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.

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.

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.

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.

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.

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.

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. Study team members conducted monthly sessions using a structured guide to identify recruitment barriers and facilitators. The proportion of participants who reported being recruited by a portal or nonportal strategy was measured as our outcomes. We used Fisher's exact test to compare outcomes by equity variable, and multivariable logistic regression to control for each covariate and adjust effect size estimates. Using grounded theory, we coded and extracted themes regarding recruitment barriers and facilitators. RESULTS: The majority (84.4%) of patients who met study inclusion criteria were patient portal enrollees. Of 6,366 eligible patients who were approached, 627 completed the eligibility questionnaire and were less frequently Hispanic, less frequently Spanish-speaking, and more frequently patient portal enrollees. Of 445 patients who consented to participate, 241 (54.2%) reported completing the eligibility questionnaire after being contacted by a patient portal message. In adjusted analysis, only race (odds ratio [OR]: 0.46, 95% confidence interval [CI]: 0.28-0.77, p = 0.003) and college education (OR: 0.60, 95% CI: 0.39-0.91, p = 0.016) remained significant. Key recruitment barriers included technology issues (e.g., lack of email access) and facilitators included bilingual study staff, Spanish-language recruitment materials, targeted phone calls, and clinician-initiated "1-click" referrals. CONCLUSION: A primarily digital strategy to recruit patients into a digital health trial is unlikely to achieve equitable participation, even in a population overrepresented by patient portal enrollees. Nondigital recruitment methods that address racial and educational disparities and less active portal enrollees are necessary to ensure equity in clinical trial enrollment.

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.

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. METHODS: This retrospective, observational cohort study included COVID-19-positive patients between March 4, 2020, and January 20, 2021, with up to 180 days of health care follow-up in a health care system in the Northeastern United States. Respiratory symptoms recorded in clinical notes from days 28 to 180 after COVID-19 diagnosis were extracted using natural language processing. Cohorts were stratified by hospitalization status during the acute COVID-19 period. Univariable and multivariable analyses were used to compare symptoms among patients with and without asthma adjusting for demographic and clinical confounders. RESULTS: Among 31,084 eligible patients with COVID-19, 2863 (9.2%) had hospitalization during the acute COVID-19 period; 4049 (13.0%) had a history of asthma, accounting for 13.8% of hospitalized and 12.9% of nonhospitalized patients. In the post-acute COVID-19 period, patients with asthma had significantly higher risk of shortness of breath, cough, bronchospasm, and wheezing than patients without an asthma history. Incident respiratory symptoms of bronchospasm and wheezing were also higher in patients with asthma. Patients with asthma who had not been hospitalized during acute COVID-19 had additionally higher risk of cough, abnormal breathing, sputum changes, and a wider range of incident respiratory symptoms. CONCLUSION: Patients with asthma may have an under-recognized burden of respiratory symptoms after COVID-19 warranting increased awareness and monitoring in this population.

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. In the past decade, new research has emerged assessing whether hypoglycemic therapies impact comorbid asthma as reflected by the incidence of asthma, asthma-related emergency department visits, asthma-related hospitalizations, and asthma-related exacerbations. The purpose of this review article is to discuss the mechanism of action, preclinical data, and existing clinical studies regarding the efficacy and safety of hypoglycemic therapies for adults with obesity and comorbid 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. Odds ratios (ORs) and incidence rate ratios for clinical characteristics associated with ADRs, including immediate hypersensitivity reactions, were calculated using multivariable logistic regression. RESULTS: Among 59,763 patients exposed to Mrgprx2-activating drugs, 4846 had a listed ADR. Female sex, White race, asthma (OR: 1.81, 95% confidence interval [CI]: 1.68-1.94), chronic urticaria (OR: 1.73, 95% CI: 1.46-2.05), and mastocytosis (OR: 12.79, 95% CI: 5.98-27.02) were associated with increased odds of a reaction. Overall, patients with allergic disease had 1.21 times the rate of an ADR compared with patients without allergic disease. Elevated absolute eosinophil count was inversely associated with reactions, and there was no association with elevated total IgE. Observed associations were similar in a patient subgroup with immediate-type hypersensitivity reactions. CONCLUSION: Specific allergic diseases and common allergic biomarkers are differentially associated with ADRs to Mrgprx2-activating drugs. These findings from a large, "real world" drug-exposed population highlight clinical factors that may contribute to non-IgE-mediated drug allergy.

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.

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. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Improving Allergy Documentation: A Retrospective Electronic Health Record System-Wide Patient Safety Initiative.

OBJECTIVES: Documentation of allergies in a coded, non-free-text format in the electronic health record (EHR) triggers clinical decision support to prevent adverse events. Health system-wide patient safety initiatives to improve EHR allergy documentation by specifically decreasing free-text allergy entries have not been reported. The goal of this initiative was to systematically reduce free-text allergen entries in the EHR allergy module. METHODS: We assessed free-text allergy entries in a commercial EHR used at a multihospital integrated health care system in the greater Boston area. Using both manual and automated methods, a multidisciplinary consensus group prioritized high-risk and frequently used free-text allergens for conversion to coded entries, added new allergen entries, and deleted duplicate allergen entries. Environmental allergies were moved to the patient problem list. RESULTS: We identified 242,330 free-text entries, which included a variety of environmental allergies (42%), food allergies (18%), contrast media allergies (13%), "no known allergy" (12%), drug allergies (2%), and "no contrast allergy" (2%). Most free-text entries were entered by medical assistants in ambulatory settings (34%) and registered nurses in perioperative settings (20%). We remediated a total of 52,206 free-text entries with automated methods and 79,578 free-text entries with manual methods. CONCLUSIONS: Through this multidisciplinary intervention, we identified and remediated 131,784 free-text entries in our EHR to improve clinical decision support and patient safety. Additional strategies are required to completely eliminate free-text allergy entry, and establish systematic, consistent, and safe guidelines for documenting allergies.