Adverse Drug Events in Ambulatory Care: A Cross-Sectional Study.

Background: Adverse drug events (ADEs) are understudied in the ambulatory care setting. We aim to estimate the prevalence and characteristics of ADEs in outpatient care using electronic health records (EHRs).

Methods: This cross-sectional study included EHR data for patients who had an outpatient encounter at an academic medical center from 1 October 2018 through 31 December 2019.

The role of type 2 diabetes in the severity of adult asthma.

Purpose Of Review: This review summarizes recent basic, translational, and clinical research on type 2 diabetes (T2D) and its relationship with asthma severity in the context of T2D mechanisms and asthma outcomes.

Recent Findings: Several clinical asthma outcomes, such as lung function and exacerbations, demonstrate a strong association between T2D and asthma and support that T2D contributes to worse asthma outcomes. Multiple mechanisms underlying those observed associations, and their representative biomarkers, have been proposed.

Evaluation of a Primary Care-Integrated Mobile Health Intervention to Monitor between-Visit Asthma Symptoms.

Objectives:  This study aimed to evaluate implementation of a digital remote symptom monitoring intervention that delivered weekly symptom questionnaires and included the option to receive nurse callbacks via a mobile app for asthma patients in primary care.

Methods:  Research questions were structured by the NASSS (Nonadoption, Abandonment, Scale-up Spread, and Sustainability) framework. Quantitative and qualitative methods assessed scalability of the electronic health record (EHR)-integrated app intervention implemented in a 12-month randomized controlled trial.

Factors Associated with Completeness of Sex and Gender Fields in Electronic Health Records.

Our purpose was to understand the completeness of sex and gender fields in electronic health record (EHR) data and patient-level factors associated with completeness of those fields. In doing so, we aimed to inform approaches to EHR sex and gender data collection. This was a retrospective observational study using 2016-2021 deidentified EHR data from a large health care system.

Streamlining social media information retrieval for public health research with deep learning.

Objective: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept.

Utilization of electronic health record sex and gender demographic fields: a metadata and mixed methods analysis.

Objectives: Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata.

Materials And Methods: Patients ≥18 years of age in the Mass General Brigham health system with a first Legal Sex entry (registration requirement) between January 8, 2018 and January 1, 2022 were included in this retrospective study.

Weight Loss Interventions for Adults With Obesity-Related Asthma.

Obesity is a common asthma comorbidity in adults, contributing to higher patient morbidity and mortality. Conversely, weight loss can reduce the impact of obesity on asthma and improve patient outcomes by diverse mechanisms including modulating airway inflammation, reducing oxidative stress, and improving lung function. Multiple lifestyle, nonpharmacological, pharmacological, and surgical interventions are effective at reducing weight in the general population.

A deep learning approach for transgender and gender diverse patient identification in electronic health records.

Background: Although accurate identification of gender identity in the electronic health record (EHR) is crucial for providing equitable health care, particularly for transgender and gender diverse (TGD) populations, it remains a challenging task due to incomplete gender information in structured EHR fields.

Objective: Using TGD identification as a case study, this research uses NLP and deep learning to build an accurate patient gender identity predictive model, aiming to tackle the challenges of identifying relevant patient-level information from EHR data and reducing annotation work.

Methods: This study included adult patients in a large healthcare system in Boston, MA, between 4/1/2017 to 4/1/2022.

Association of GLP-1 Receptor Agonists with Chronic Obstructive Pulmonary Disease Exacerbations among Patients with Type 2 Diabetes.

Patients with chronic obstructive pulmonary disease (COPD) and type 2 diabetes (T2D) have worse clinical outcomes compared with patients without metabolic dysregulation. GLP-1 (glucagon-like peptide 1) receptor agonists (GLP-1RAs) reduce asthma exacerbation risk and improve FVC in patients with COPD. To determine whether GLP-1RA use is associated with reduced COPD exacerbation rates, and severe and moderate exacerbation risk, compared with other T2D therapies.

Progestogen Hypersensitivity.

Progestogen hypersensitivity (PH) is a heterogeneous disease characterized by diverse cutaneous manifestations, bronchospasm, and/or anaphylaxis. Possible triggers include ovarian progesterone and exogenous progestogens. The timing of symptoms is critical to diagnose PH: during the luteal phase of the menstrual cycle for the endogenous form and after exposure to progestins for exogenous PH.

Assessing Equitable Recruitment in a Digital Health Trial for Asthma.

Objective: This study aimed to assess a multipronged strategy using primarily digital methods to equitably recruit asthma patients into a clinical trial of a digital health intervention.

Methods: We approached eligible patients using at least one of eight recruitment strategies. We recorded approach dates and the strategy that led to completion of a web-based eligibility questionnaire that was reported during the verbal consent phone call.

Comparative effectiveness of omalizumab, mepolizumab, and dupilumab in asthma: A target trial emulation.

Background: Multiple mAbs are currently approved for the treatment of asthma. However, there is limited evidence on their comparative effectiveness.

Objective: Our aim was to compare the effectiveness of omalizumab, mepolizumab, and dupilumab in individuals with moderate-to-severe asthma.

Utility of Hypoglycemic Agents to Treat Asthma with Comorbid Obesity.

Adults with obesity may develop asthma that is ineffectively controlled by inhaled corticosteroids and long-acting beta-adrenoceptor agonists. Mechanistic and translational studies suggest that metabolic dysregulation that occurs with obesity, particularly hyperglycemia and insulin resistance, contributes to altered immune cell function and low-grade systemic inflammation. Importantly, in these cases, the same proinflammatory cytokines believed to contribute to insulin resistance may also be responsible for airway remodeling and hyperresponsiveness.

Post-Acute COVID-19 Respiratory Symptoms in Patients With Asthma: An Electronic Health Records-Based Study.

Background: Post-viral respiratory symptoms are common among patients with asthma. Respiratory symptoms after acute COVID-19 are widely reported in the general population, but large-scale studies identifying symptom risk for patients with asthma are lacking.

Objective: To identify and compare risk for post-acute COVID-19 respiratory symptoms in patients with and without asthma.

Patient Characteristics Associated With Reactions to Mrgprx2-Activating Drugs in an Electronic Health Record-Linked Biobank.

Background: Mas-related G protein-couple receptor x2 (Mrgprx2) activation underlies many common non-IgE-mediated adverse drug reactions (ADRs), yet the features of patients with reactions to Mrgprx2-activating drugs are unknown.

Objective: To characterize the patient-specific comorbidities and laboratory characteristics associated with listed reactions to Mrgprx2-activating drugs, including fluoroquinolones, morphine, neuromuscular blockade agents, vancomycin, and leuprolide.

Methods: We used a retrospective, observational cohort study design using electronic health record data from adults with an Mrgprx2-activating drug exposure recorded within a hospital system clinical Biobank.

Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review.

Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing.