Key result
Adding heart rate variability analysis improves asthma control classification accuracy to ~72%.
Why the study?
The role of autonomic nervous system activity in asthma control stratification is not fully understood, and non-invasive methods to improve asthma monitoring are needed.
Cross-Sectional (n=30)
No
Absolute Event Rate: 71.77% vs 64.73%
May aid asthma stratification models; extends autonomic observations but leaves clinical adoption open pending validation.
Autonomic nervous system (ANS) has been suggested to play a major role in the pathogenesis of asthma. This hypothesis has motivated large research, revealing a reduced modulation of the heart rate in subjects with uncontrolled asthma, when compared to asthmatics with controlled symptomatology. In this work, we assessed ANS activity through heart rate variability analysis in a group of asthmatics classified attending to the control of their symptoms. This information was later used for training a logistic regression classifier aiming at differentiating between the levels of control in asthmatic patients. The accuracy of the classifier improved when including ANS information (71.77%, versus 64.73% when only clinical parameters were considered), suggesting that ANS assessment could contribute to better non-invasive asthma monitoring.
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Milagro et al. (2019) conducted a cross-sectional in Asthma (n=30). Heart rate variability analysis vs. Clinical parameters alone was evaluated on Classification accuracy for asthma control. Including heart rate variability analysis improved the accuracy of a logistic regression classifier for asthma control stratification to 71.77%, compared to 64.73% using clinical parameters alone.
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