Prediction of adult asthma risk in early childhood using novel adult asthma predictive risk scores

Asthma Inception: Epidemiologic Risk Factors and Natural History Across the Life Course

Asthma is a descriptive label for an obstructive inflammatory disease in the lower airways manifesting with symptoms including breathlessness, cough, difficulty in breathing, and wheezing. From a clinician's point of view, asthma symptoms can commence at any age, although most patients with asthma-regardless of their age of onset-seem to have had some form of airway problems during childhood. Asthma inception and related pathophysiologic processes are therefore very likely to occur early in life, further evidenced by recent lung physiologic and mechanistic research. Herein, we present state-of-the-art updates on the role of genetics and epigenetics, early viral and bacterial infections, immune response, and pathophysiology, as well as lifestyle and environmental exposures, in asthma across the life course. We conclude that early environmental insults in genetically vulnerable individuals inducing abnormal, pre-asthmatic airway responses are key events in asthma inception, and we highlight disease heterogeneity across ages and the potential shortsightedness of treating all patients with asthma using the same treatments. Although there are no interventions that, at present, can modify long-term outcomes, a precision-medicine approach should be implemented to optimize treatment and tailor follow-up for all patients with asthma.

Development of a predictive model for pediatric atopic dermatitis: A retrospective cross-sectional nationwide database study

BACKGROUND

The rise in prevalence of atopic dermatitis (AD) has been correlated with numerous elements of the exposome, modern-day lifestyle, and familial history. The combined analysis of familial history and other risk elements may allow us to understand the driving factors behind the development of AD.

OBJECTIVE

To develop prediction models to assess the risk of developing AD using a large and diverse cohort (N = 77,525) and easily assessed risk factors.

METHODS

We analyzed electronic medical record data from Leumit Health System. Documented predictive factors include sex, season of birth, environment (urban/rural), socioeconomic status, household smoking, diagnosed skin conditions, number of siblings, a paternal, maternal, or sibling history of an atopic condition, and antibiotic prescriptions during pregnancy or after birth. Predictive models were trained and validated on the data set.

RESULTS

Medium (odds ratio [OR] 2.04, CI 1.92-2.17, P < .001) and high (OR 2.13, CI 1.95-2.34, P < .001) socioeconomic status, a previous diagnosis of contact dermatitis (OR 2.57, CI 2.37-2.78, P < .001), presence of siblings with an AD diagnosis (OR 2.21, CI 2.04-2.40, P < .001), and the percentage of siblings with any atopic condition (OR 2.58, CI 2.09-3.17, P < .001) drove risk for AD in a logistic regression model. A random forest prediction model with a sensitivity of 61% and a specificity of 84% was developed. Generalized mixed models accounting for the random effect of familial relationships boasted an area under the curve of 0.98.

CONCLUSION

Predictive modeling using noninvasive and accessible inputs is a powerful tool to stratify risk for developing AD.

Understanding progression from pre-school wheezing to school-age asthma: Can modern data approaches help?

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.