Pediatric Emergency Medicine, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colo.
Division of General Pediatrics, Harvard Medical School, Harvard University, Boston, Mass.
J Allergy Clin Immunol Pract. 2021 Jul;9(7):2619-2626. doi: 10.1016/j.jaip.2021.03.039. Epub 2021 Apr 5.
Severe asthma exacerbations are the primary cause of morbidity and mortality in children with asthma. Accurate prediction of children at risk for severe exacerbations, defined as those requiring systemic corticosteroids, emergency department visit, and/or hospitalization, would considerably reduce health care utilization and improve symptoms and quality of life. Substantial progress has been made in identifying high-risk exacerbation-prone children. Known risk factors for exacerbations include demographic characteristics (ie, low income, minority race/ethnicity), poor asthma control, environmental exposures (ie, aeroallergen exposure/sensitization, concomitant viral infection), inflammatory biomarkers, genetic polymorphisms, and markers from other "omic" technologies. The strongest risk factor for a future severe exacerbation remains having had one in the previous year. Combining risk factors into composite scores and use of advanced predictive analytic techniques such as machine learning are recent methods used to achieve stronger prediction of severe exacerbations. However, these methods are limited in prediction efficiency and are currently unable to predict children at risk for impending (within days) severe exacerbations. Thus, we provide a commentary on strategies that have potential to allow for accurate and reliable prediction of children at risk for impending exacerbations. These approaches include implementation of passive, real-time monitoring of impending exacerbation predictors, use of population health strategies, prediction of severe exacerbation responders versus nonresponders to conventional exacerbation management, and considerations for preschool-age children who can be especially high risk. Rigorous prediction and prevention of severe asthma exacerbations is needed to advance asthma management and improve the associated morbidity and mortality.
严重哮喘发作是儿童哮喘发病率和死亡率的主要原因。准确预测有发生严重哮喘发作风险的儿童（定义为需要全身皮质类固醇、急诊就诊和/或住院治疗的儿童），将显著减少医疗保健的利用，并改善症状和生活质量。在识别易发生严重哮喘发作的高风险儿童方面已取得重大进展。已知的哮喘发作危险因素包括人口统计学特征（如低收入、少数族裔）、哮喘控制不佳、环境暴露（如过敏原暴露/致敏、同时存在病毒感染）、炎症生物标志物、遗传多态性和其他“组学”技术的标志物。发生未来严重哮喘发作的最强危险因素仍然是在前一年中发生过一次。将危险因素结合成复合评分，并使用机器学习等先进的预测分析技术是最近用于实现更准确预测严重哮喘发作的方法。然而，这些方法在预测效率方面存在局限性，目前无法预测即将发生（在数天内）严重哮喘发作的儿童。因此，我们对具有潜在能力的策略进行了评论，这些策略有可能实现对即将发生哮喘发作的儿童进行准确、可靠的预测。这些方法包括实施对即将发生的哮喘发作预测指标的被动、实时监测，使用人群健康策略，预测严重哮喘发作患者对常规哮喘发作管理的反应者与非反应者，以及考虑到特别高风险的学龄前儿童。需要严格预测和预防严重哮喘发作，以推进哮喘管理并降低相关发病率和死亡率。
