Enhancing Healthcare Data Integration: A Machine Learning Approach to Harmonizing Laboratory Labels.

Variations in laboratory test names across healthcare systems-stemming from inconsistent terminologies, abbreviations, misspellings, and assay vendors-pose significant challenges to the integration and analysis of clinical data. These discrepancies hinder interoperability and complicate efforts to extract meaningful insights for both clinical research and patient care. In this study, we propose a machine learning-driven solution, enhanced by natural language processing techniques, to standardize lab test names.

Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records.

Background: Asthma is a heterogeneous disease with a diverse array of phenotypes that differ in inflammatory characteristics and severity. Identifying and classifying phenotypes in the real world could provide a foundation to improve and personalize asthma management. Leveraging machine learning in analyzing electronic health records (EHRs) provides an opportunity to identify real-world asthma phenotypes.

Characteristics and outcomes of ambulatory patients with suspected COVID-19 at a respiratory referral center.

Rationale: SARS-CoV-2 continues to cause a global pandemic and management of COVID-19 in outpatient settings remains challenging.

Objective: We sought to describe characteristics of patients with chronic respiratory disease (CRD) experiencing symptoms consistent with COVID-19, who were seen in a novel Acute Respiratory Clinic, prior to widely available testing, emergence of variants, COVID-19 vaccination, and post-vaccination (breakthrough) SARS-CoV-2 infections.

Methods: Retrospective electronic medical record data were analyzed from 907 adults with presumed COVID-19 seen between March 16, 2020 and January 7, 2021.

NHLBI-CMREF Workshop Report on Pulmonary Vascular Disease Classification: JACC State-of-the-Art Review.

The National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes.

Asthma biologics: Comparing trial designs, patient cohorts and study results.

Objective: Five biologic therapies have FDA-approved indications for difficult-to-control asthma. The clinical trials that proved the efficacy and safety of these biologics were often similar in their inclusion criteria, study designs, and endpoints. Many of these trials have been reanalyzed post hoc to identify subsets of subjects considered to be enhanced responders.

BAL Cell Gene Expression in Severe Asthma Reveals Mechanisms of Severe Disease and Influences of Medications.

Gene expression of BAL cells, which samples the cellular milieu within the lower respiratory tract, has not been well studied in severe asthma. To identify new biomolecular mechanisms underlying severe asthma by an unbiased, detailed interrogation of global gene expression. BAL cell expression was profiled in 154 asthma and control subjects.

Sialylation of MUC4β N-glycans by ST6GAL1 orchestrates human airway epithelial cell differentiation associated with type-2 inflammation.

Although type-2-induced (T2-induced) epithelial dysfunction is likely to profoundly alter epithelial differentiation and repair in asthma, the mechanisms for these effects are poorly understood. A role for specific mucins, heavily N-glycosylated epithelial glycoproteins, in orchestrating epithelial cell fate in response to T2 stimuli has not previously been investigated. Levels of a sialylated MUC4β isoform were found to be increased in airway specimens from asthmatic patients in association with T2 inflammation.

Dysfunctional ErbB2, an EGF receptor family member, hinders repair of airway epithelial cells from asthmatic patients.

Background: Genetic and genomic data increasingly point to the airway epithelium as critical to asthma pathogenesis. Epithelial growth factor (EGF) family members play a fundamental role in epithelial differentiation, proliferation, and repair. Although expression of erythroblastosis oncogene B2 (ErbB2) mRNA, an EGF family receptor, was reported to be lower in asthmatic patients, little is understood about its functional role.

Advanced and Accurate Mobile Health Tracking Devices Record New Cardiac Vital Signs.

Cardiovascular disease remains the leading cause of death and disease worldwide. As demands on an already resource-constrained healthcare system intensify, disease prevention in the future will likely depend on out-of-office monitoring of cardiovascular risk factors. Mobile health tracking devices that can track blood pressure and heart rate, in addition to new cardiac vital signs, such as physical activity level and pulse wave velocity (PWV), offer a promising solution.

Use of a composite symptom score during challenge in patients with suspected aspirin-exacerbated respiratory disease.

Background: Aspirin-exacerbated respiratory disease is characterized by asthma, chronic rhinosinusitis, nasal polyposis, and sensitivity to aspirin and other nonsteroidal anti-inflammatory drugs. Confirmation of the diagnosis requires provocation challenge with resulting upper and/or lower airways reactivity. Currently, determination of a positive challenge result is based solely on clinical judgment that synthesizes subjective symptoms and objective measures, as a concomitant increase in nasal or bronchial airways resistance is measured in only half of patients.

Gene Expression Correlated with Severe Asthma Characteristics Reveals Heterogeneous Mechanisms of Severe Disease.

Rationale: Severe asthma (SA) is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of bronchial epithelial cells in individuals with asthma have provided biological insight and underscored possible mechanistic differences between individuals.

Objectives: Identify networks of genes reflective of underlying biological processes that define SA.

Improvement in Asthma Control Using a Minimally Burdensome and Proactive Smartphone Application.

Background: Integrated chronic disease treatment models that enable patient self-care and shared treatment decision making have recently been shown to improve medication adherence and outcomes. Smartphone applications (apps) are a readily available means to enable this model, although sustained user engagement remains a challenge.

Objective: To assess the efficacy of improving asthma control using a proactive smartphone app without required regular inputs.

Emerging concepts: mast cell involvement in allergic diseases.

In a process known as overt degranulation, mast cells can release all at once a diverse array of products that are preformed and present within cytoplasmic granules. This occurs typically within seconds of stimulation by environmental factors and allergens. These potent, preformed mediators (ie, histamine, heparin, serotonin, and serine proteases) are responsible for the acute symptoms experienced in allergic conditions such as allergic conjunctivitis, allergic rhinitis, allergy-induced asthma, urticaria, and anaphylaxis.

Gene expression in relation to exhaled nitric oxide identifies novel asthma phenotypes with unique biomolecular pathways.

Rationale: Although asthma is recognized as a heterogeneous disease associated with clinical phenotypes, the molecular basis of these phenotypes remains poorly understood. Although genomic studies have successfully broadened our understanding in diseases such as cancer, they have not been widely used in asthma studies.

Objectives: To link gene expression patterns to clinical asthma phenotypes.