2021年，全球疾病负担（global burden of disease，GBD）数据显示，全球哮喘年龄标准化患病率为3 340.1/10万，患者总数约为2.6亿，病死率为5.2/10万，死亡病例达43.6万例。我国2012年至2015年的调查显示，20岁及以上人群喘息相关哮喘患病率为4.2%，患者总数约4 570万，但诊断率仅为28.8%，控制率仅为28.5%，远低于国际水平，凸显了加强哮喘管理和干预的紧迫性。2024年3月，中华医学会呼吸病学分会发布了《支气管哮喘防治指南（2024年版）》（以下简称“2024版指南”）。该指南在路径诊断上，完善了哮喘诊断标准，强调可变呼气气流的判断依据（如支气管舒张试验、激发试验等），并提出“拟诊路径”，适用于基层和条件有限的医疗机构，以提升诊断率并避免过度治疗。在哮喘分期与分级方面，新增了“临床治愈”概念，定义为无症状时长≥1年且无需全身糖皮质激素治疗；取消了“间歇状态”分类，将哮喘严重程度简化为轻度、中度和重度3级采用，并提出动态评估模式。在评估体系上，新增2型炎症分型评估，推荐检测外周血嗜酸性粒细胞计数（eosinophils count， EOS）、呼出气一氧化氮（fractional exhaled nitric oxid， FeNO）等生物标志物，以指导个体化治疗，同时强调共病筛查和危险因素评估。治疗策略上，慢性持续期治疗采用阶梯式管理，推荐首选用吸入糖皮质激素（inhaled corticosteroids， ICS）福莫特罗作为缓解药物（路径1），以减少急性发作风险；重度哮喘管理强调生物靶向药物的应用，如抗IgE、抗白细胞介素（interleukin，IL）-5等单抗；急性发作期处理则根据严重程度分级推荐治疗方案。尽管2024版指南取得了显著进展，但仍面临挑战。我国哮喘流行病学数据更新滞后，亟需开展全国性调查以反映最新疾病负担；基层医院哮喘诊断率低，炎症评估和动态管理欠缺，需加强基层医院的相关能力建设；生物制剂在我国的真实世界数据不足，限制了其在哮喘精准治疗中的应用；信息技术在哮喘管理中的应用仍处于探索阶段，需借助5G等技术提升患者教育和随访效率。未来，我国哮喘防治需进一步优化早诊早治策略，动态识别炎症表型，建立药物反应预测模型，并推动人工智能辅助诊疗，以实现更精细。

According to the Global Burden of Disease (GBD) data for 2021, the global age-standardized prevalence of asthma is 3 340.1/100 000, with a total of about 260 million patients, a mortality rate of 5.2/100 000, and 436 000 deaths. A 2012-2015 survey conducted in China shows that the prevalence of wheezing-related asthma among people aged 20 and above is 4.2%, with a total of about 45.7 million patients. However, the diagnosis rate is only 28.8%, and the control rate is only 28.5%, far below the international level, highlighting the urgent need for better asthma management and intervention. In March 2024, the Chinese Thoracic Society (CTS) released the Guidelines for the Prevention and Management of Bronchial Asthma (2024 Edition) (hereinafter referred to as the "2024 Guidelines"). For diagnostic pathways, the 2024 Guidelines improve the diagnostic criteria for asthma, emphasizing the evidence for variable expiratory airflow (such as bronchodilator tests, provocation tests, etc.). A "presumptive diagnosis pathway" is proposed for primary care and resource-limited medical institutions to improve the diagnosis rate and avoid overtreatment. In terms of staging and classification, the concept of "clinical remission" is introduced, defined as being asymptomatic for ≥1 year without the need for systemic glucocorticoid therapy. The classification of "intermittent state" is eliminated, and asthma severity is now simplified into three levels—mild, moderate and severe—with a dynamic assessment model proposed. The assessment system newly includes a type 2 inflammatory phenotype assessment, recommending the measurement of biomarkers such as peripheral blood eosinophil count (EOS) and fractional exhaled nitric oxide (FeNO) to guide individualized treatment, while also emphasizing comorbidity screening and risk factor assessment. In terms of treatment strategies, a stepwise management approach is used for chronic persistent treatment, with inhaled corticosteroid (ICS)-formoterol recommended as the preferred reliever (Pathway 1) to reduce the risk of acute exacerbations. The management of severe asthma emphasizes the use of biological targeted drugs, such as anti-IgE and anti-interleukin (IL)-5 monoclonal antibodies, while the treatment of acute exacerbations is recommended based on the severity level. Despite the significant progress made in the 2024 Guidelines, challenges remain. Epidemiological data on asthma in China are outdated, highlighting the urgent need for nationwide surveys to reflect the latest disease burden. Diagnosis rates in primary care are low, and inflammation assessment and dynamic mana-gement are insufficient, requiring strengthened capacity building at the primary care level. Real-world data on biologics in China are limited, restricting their application in precision therapy. The application of information technology in asthma management is still at an exploratory stage, and technologies like 5G should be leveraged to enhance patient education and follow-up efficiency. In the future, asthma prevention and treatment in China need to further optimize strategies for early diagnosis and early treatment, dynamically identify inflammatory phenotypes, establish drug response prediction models, and promote AI-assisted diagnosis and treatment to achieve more precise management.