Using large language models to assess public perceptions around glucagon-like peptide-1 receptor agonists on social media.

BACKGROUND: The prevalence of obesity has been increasing worldwide, with substantial implications for public health. Obesity is independently associated with cardiovascular morbidity and mortality and is estimated to cost the health system over $200 billion dollars annually. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as a practice-changing therapy for weight loss and cardiovascular risk reduction independent of diabetes. METHODS: We used large language models to augment our previously reported artificial intelligence-enabled topic modeling pipeline to analyze over 390,000 unique GLP-1 RA-related Reddit discussions. RESULTS: We find high interest around GLP-1 RAs, with a total of 168 topics and 33 groups focused on the GLP-1 RA experience with weight loss, comparison of side effects between differing GLP-1 RAs and alternate therapies, issues with GLP-1 RA access and supply, and the positive psychological benefits of GLP-1 RAs and associated weight loss. Notably, public sentiment in these discussions was mostly neutral-to-positive. CONCLUSIONS: These findings have important implications for monitoring new side effects not captured in randomized control trials and understanding the public health challenge of drug shortages.

Obesity is a global public health burden that increases heart disease risk. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are a class of medications originally developed for diabetes but are now used to improve lifespans in those with heart disease and increase weight loss. To better understand how the public views this type of drug, over 390,000 discussions from the social media platform Reddit were analyzed using computer software. Topics of discussion included experiences with weight loss, side effects of different GLP-1 RAs, and concerns about drug access and supply. The results showed a mainly neutral-to-positive view of these medications. The findings may help identify new side effects not previously seen in clinical trials and highlight future directions for research and public health efforts.

Lipoprotein(a) Levels in Disaggregated Racial and Ethnic Subgroups Across Atherosclerotic Cardiovascular Disease Risk Levels.

Background: Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Objectives: The authors assessed differences in Lp(a) testing and levels by disaggregated race, ethnicity, and ASCVD risk. Methods: This was a retrospective cohort study of patients from a large California health care system from 2010 to 2021. Eligible individuals were ≥18 years old, with ≥2 primary care visits, and complete race and ethnicity data who underwent Lp(a) testing. Race and ethnicity were self-reported and categorized as follows: non-Hispanic (NH) White, NH-Black, Hispanic (Mexican, Puerto Rican, other), NH-Asian (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other). Logistic regression models tested associations between elevated Lp(a) (≥50 mg/dL) and race, ethnicity, and ASCVD risk. Results: 13,689 (0.9%) individuals underwent Lp(a) testing with a mean age of 54.6 ± 13.8 years, 49% female, 28.8% NH Asian. Over one-third of those tested had Lp(a) levels ≥50 mg/dL, ranging from 30.7% of Mexican patients to 62.6% of NH-Black patients. The ASCVD risk of those tested varied by race: 73.6% of Asian Indian individuals had <5% 10-year risk, whereas 27.2% of NH-Black had established ASCVD. Lp(a) prevalence ≥50 mg/dL increased across the ASCVD risk spectrum. After adjustment, Hispanic (OR: 0.76 [95% CI: 0.66-0.88]) and Asian (OR: 0.88 [95% CI: 0.81-0.96]) had lower odds of Lp(a) ≥50 mg/dL, whereas Black individuals had higher odds (OR: 2.46 [95% CI: 1.97-3.07]). Conclusions: Lp(a) testing is performed infrequently. Of those tested, Lp(a) levels were frequently elevated and differed significantly across disaggregated race and ethnicity groups. The prevalence of elevated Lp(a) increased with increasing ASCVD risk, with significant variation by race and ethnicity.

Effects of canagliflozin on total heart failure events across the kidney function spectrum: Participant-level pooled analysis from the CANVAS Program and CREDENCE trial.

AIMS: People with type 2 diabetes (T2D) face high risks of heart failure (HF) hospitalizations that are often recurrent, especially as kidney function declines. We examined the effects of canagliflozin on total HF events by baseline kidney function in patients with T2D at high cardiovascular risk and/or with chronic kidney disease. METHODS AND RESULTS: Leveraging pooled participant-level data from the CANVAS programme (n = 10 142) and CREDENCE trial (n = 4401), first and total HF hospitalizations were examined. Cox proportional hazards models were built for the time to first HF hospitalization, and proportional means models based on cumulative mean functions were used for recurrent HF hospitalizations. Treatment effects were evaluated overall as well as within baseline estimated glomerular filtration rate (eGFR) strata (<45, 45-60, and >60 ml/min/1.73 m2). HF hospitalizations were independently and blindly adjudicated. Among 14 540 participants with available baseline eGFR values, 672 HF hospitalizations occurred over a median follow-up of 2.5 years. Among participants who experienced a HF hospitalization, 357 had a single event (201 in placebo-treated patients and 156 in canagliflozin-treated patients), 77 had 2 events, and 39 had >2 events. Canagliflozin reduced risk of first HF hospitalization (hazard ratio 0.58, 95% confidence interval [CI] 0.48-0.70) consistently across baseline eGFR strata (pinteraction = 0.84). Canagliflozin reduced total HF hospitalizations overall (mean event ratio 0.63, 95% CI 0.54-0.73) and across eGFR subgroups (pinteraction = 0.51). Canagliflozin also reduced cardiovascular death and total HF hospitalizations (mean event ratio 0.72, 95% CI 0.65-0.80) and across eGFR subgroups (pinteraction = 0.82). The absolute risk reductions were numerically larger, and numbers needed to treat were smaller when evaluating total events versus first events alone. These observed HF benefits were highly consistent across the range of eGFR, with larger absolute benefits in participants who had worse kidney function at baseline. CONCLUSIONS: In individuals with T2D at high cardiovascular risk and/or with chronic kidney disease, canagliflozin reduced the total burden of HF hospitalizations, with consistent benefits observed across the kidney function spectrum. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: CANVAS (NCT01032629), CANVAS-R (NCT01989754), CREDENCE (NCT02065791).

Evaluation of Prompts to Simplify Cardiovascular Disease Information Generated Using a Large Language Model: Cross-Sectional Study.

In this cross-sectional study, we evaluated the completeness, readability, and syntactic complexity of cardiovascular disease prevention information produced by GPT-4 in response to 4 kinds of prompts.

Patterns and gaps in guideline-directed statin use for atherosclerotic cardiovascular disease by race and ethnicity.

Objective: There remain disparities by race and ethnicity in atherosclerotic cardiovascular disease (ASCVD). Statins reduce low-density lipoprotein cholesterol (LDL-c) and improve ASCVD outcomes. ASCVD treatment patterns across disaggregated race and ethnicity groups are incompletely understood. We aimed to evaluate statin use and LDL-c control for ASCVD by race and ethnicity. Methods: From an electronic health record (EHR)-based cohort from a multisite Northern California health system, we included adults with an ASCVD diagnosis from 2010 to 2021 and at least 2 primary care visits, stratified by race and ethnicity (Non-Hispanic White [NHW], Non-Hispanic Black [Black], Hispanic, and Asian). Hispanic (Mexican, Puerto Rican, Other) and Asian (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other) groups were disaggregated. Primary outcomes were 1-year post-ASCVD statin use (prescription) and LDL-c control (at least one value <70 mg/dL). Adjusted odds ratios (ORs) were estimated using logistic regression. Results: Of 133,158 patients, there were 89,944 NHW, 6,294 Black, 12,478 (9.4 %) Hispanic and 13,179 (9.9 %) Asian patients. At 1 year after incident ASCVD, there was suboptimal statin use (any statins <60 %, high-intensity <25 %) and LDL-c control (<30 %) across groups, with lowest proportions in Black patients for statin use (46.7 %, any statin) and LDL-c control (10.7 %, OR 0.89 (0.81-0.97), referent NHW). Disaggregation of Asian and Hispanic groups unmasked within-group heterogeneity. Conclusions: In patients with incident ASCVD, we describe suboptimal and heterogenous 1-year post-ASCVD guideline-directed statin use and 1-year post-ASCVD LDL-c control across disaggregated race and ethnicity groups. Findings may improve understanding of ASCVD treatment disparities and guide implementation.

Underrepresentation of Women in Reduced Ejection Heart Failure Clinical Trials With Improved Mortality or Hospitalization.

BACKGROUND: There are established sex-specific differences in heart failure with reduced ejection fraction (HFrEF) outcomes. Randomized clinical trials (RCTs) based on cardiovascular outcome benefits, typically either reduced cardiovascular mortality or hospitalization for heart failure (HHF), influence current guidelines for therapy. OBJECTIVES: The authors evaluate the representation of women in HFrEF RCTs that observed reduced all-cause or cardiovascular mortality or HHF. METHODS: We queried Cumulative Index to Nursing and Allied Health Literature, Excerpta Medica dataBASE, Medical Literature Analysis and Retrieval System Online, and PubMed for HFrEF RCTs that reported a statistically significant benefit of intervention resulting in improved mortality or HHF published from 1980 to 2021. We estimated representation using the participation-to-prevalence ratio (PPR). A PPR of 0.8 to 1.2 was considered representative. RESULTS: The final analysis included 33 RCTs. Women represented only 23.2% of all enrolled participants (n = 24,366/104,972), ranging from 11.4% to 40.1% per trial. Overall PPR was 0.58, with per-trial PPR estimates ranging from 0.29 to 1.00. Only 5 trials (15.2%) had a PPR of women representative of the disease population. Representation did not change significantly over time. The proportion of women in North American trials was significantly greater than trials conducted in Europe (P = 0.03). The proportion of women was greater in industry trials compared to government-funded trials (P = 0.05). CONCLUSIONS: Women are underrepresented in HFrEF RCTs that have demonstrated mortality or HHF benefits and influence current guidelines. Representation is key to further delineation of sex-specific differences in major trial results. Sustained efforts are warranted to ensure equitable and appropriate inclusion of women in HFrEF trials.

Atherosclerotic plaque stabilization and regression: a review of clinical evidence.

Atherosclerotic plaque results from a complex interplay between lipid deposition, inflammatory changes, cell migration and arterial wall injury. Over the past two decades, clinical trials utilizing invasive arterial imaging modalities, such as intravascular ultrasonography, have shown that reducing levels of atherogenic lipoproteins, mainly serum LDL-cholesterol (LDL-C), to very low levels can safely reduce overall atherosclerotic plaque burden and favourably modify plaque composition. Classically, this outcome has been achieved with intensive statin therapy. Since 2016, newer and potent lipid-lowering strategies, such as proprotein convertase subtilisin-kexin type 9 inhibition, have shown incremental effects on plaque regression and risk of clinical events. Despite maximal reduction in plasma LDL-C levels, considerable residual cardiovascular risk remains in some patients. Therefore, there is a need to study therapeutic approaches that address residual risk beyond LDL-C reduction to promote plaque stabilization or regression. Contemporary imaging modalities, such as coronary computed tomography angiography, enable non-invasive assessment of the overall atherosclerotic plaque burden as well as of certain local plaque characteristics. This technology could allow further study of plaque stabilization and regression using novel therapeutic approaches. Non-invasive plaque assessment might also offer the potential to guide personalized management strategies if validated for this purpose.

Design and Implementation of an Electronic Health Record-Integrated Hypertension Management Application.

BACKGROUND: High blood pressure affects approximately 116 million adults in the United States. It is the leading risk factor for death and disability across the world. Unfortunately, over the past decade, hypertension control rates have decreased across the United States. Prediction models and clinical studies have shown that reducing clinician inertia alone is sufficient to reach the target of ≥80% blood pressure control. Digital health tools containing evidence-based algorithms that are able to reduce clinician inertia are a good fit for turning the tide in blood pressure control, but careful consideration should be taken in the design process to integrate digital health interventions into the clinical workflow. METHODS: We describe the development of a provider-facing hypertension management platform. We enumerate key steps of the development process, including needs finding, clinical workflow analysis, treatment algorithm creation, platform design and electronic health record integration. We interviewed and surveyed 5 Stanford clinicians from primary care, cardiology, and their clinical care team members (including nurses, advanced practice providers, medical assistants) to identify needs and break down the steps of clinician workflow analysis. The application design and development stage were aided by a team of approximately 15 specialists in the fields of primary care, hypertension, bioinformatics, and software development. CONCLUSIONS: Digital monitoring holds immense potential for revolutionizing chronic disease management. Our team developed a hypertension management platform at an academic medical center to address some of the top barriers to adoption and achieving clinical outcomes. The frameworks and processes described in this article may be used for the development of a diverse range of digital health tools in the cardiovascular space.

Readability and reliability of online patient education materials about statins.

Objective: Statins are the cornerstone for the prevention and treatment of cardiovascular disease. Patients often consult online patient education materials (OPEMs) to inform medical decision-making. We therefore aimed to assess the readability and reliability of OPEMs related to statins. Methods: A total of 17 statin-related terms were queried using an online search engine to identify the top 20 search results for each statin-related term. Each OPEM was then grouped into the following categories based on 2 independent reviewers: government OPEMs (national, state, or local government agencies); healthcare/nonprofit OPEMs (major health systems and nonprofit organizations with a specific cardiovascular health focus); industry/commercial OPEMs (pharmaceutical manufacturers and online pharmacies); lay press OPEMs (healthcare-oriented news organizations); and dictionary/encyclopedia OPEMs. Grade-level readability for each OPEM was calculated using 5 standard readability metrics and compared with AMA-recommended readability recommendations. Reliability of each OPEM was evaluated using the JAMA benchmark criteria for online health information and certification from Health on the Net (HONCode). Results: A total of 340 websites were identified across the 17 statin search terms. There were 211 statin OPEMs after excluding non-OPEM results; 172 OPEMs had unique content. Statin OPEM readability exceeded the recommended 6th grade AMA reading level (average reading grade level of 10.9). The average JAMA benchmark criteria score was 2.13 (on a scale of 0-4, with higher scores indicating higher reliability), and only 60% of statin OPEMs were HONCode-certified. There was an inverse association between readability and reliability. The most readable results were from industry and commercial sources, while the most reliable sites were from lay press sources. Conclusions: Statin OPEMs are written at an overall averaging reading grade level of 10.9. There was an inverse association between readability and reliability. Lack of accessible, high-quality online health information may contribute to statin nonadherence.

Natural language processing to identify reasons for sex disparity in statin prescriptions.

Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity. Methods: Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets. Results: There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p=0.003). Conclusions: Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD.

The leaky pipeline of diverse race and ethnicity representation in academic science and technology training in the United States, 2003-2019.

INTRODUCTION: Diverse race and ethnicity representation remains lacking in science and technology (S&T) careers in the United States (US). Due to systematic barriers across S&T training stages, there may be sequential loss of diverse representation leading to low representation, often conceptualized as a leaky pipeline. We aimed to quantify the contemporary leaky pipeline of S&T training in the US. METHODS: We analyzed US S&T degree data, stratified by sex and then by race or ethnicity, obtained from survey data the National Science Foundation and the National Center for Science and Engineering Statistics. We assessed changes in race and ethnicity representation in 2019 at two major S&T transition points: bachelor to doctorate degrees (2003-2019) and doctorate degrees to postdoctoral positions (2010-2019). We quantified representation changes at each point as the ratio of representation in the later stage to earlier stage (representation ratio [RR]). We assessed secular trends in the representation ratio through univariate linear regression. RESULTS: For 2019, the survey data included for bachelor degrees, 12,714,921 men and 10.612,879 women; for doctorate degrees 14,259 men and 12,860 women; and for postdoctoral data, 11,361 men and 8.672 women. In 2019, we observed that Black, Asian, and Hispanic women had comparable loss of representation among women in the bachelor to doctorate transition (RR 0.86, 95% confidence interval [CI] 0.81-0.92; RR 0.85, 95% CI 0.81-0.89; and RR 0.82, 95% CI 0.77-0.87, respectively), while among men, Black and Asian men had the greatest loss of representation (Black men RR 0.72, 95% CI 0.66-0.78; Asian men RR 0.73, 95% CI 0.70-0.77)]. We observed that Black men (RR 0.60, 95% CI 0.51-0.69) and Black women (RR 0.56, 95% CI 0.49-0.63) experienced the greatest loss of representation among men and women, respectively, in the doctorate to postdoctoral transition. Black women had a statistically significant decrease in their representation ratio in the doctorate to postdoctoral transition from 2010 to 2019 (p-trend = 0.02). CONCLUSION: We quantified diverse race and ethnicity representation in contemporary US S&T training and found that Black men and women experienced the most consistent loss in representation across the S&T training pipeline. Findings should spur efforts to mitigate the structural racism and systemic barriers underpinning such disparities.

Artificial Intelligence-Enabled Analysis of Statin-Related Topics and Sentiments on Social Media.

Importance: Despite compelling evidence that statins are safe, are generally well tolerated, and reduce cardiovascular events, statins are underused even in patients with the highest risk. Social media may provide contemporary insights into public perceptions about statins. Objective: To characterize and classify public perceptions about statins that were gleaned from more than a decade of statin-related discussions on Reddit, a widely used social media platform. Design, Setting, and Participants: This qualitative study analyzed all statin-related discussions on the social media platform that were dated between January 1, 2009, and July 12, 2022. Statin- and cholesterol-focused communities, were identified to create a list of statin-related discussions. An artificial intelligence (AI) pipeline was developed to cluster these discussions into specific topics and overarching thematic groups. The pipeline consisted of a semisupervised natural language processing model (BERT [Bidirectional Encoder Representations from Transformers]), a dimensionality reduction technique, and a clustering algorithm. The sentiment for each discussion was labeled as positive, neutral, or negative using a pretrained BERT model. Exposures: Statin-related posts and comments containing the terms statin and cholesterol. Main Outcomes and Measures: Statin-related topics and thematic groups. Results: A total of 10 233 unique statin-related discussions (961 posts and 9272 comments) from 5188 unique authors were identified. The number of statin-related discussions increased by a mean (SD) of 32.9% (41.1%) per year. A total of 100 discussion topics were identified and were classified into 6 overarching thematic groups: (1) ketogenic diets, diabetes, supplements, and statins; (2) statin adverse effects; (3) statin hesitancy; (4) clinical trial appraisals; (5) pharmaceutical industry bias and statins; and (6) red yeast rice and statins. The sentiment analysis revealed that most discussions had a neutral (66.6%) or negative (30.8%) sentiment. Conclusions and Relevance: Results of this study demonstrated the potential of an AI approach to analyze large, contemporary, publicly available social media data and generate insights into public perceptions about statins. This information may help guide strategies for addressing barriers to statin use and adherence.

Identifying Reasons for Statin Nonuse in Patients With Diabetes Using Deep Learning of Electronic Health Records.

Background Statins are guideline-recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high-risk population. Identifying reasons for statin nonuse, which are typically described in unstructured electronic health record data, can inform targeted system interventions to improve statin use. We aimed to leverage a deep learning approach to identify reasons for statin nonuse in patients with diabetes. Methods and Results Adults with diabetes and no statin prescriptions were identified from a multiethnic, multisite Northern California electronic health record cohort from 2014 to 2020. We used a benchmark deep learning natural language processing approach (Clinical Bidirectional Encoder Representations from Transformers) to identify statin nonuse and reasons for statin nonuse from unstructured electronic health record data. Performance was evaluated against expert clinician review from manual annotation of clinical notes and compared with other natural language processing approaches. Of 33 461 patients with diabetes (mean age 59±15 years, 49% women, 36% White patients, 24% Asian patients, and 15% Hispanic patients), 47% (15 580) had no statin prescriptions. From unstructured data, Clinical Bidirectional Encoder Representations from Transformers accurately identified statin nonuse (area under receiver operating characteristic curve [AUC] 0.99 [0.98-1.0]) and key patient (eg, side effects/contraindications), clinician (eg, guideline-discordant practice), and system reasons (eg, clinical inertia) for statin nonuse (AUC 0.90 [0.86-0.93]) and outperformed other natural language processing approaches. Reasons for nonuse varied by clinical and demographic characteristics, including race and ethnicity. Conclusions A deep learning algorithm identified statin nonuse and actionable reasons for statin nonuse in patients with diabetes. Findings may enable targeted interventions to improve guideline-directed statin use and be scaled to other evidence-based therapies.

Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model.

This study examines the appropriateness of artificial intelligence model responses to fundamental cardiovascular disease prevention questions.