Preschool wheezing and childhood asthma create a heavy disease burden which is only exacerbated by the complexity of the conditions. Preschool wheezing exhibits both “curricular” and “aetiological” heterogeneity: that is, heterogeneity across patients both in the time-course of its development and in its underpinning pathological mechanisms. Since these are not fully understood, but clinical presentations across patients may nonetheless be similar, current diagnostic labels are imprecise—not mapping cleanly onto underlying disease mechanisms—and prognoses uncertain. These uncertainties also make a identifying new targets for therapeutic intervention difficult. In the past few decades, carefully designed birth cohort studies have collected “big data” on a large scale, incorporating not only a wealth of longitudinal clinical data, but also detailed information from modalities as varied as imaging, multiomics, and blood biomarkers. The profusion of big data has seen the proliferation of what we term “modern data approaches” (MDAs)—grouping together machine learning, artificial intelligence, and data science—to make sense and make use of this data. In this review, we survey applications of MDAs (with an emphasis on machine learning) in childhood wheeze and asthma, highlighting the extent of their successes in providing tools for prognosis, unpicking the curricular heterogeneity of these conditions, clarifying the limitations of current diagnostic criteria, and indicating directions of research for uncovering the etiology of the diseases underlying these conditions. Specifically, we focus on the trajectories of childhood wheeze phenotypes. Further, we provide an explainer of the nature and potential use of MDAs and emphasize the scope of what we can hope to achieve with them.

中文翻译：

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了解从学前喘息到学龄哮喘的进展：现代数据方法有帮助吗？

学龄前喘息和儿童哮喘造成沉重的疾病负担，而且病情的复杂性只会加剧这种负担。学龄前喘息表现出“课程”和“病因”异质性：即患者之间在其发展的时间过程和其基础病理机制方面的异质性。由于这些尚未完全被理解，但患者的临床表现可能仍然相似，目前的诊断标签不精确——没有清楚地映射到潜在的疾病机制——并且预后不确定。这些不确定性也使得确定治疗干预的新目标变得困难。在过去的几十年里，精心设计的出生队列研究大规模收集了“大数据”，不仅包含大量的纵向临床数据，还包含来自成像、多组学和血液生物标志物等多种模式的详细信息。大数据的丰富催生了我们所说的“现代数据方法”（MDA）——将机器学习、人工智能和数据科学组合在一起——以理解和利用这些数据。在这篇综述中，我们调查了 MDA（重点是机器学习）在儿童喘息和哮喘中的应用，强调了它们在提供预后工具、消除这些疾病的课程异质性、澄清当前诊断标准的局限性方面的成功程度，并指出揭示这些疾病的病因学的研究方向。具体来说，我们关注儿童喘息表型的轨迹。此外，我们还解释了 MDA 的性质和潜在用途，并强调了我们希望利用它们实现的目标范围。