mechanisms are not mutually
exclusive and may even act synergistically.
The emergence of advanced machine learning algorithms, abundance of
clinically significant data and computing power can be attributed as key
enablers in the development of personalized medicine. There is a
substantial body of scientific work on data-driven methods in asthma
phenotyping and the variables as well as the model chosen can largely
affect the models and obtained results5-11. Careful
selection of both the predictive model and the dataset are essential in
such studies, with expert clinical interpretation being of utmost
importance.
In childhood asthma, inhaled corticosteroids- ICS (in combination with
long-acting beta-agonists, LABA and/or add-on leukotriene receptor
antagonists, LTRA) remain controller medications of choice, although
evidence shows that a significant proportion of patients fail to respond
adequately to such
treatment.5–8The complexity of the disease, or better said, the “umbrella”
diagnosis of asthma that encompasses a number of different phenotypes
underpinned by different pathophysiological mechanisms or distinct
inflammatory pathways (endotypes) seems to be the major obstacle in
asthma management as well as in the development of personalized
treatment approaches9.
An important study on prediction was conducted by Belgrave et
al.10, focusing on preschool wheezers (N=150) with a
large dataset (N(variables)=636) using selected
state-of-the-art techniques for data processing and machine learning and
obtained 90%+ performance in Kappa statistics. The authors also
reported robustness and performance quality when using Random Forest and
that subjective variables are important in distinguishing ill patients
from controls.
A key study focusing on asthma control-based response to controller
medication was conducted in the Childhood Asthma Management Program
(CAMP) cohort using novel machine learning
algorithms11. They reported that asthma control,
bronchodilator response and serum eosinophils were the most predictive
features of asthma control, regardless of the medication used.
Our aim and
contribution
In this work we present the data and results from an observational
childhood asthma study in an ethnically homogeneous, age diverse cohort,
reflecting real-life clinical situations with the majority of patients
having mild to moderate disease. The primary aim was to test the
predictive possibilities for treatment success in pediatric asthma
patients and reveal key variables for understanding the mechanisms
underlying such response. The primary endpoints were four different
parameters of response to treatment, assessed by changes in lung
function i) Forced expiratory volume in one second (FEV1) and ii)
Maximal Expiratory Flow at 50% of Vital Flow Capacity (MEF50), iii)
changes in airway inflammation- Fractional Exhaled nitric oxide (FENO)
and iv) the level of asthma control assessed by a pediatric
pulmonologist. Each of these targets was evaluated at baseline and after
6 months of treatment use alongside other parameters and biomarkers. The
predictive possibilities were tested using the Random Forest (RF) and
Adaptive Boosting (AdaBoost) machine learning algorithms. Since these
algorithms are considered to be black-boxes, we introduced model
explainability by the use of variable importance for evaluating the most
important variables for differentiating between responders and
non-responders. Furthermore, we discuss the use of different
classification metrics for the validation of the results.