%0 Journal Article
%T Imagining the severe asthma decision trees of the future.
%A Bourdin A
%A Bardin P
%A Chanez P
%J Expert Rev Respir Med
%V 0
%N 0
%D 2024 Aug 20
%M 39120156
%F 4.3
%R 10.1080/17476348.2024.2390987
%X <B>UNASSIGNED: </B>There are no validated decision-making algorithms concerning severe asthma (SA) management. Future risks are crucial factors and can be derived from SA trajectories.<BR><B>UNASSIGNED: </B>The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted.<BR><B>UNASSIGNED: </B>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.