Abstract：
UNASSIGNED: There are no validated decision-making algorithms concerning severe asthma (SA) management. Future risks are crucial factors and can be derived from SA trajectories.
UNASSIGNED: The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted.
UNASSIGNED: 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.
UNASSIGNED: The future severe asthma-decision trees should revisit current knowledge and gaps. A focused literature search has been conducted.
UNASSIGNED: 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.
摘要：
■没有关于严重哮喘（SA）管理的有效决策算法。未来风险是关键因素，可以从SA轨迹中得出。
■未来的严重哮喘决策树应该重新审视当前的知识和差距。已进行了重点文献检索。
■哮喘的严重程度目前是先验定义的，因此排除了早期干预措施的作用，旨在预防加重（全身皮质类固醇暴露）和肺功能下降等结果。哮喘“高危”可能代表最终范式，但值得考虑现代干预措施的纵向研究。真正的恶化，严重的气道高反应性，过度的T2相关生物标志物，有害的环境和病人的行为，OCS和高剂量吸入性糖皮质激素(ICS)的危害以及含ICS的吸入器的低依从性-有效性比率是未来风险的预测因素.成像等新工具，应使用遗传和表观遗传特征。逻辑和数值人工智能可用于生成一致的风险评分。对治疗反应的实用定义将允许开发经过验证和适用的算法。生物制品有最大的潜力，以尽量减少风险，但成本仍然是个问题。我们提出了一种简化的六步决策算法，最终旨在实现哮喘缓解。
■未来的严重哮喘决策树应该重新审视当前的知识和差距。已进行了重点文献检索。
■哮喘的严重程度目前是先验定义的，因此排除了早期干预措施的作用，旨在预防加重（全身皮质类固醇暴露）和肺功能下降等结果。哮喘“高危”可能代表最终范式，但值得考虑现代干预措施的纵向研究。真正的恶化，严重的气道高反应性，过度的T2相关生物标志物，有害的环境和病人的行为，OCS和高剂量吸入性糖皮质激素(ICS)的危害以及含ICS的吸入器的低依从性-有效性比率是未来风险的预测因素.成像等新工具，应使用遗传和表观遗传特征。逻辑和数值人工智能可用于生成一致的风险评分。对治疗反应的实用定义将允许开发经过验证和适用的算法。生物制品有最大的潜力，以尽量减少风险，但成本仍然是个问题。我们提出了一种简化的六步决策算法，最终旨在实现哮喘缓解。
