Imagining the severe asthma decision trees of the future

There are no validated decision-making algorithms concerning severe asthma (SA) management. Future risks are crucial factors and can be derived from SA trajectories.

The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted.

Asthma severity is currently defined a priori, thereby precluding a role for early interventions aiming to prevent outcomes such as exacerbations (systemic corticosteroids exposure) and lung function decline. Asthma 'at-risk' might represent the ultimate paradigm but merits longitudinal studies considering modern interventions. Real exacerbations, severe airway hyperresponsiveness, excessive T2-related biomarkers, noxious environments and patient behaviors, harms of OCS and high-doses inhaled corticosteroids (ICS), and low adherence-to-effectiveness ratios of ICS-containing inhalers are predictors of future risks. New tools such as imaging, genetic, and epigenetic signatures should be used. Logical and numerical artificial intelligence may be used to generate a consistent risk score. A pragmatic definition of response to treatments will allow development of a validated and applicable algorithm. Biologics have the best potential to minimize the risks, but cost remains an issue. We propose a simplified six-step algorithm for decision-making that is ultimately aiming to achieve asthma remission.