Lung Function Trajectory Types in Never-Smoking Adults With Asthma: Clinical Features and Inflammatory Patterns

Features of severe asthma response to anti-IL5/IL5r therapies: identikit of clinical remission

Introduction

Clinical remission (CliR) achievement has been recognized as a new potential outcome in severe asthma. Nevertheless, we still lack a detailed profile of what features could better identify patients undergoing clinical remission. In this study, we aim to address this issue, tracing a possible identikit of patients fulfilling remission criteria.

Methods

We enrolled 266 patients with severe eosinophilic asthma (SEA) treated with a 12-month course of anti-IL5/IL5 receptor (IL5r) monoclonal antibodies. Patients with no exacerbation, OCS withdrawal, ACT ≥ 20 and FEV1 ≥ 80% after 1 year of biologic treatment were classified as in clinical remission.

Results

30.5% of the enrolled patients achieved remission after biologic administration. CliR group showed a lower number of baseline asthma exacerbations and better lung function parameters, with a trend for higher ACT scores and a less frequent history of a positive skin prick test. CliR achievement was unlikely in presence of a higher BMI, a positive skin prick test, an increased number of asthma exacerbations before biologic treatment, anti-muscarinic administration, and a previous diagnosis of EGPA, bronchiectasis or osteoporosis. In contrast, a better lung function, an increased blood eosinophilic count, the presence of chronic rhinosinusitis with nasal polyps and a more frequent use of reliever therapy predicts remission development. Changes in exacerbations number, OCS use, ACT scores and FEV1% between remittent and non-remittent patients arise at specific follow up timepoints and are positively associated with CliR achievement.

Discussion

anti-IL5/IL5r biologics can induce CliR in a proportion of patients with SEA. Patients achieving remission demonstrate specific clinical, functional and inflammatory features, as well as a specific moment of improvement in all the CliR items.

Data-driven overdiagnosis definitions: A scoping review

INTRODUCTION

Adequate methods to promptly translate digital health innovations for improved patient care are essential. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have been sources of digital innovation and hold the promise to revolutionize the way we treat, manage and diagnose patients. Understanding the benefits but also the potential adverse effects of digital health innovations, particularly when these are made available or applied on healthier segments of the population is essential. One of such adverse effects is overdiagnosis.

OBJECTIVE

to comprehensively analyze quantification strategies and data-driven definitions for overdiagnosis reported in the literature.

METHODS

we conducted a scoping systematic review of manuscripts describing quantitative methods to estimate the proportion of overdiagnosed patients.

RESULTS

we identified 46 studies that met our inclusion criteria. They covered a variety of clinical conditions, primarily breast and prostate cancer. Methods to quantify overdiagnosis included both prospective and retrospective methods including randomized clinical trials, and simulations.

CONCLUSION

a variety of methods to quantify overdiagnosis have been published, producing widely diverging results. A standard method to quantify overdiagnosis is needed to allow its mitigation during the rapidly increasing development of new digital diagnostic tools.

Relationships Between High-Resolution Computed Tomographic Features and Lung Function Trajectory in Patients With Asthma

PURPOSE

A subset of asthmatics suffers from persistent airflow limitation, known as remodeled asthma, despite optimal treatment. Typical quantitative scoring methods to evaluate structural changes of airway remodeling on high-resolution computed tomography (HRCT) are time-consuming and laborious. Thus, easier and simpler methods are required in clinical practice. We evaluated the clinical usefulness of a simple, semi-quantitative method based on 8 HRCT parameters by comparing asthmatics with a persistent decline of post-bronchodilator (BD)-FEV1 to those with a BD-FEV1 that normalized over time and evaluated the relationships of the parameters with BD-FEV1.

METHODS

Asthmatics (n = 59) were grouped into 5 trajectories (Trs) according to the changes of BD-FEV1 over 1 year. After 9-12 months of guideline-based treatment, HRCT parameters including emphysema, bronchiectasis, anthracofibrosis, bronchial wall thickening (BWT), fibrotic bands, mosaic attenuation on inspiration, air-trapping on expiration, and centrilobular nodules were classified as present (1) or absent (0) in 6 zones.

RESULTS

The Tr5 group (n = 11) was older and exhibited a persistent decline in BD-FEV1. The Tr5 and Tr4 groups (n = 12), who had a lower baseline BD-FEV1 that normalized over time, had longer durations of asthma, frequent exacerbations, and higher doses of steroid use compared to the Tr1-3 groups (n = 36), who had a normal baseline BD-FEV1. The Tr5 group had higher emphysema and BWT scores than the Tr4 (P = 8.25E-04 and P = 0.044, respectively). Scores for the other 6 parameters were not significantly different among the Tr groups. BD-FEV1 was inversely correlated with the emphysema and BWT scores in multivariate analysis (P = 1.70E-04, P = 0.006, respectively).

CONCLUSIONS

Emphysema and BWT are associated with airway remodeling in asthmatics. Our simple, semi-quantitative scoring system based on HRCT may be an easy-to-use method for estimating airflow limitation.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

Biomarkers for Severe Asthma: Lessons From Longitudinal Cohort Studies

Severe asthma (SA) is a heterogeneous disease characterized by uncontrolled symptoms, frequent exacerbations, and lung function decline. The discovery of phenotypes and endotypes of SA significantly improves our understanding of its pathophysiology and allows the advent of biologics blocking multiple molecular targets. The advances have mainly been made in type 2-high asthma associated with elevated type 2 inflammatory biomarkers such as immunoglobulin E (IgE), interleukins (IL)-4, IL-5, and IL-13. Previous clinical trials have demonstrated that type 2 biomarkers, including blood/sputum eosinophils and the fraction of exhaled nitric oxide (FeNO), were correlated to severe airway inflammation, persistent symptoms, frequent exacerbations, and the clinical efficacy of these biomarkers in predicting treatment outcomes of type 2-targeting biologics. However, it is well known that type 2 inflammation is partially attributable to the pathogenesis of SA. Although some recent studies have suggested that type 2-low and mixed phenotypes of asthma are important contributors to the heterogeneity of SA, many questions about these non-type 2 asthma phenotypes remain to be solved. Consequently, many efforts to investigate and find novel biomarkers for SA have also made in their methods. Many cross-sectional experimental studies in large-scale cohorts and randomized clinical trials have proved their value in understanding SA. More recently, real-world cohort studies have been in the limelight for SA research, which is unbiased and expected to give us an answer to the unmet needs of the heterogeneity of SA.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.