Glucagon-like Peptide-1 Receptor Pathway Attenuates Platelet Activation in Aspirin-Exacerbated Respiratory Disease.

Platelets are key contributors to allergic asthma and aspirin-exacerbated respiratory disease (AERD), an asthma phenotype involving platelet activation and IL-33-dependent mast cell activation. Human platelets express the glucagon-like peptide-1 receptor (GLP-1R). GLP-1R agonists decrease lung IL-33 release and airway hyperresponsiveness in mouse asthma models. We hypothesized that GLP-1R agonists reduce platelet activation and downstream platelet-mediated airway inflammation in AERD. GLP-1R expression on murine platelets was assessed using flow cytometry. We tested the effect of the GLP-1R agonist liraglutide on lysine-aspirin (Lys-ASA)-induced changes in airway resistance, and platelet-derived mediator release in a murine AERD model. We conducted a prospective cohort study comparing the effect of pretreatment with liraglutide or vehicle on thromboxane receptor agonist-induced in vitro activation of platelets from patients with AERD and nonasthmatic controls. GLP-1R expression was higher on murine platelets than on leukocytes. A single dose of liraglutide inhibited Lys-ASA-induced increases in airway resistance and decreased markers of platelet activation and recruitment to the lung in AERD-like mice. Liraglutide attenuated thromboxane receptor agonist-induced activation as measured by CXCL7 release in plasma from patients with AERD and CD62P expression in platelets from both patients with AERD (n = 31) and nonasthmatic, healthy controls (n = 11). Liraglutide, a Food and Drug Administration-approved GLP-1R agonist for treatment of type 2 diabetes and obesity, attenuates in vivo platelet activation in an AERD murine model and in vitro activation in human platelets in patients with and without AERD. These data advance the GLP-1R axis as a new target for platelet-mediated inflammation warranting further study in asthma.

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

Rationale: 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. Objectives: To determine whether GLP-1RA use is associated with reduced COPD exacerbation rates, and severe and moderate exacerbation risk, compared with other T2D therapies. Methods: A retrospective, observational, electronic health records-based study was conducted using an active comparator, new-user design of 1,642 patients with COPD in a U.S. health system from 2012 to 2022. The COPD cohort was identified using a previously validated machine learning algorithm that includes a natural language processing tool. Exposures were defined as prescriptions for GLP-1RAs (reference group), DPP-4 (dipeptidyl peptidase 4) inhibitors (DPP-4is), SGLT2 (sodium-glucose cotransporter 2) inhibitors, or sulfonylureas. Measurements and Main Results: Unadjusted COPD exacerbation counts were lower in GLP-1RA users. Adjusted exacerbation rates were significantly higher in DPP-4i (incidence rate ratio, 1.48 [95% confidence interval, 1.08-2.04]; P = 0.02) and sulfonylurea (incidence rate ratio, 2.09 [95% confidence interval, 1.62-2.69]; P < 0.0001) users compared with GLP-1RA users. GLP-1RA use was also associated with significantly reduced risk of severe exacerbations compared with DPP-4i and sulfonylurea use, and of moderate exacerbations compared with sulfonylurea use. After adjustment for clinical covariates, moderate exacerbation risk was also lower in GLP-1RA users compared with DPP-4i users. No statistically significant difference in exacerbation outcomes was seen between GLP-1RA and SGLT2 inhibitor users. Conclusions: Prospective studies of COPD exacerbations in patients with comorbid T2D are warranted. Additional research may elucidate the mechanisms underlying these observed associations with T2D medications.

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. METHODS: We emulated a hypothetical randomized trial using electronic health records from a large US-based academic health care system. Participants aged 18 years or older with baseline IgE levels between 30 and 700 IU/mL and peripheral eosinophil counts of at least 150 cells/µL were eligible for study inclusion. The study period extended from March 2016 to August 2021. Outcomes included the incidence of asthma-related exacerbations and change in baseline FEV1 value over 12 months of follow-up. RESULTS: In all, 68 individuals receiving dupilumab, 68 receiving omalizumab, and 65 receiving mepolizumab met the inclusion criteria. Over 12 months of follow-up, 31 exacerbations occurred over 68 person years (0.46 exacerbations per person year) in the dupilumab group, 63 over 68 person years (0.93 per person year) in the omalizumab group, and 86 over 65 person years (1.32 per person year) in the mepolizumab group (adjusted incidence rate ratios: dupilumab vs mepolizumab, 0.28 [95% CI = 0.09-0.84]; dupilumab vs omalizumab, 0.36 [95% CI = 0.12-1.09]; and omalizumab vs mepolizumab, 0.78 [95% CI = 0.32-1.91]). The differences in the change in FEV1 comparing patients who received the different biologics were as follows: 0.11 L (95% CI = -0.003 to 0.222 L) for dupilumab versus mepolizumab, 0.082 L (95% CI -0.040 to 0.204 L) for dupilumab versus omalizumab, and 0.026 L (95% CI -0.083 to 0.140 L) for omalizumab versus mepolizumab. CONCLUSIONS: Among patients with asthma and eosinophil counts of at least 150 cells/µL and IgE levels of 30 to 700 kU/L, dupilumab was associated with greater improvements in exacerbation and FEV1 value than omalizumab and mepolizumab.

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. To identify relevant information from massive clinical notes, we compiled a list of gender-related keywords through expert curation, literature review, and expansion via a fine-tuned BioWordVec model. This keyword list was used to pre-screen potential TGD individuals and create two datasets for model training, testing, and validation. Dataset I was a balanced dataset that contained clinician-confirmed TGD patients and cases without keywords. Dataset II contained cases with keywords. The performance of the deep learning model was compared to traditional machine learning and rule-based algorithms. RESULTS: The final keyword list consists of 109 keywords, of which 58 (53.2%) were expanded by the BioWordVec model. Dataset I contained 3,150 patients (50% TGD) while Dataset II contained 200 patients (90% TGD). On Dataset I the deep learning model achieved a F1 score of 0.917, sensitivity of 0.854, and a precision of 0.980; and on Dataset II a F1 score of 0.969, sensitivity of 0.967, and precision of 0.972. The deep learning model significantly outperformed rule-based algorithms. CONCLUSION: This is the first study to show that deep learning-integrated NLP algorithms can accurately identify gender identity using EHR data. Future work should leverage and evaluate additional diverse data sources to generate more generalizable algorithms.

PASCLex: A comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon derived from electronic health record clinical notes.

OBJECTIVE: To develop a comprehensive post-acute sequelae of COVID-19 (PASC) symptom lexicon (PASCLex) from clinical notes to support PASC symptom identification and research. METHODS: We identified 26,117 COVID-19 positive patients from the Mass General Brigham's electronic health records (EHR) and extracted 328,879 clinical notes from their post-acute infection period (day 51-110 from first positive COVID-19 test). PASCLex incorporated Unified Medical Language System® (UMLS) Metathesaurus concepts and synonyms based on selected semantic types. The MTERMS natural language processing (NLP) tool was used to automatically extract symptoms from a development dataset. The lexicon was iteratively revised with manual chart review, keyword search, concept consolidation, and evaluation of NLP output. We assessed the comprehensiveness of PASCLex and the NLP performance using a validation dataset and reported the symptom prevalence across the entire corpus. RESULTS: PASCLex included 355 symptoms consolidated from 1520 UMLS concepts of 16,466 synonyms. NLP achieved an averaged precision of 0.94 and an estimated recall of 0.84. Symptoms with the highest frequency included pain (43.1%), anxiety (25.8%), depression (24.0%), fatigue (23.4%), joint pain (21.0%), shortness of breath (20.8%), headache (20.0%), nausea and/or vomiting (19.9%), myalgia (19.0%), and gastroesophageal reflux (18.6%). DISCUSSION AND CONCLUSION: PASC symptoms are diverse. A comprehensive lexicon of PASC symptoms can be derived using an ontology-driven, EHR-guided and NLP-assisted approach. By using unstructured data, this approach may improve identification and analysis of patient symptoms in the EHR, and inform prospective study design, preventative care strategies, and therapeutic interventions for patient care.

Toward an asthma patient-reported outcome measure for use in digital remote monitoring.

OBJECTIVE: To develop and test a patient-reported outcome measure (PROM) for suitability in digital remote asthma symptom monitoring to identify uncontrolled asthma. METHODS: We modified a 5-item PROM that does not require a license, the asthma control measure (ACM), from a one-month to one-week lookback period, and evaluated it using the 5-item asthma control questionnaire (ACQ-5). We recruited subjects with asthma through MTurk, an online platform. RESULTS: In a sample of 498 subjects, the ACM identified uncontrolled asthma with sensitivity 0.99 and specificity 0.65. The two measures correlated with r = 0.81. CONCLUSION: The ACM modified to a weekly lookback period can differentiate patients with well-controlled asthma from those with uncontrolled asthma. This PROM does not require a license and can be used in digital remote monitoring interventions.

Asthma Exacerbations in Patients with Type 2 Diabetes and Asthma on Glucagon-like Peptide-1 Receptor Agonists.

Rationale: GLP-1R (glucagon-like peptide-1 receptor) agonists are approved to treat type 2 diabetes mellitus and obesity. GLP-1R agonists reduce airway inflammation and hyperresponsiveness in preclinical models.Objectives: To compare rates of asthma exacerbations and symptoms between adults with type 2 diabetes and asthma prescribed GLP-1R agonists and those prescribed SGLT-2 (sodium-glucose cotransporter-2) inhibitors, DPP-4 (dipeptidyl peptidase-4) inhibitors, sulfonylureas, or basal insulin for diabetes treatment intensification.Methods: This study was an electronic health records-based new-user, active-comparator, retrospective cohort study of patients with type 2 diabetes and asthma newly prescribed GLP-1R agonists or comparator drugs at an academic healthcare system from January 2000 to March 2018. The primary outcome was asthma exacerbations; the secondary outcome was encounters for asthma symptoms. Propensity scores were calculated for GLP-1R agonist and non-GLP-1R agonist use. Zero-inflated Poisson regression models included adjustment for multiple covariates.Measurements and Main Results: Patients initiating GLP-1R agonists (n = 448), SGLT-2 inhibitors (n = 112), DPP-4 inhibitors (n = 435), sulfonylureas (n = 2,253), or basal insulin (n = 2,692) were identified. At 6 months, asthma exacerbation counts were lower in persons initiating GLP-1R agonists (reference) compared with SGLT-2 inhibitors (incidence rate ratio [IRR], 2.98; 95% confidence interval [CI], 1.30-6.80), DPP-4 inhibitors (IRR, 2.45; 95% CI, 1.54-3.89), sulfonylureas (IRR, 1.83; 95% CI, 1.20-2.77), and basal insulin (IRR, 2.58; 95% CI, 1.72-3.88). Healthcare encounters for asthma symptoms were also lower among GLP-1R agonist users.Conclusions: Adult patients with asthma prescribed GLP-1R agonists for type 2 diabetes had lower counts of asthma exacerbations compared with other drugs initiated for treatment intensification. GLP-1R agonists may represent a novel treatment for asthma associated with metabolic dysfunction.

Presentation and natural history of progestogen hypersensitivity.

OBJECTIVE: To review the published medical literature on the clinical presentation, risk factors, and natural history of hypersensitivity reactions to progestogens. DATA SOURCES: Through the use of PubMed, we conducted a review of allergy, dermatology, and obstetric literature for cases and case series of patients with hypersensitivity reactions to exogenous or endogenous progestogens. There are no longitudinal, prospective studies related to progestogen hypersensitivity. STUDY SELECTIONS: Publications were selected that described cases that were clinically consistent with progesterone hypersensitivity and positive test results or clear symptoms with exposure to progestogens to confirm the diagnosis. RESULTS: Progestogen hypersensitivity symptoms can be triggered by endogenous progesterone or exogenous progestins used for contraception or fertility treatments. Symptoms are varied and include dermatitis, urticaria, asthma, and anaphylaxis. CONCLUSION: Although the medical literature on progestogen hypersensitivity is limited to case reports and small case series, significant heterogeneity exists in clinical presentation among patients.

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

Purpose: 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. Methods: This was a retrospective observational study using 2016-2021 deidentified EHR data from a large health care system. Our sample included adults who had an encounter at any of three hospitals within the health care system or were enrolled in the health care system's Accountable Care Organization. The sex and gender fields of interest were gender identity, sex assigned at birth (SAB), and legal sex. Patient characteristics included demographics, clinical features, and health care utilization. Results: In the final study sample (N = 3,473,123), gender identity, SAB, and legal sex (required for system registration) were missing for 75.4%, 75.8%, and 0.1% of individuals, respectively. Several demographic and clinical factors were associated with having complete gender identity and SAB. Notably, the odds of having complete gender identity and SAB were greater among individuals with an activated patient portal (odds ratio [OR] = 2.68; 95% confidence interval [CI] = 2.66-2.70) and with more outpatient visits (OR = 4.34; 95% CI = 4.29-4.38 for 5+ visits); odds of completeness were lower among those with any urgent care visits (OR = 0.80; 95% CI = 0.78-0.82). Conclusions: Missingness of sex and gender data in the EHR was high and associated with a range of patient factors. Key features associated with completeness highlight multiple opportunities for intervention with a focus on patient portal use, primary care provider reporting, and urgent care settings.

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. Fewer have been studied specifically in the context of patients with asthma. However, increasingly effective pharmacologic options for weight loss highlight the need for allergists and pulmonologists to understand the range of approaches that may directly or indirectly yield clinical benefits in asthma management. Weight loss interventions often require multidisciplinary support to create strategies that can realistically achieve a patient's personalized asthma and weight goals. This includes minimizing the adverse weight effects of glucocorticoids, which remain a mainstay of asthma management. Disparities in access, cost, and insurance coverage of weight loss interventions remain acute challenges for providers and patients. Future studies are needed to elucidate mechanisms of action of specific weight loss interventions on short-term and long-term asthma outcomes.

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. Metadata for all sex and gender fields (Legal Sex, Sex Assigned at Birth [SAAB], Gender Identity) were quantified by completion rates, user types, and longitudinal change. A nested qualitative study of providers from specialties with high and low field use identified themes related to utilization. RESULTS: 1 576 120 patients met inclusion criteria: 100% had a Legal Sex, 20% a Gender Identity, and 19% a SAAB; 321 185 patients had field changes other than initial Legal Sex entry. About 2% of patients had a subsequent Legal Sex change, and 25% of those had ≥2 changes; 20% of patients had ≥1 update to Gender Identity and 19% to SAAB. Excluding the first Legal Sex entry, administrators made most changes (67%) across all fields, followed by patients (25%), providers (7.2%), and automated Health Level-7 (HL7) interface messages (0.7%). Provider utilization varied by subspecialty; themes related to systems barriers and personal perceptions were identified. DISCUSSION: Sex and gender demographic fields are primarily used by administrators and raise concern about data accuracy; provider use is heterogenous and lacking. Provider awareness of field availability and variable workflows may impede use. CONCLUSION: EHR metadata highlights areas for improvement of sex and gender field utilization.

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. METHODS: COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity samples were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. RESULTS: We identified 498 480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18 226. The final dictionary contains 38 175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. CONCLUSIONS: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret, and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.

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. Diagnostic modalities such as progesterone skin testing have low sensitivity and specificity for PH. When exogenous PH is suspected, the allergist should consider a progestogen challenge. Treatment strategies should be tailored for each patient, including symptom-directed therapies, ovulation suppression, and progesterone desensitization. Future studies should explore the mechanisms of PH, validation of diagnostic criteria, and standardization of treatment strategies.