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Abstract 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.

Therapeutic Area: Other: Artificial intelligence; Misinformation Background: Misinformation regarding CVD prevention is prevalent on the internet and on social media. Chat-based artificial intelligence (AI) models such as ChatGPT have gained over 100 million users, are publicly accessible, and may provide appropriate information for simple CVD prevention topics. Whether these public AI models may propagate misinformation regarding CVD prevention is uncertain. Methods: This study was performed in March 2024 using the subscription-based version of GPT-4 (OpenAI, USA). Prompts regarding six CVD prevention topics (statin therapy and muscle-side effects, dementia, and liver disease; fish oil; supplements; and low-density lipoprotein-cholesterol and heart disease) were posed. Prompts were framed in two tones: a neutral tone and a misinformation-prompting tone. The misinformation-prompting tone requested specific arguments and scientific references to support misinformation. Each tone and topic was prompted in a different chatbot instance. Each response was reviewed by a board-certified cardiologist specializing in preventive cardiology at a tertiary care center. If a response had multiple bullet-points with individual scientific references, each bullet-point was graded separately. Responses were graded as appropriate (accurate content and references), borderline (minor inaccuracies or references published >20 years ago), or inappropriate (inaccurate content and/or references, including non-existent references). Results: For the six prompts posed with a neutral tone, all responses lacked scientific references and were graded as appropriate (100%). For all six prompts posed with a misinformation-prompting tone, each response consisted of multiple discrete bullet-points with a scientific reference for each individual point. Of 31 bullet-points across the six topics obtained using a misinformation-prompting tone, 32.2% (10/31) were graded as appropriate, 19.4% (6/31) were graded as borderline, and 48.4% (15/31) were graded as inappropriate. Conclusions: In this exploratory study, GPT-4 – a popular and publicly accessible chat-based AI model – was easily prompted to support CVD prevention misinformation. Misinformation-supporting arguments and scientific references were inappropriate due to inaccurate content and/or references nearly 50% of the time. Robust research efforts and policies are needed to study and prevent AI-enabled propagation of misinformation regarding CVD prevention.

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.

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.

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

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.

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.

Therapeutic Area: ASCVD/CVD Risk Factors Background: Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Despite practice guidelines recommending routine Lp(a) testing, uptake of screening has been low and variable. We sought to understand differences in Lp(a) testing and levels by race, ethnicity, and ASCVD risk across a large, integrated healthcare system. Methods: We conducted a retrospective cohort study of patients from a large California healthcare 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. Baseline variables were extracted prior to Lp(a) testing. Race/ethnicity was self-reported and stratified as follows: Non-Hispanic White, Non-Hispanic Black, Hispanic (Mexican, Puerto Rican, Other), Non-Hispanic Asian (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, Other). T-tests were used to test differences among groups for normally distributed data, Wilcoxon tests for skewed data, and chi-square test for categorical data. Results: Among 1,484,410 patients who met the inclusion criteria, 13,689 (0.9%) underwent Lp(a) testing—mean age 54.6±13.8y, 49% female, 59% Non-Hispanic (NH) White. Average Lp(a) values differed significantly by race and ethnicity ranging from 55mg/dL ±63.3 among NH-Asian participants to 99.8mg/dL ±95.3 for NH-Black individuals. 37.3% of tested patients had Lp(a) levels above 50mg/dL with significant variation among subgroups, ranging from 30.7% of Mexican patients to 62.6% of NH Black patients. Estimated ASCVD risk and clinical ASCVD prevalence varied among tested patients. Asian Indian individuals had the lowest overall risk and NH-Black individuals had the highest overall risk (Panel A). There was an increasing trend of elevated Lp(a) by ASCVD risk—40.6% of those with ≥20% 10-Year ASCVD risk and 43.1% of those with a history of ASCVD across had Lp(a) ≥50mg/dL, compared with 36.2% of those with

Sarraju, Coquet et al. utilise a natural language processing approach to identify reasons for statin nonuse in the health records of patients with atherosclerotic cardiovascular disease (ASCVD). Their approach identifies patient-level and clinician-level reasons for statin nonuse, with differences by ASCVD type and patient race/ethnicity.

Abstract Hispanic populations generally experience more adverse socioeconomic conditions yet demonstrate lower mortality compared with Non-Hispanic White (NHW) populations in the US. This finding of a mortality advantage is well-described as the “Hispanic paradox.” The Coronavirus Disease 2019 (COVID-19) pandemic has disproportionately affected Hispanic populations. To quantify these effects, we evaluated US national and county-level trends in Hispanic versus NHW mortality from 2011 through 2020. We found that a previously steady Hispanic mortality advantage significantly decreased in 2020, potentially driven by COVID-19-attributable Hispanic mortality. Nearly 16% of US counties experienced a reversal of their pre-pandemic Hispanic mortality advantage such that their Hispanic mortality exceeded NHW mortality in 2020. An additional 50% experienced a decrease in a pre-pandemic Hispanic mortality advantage. Our work provides a quantitative understanding of the disproportionate burden of the pandemic on Hispanic health and the Hispanic paradox and provides a renewed impetus to tackle the factors driving these concerning disparities.

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

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.

Abstract The Coronavirus Disease 2019 (COVID-19) pandemic curtailed clinical trial activity. Decentralized clinical trials (DCTs) can expand trial access and reduce exposure risk but their feasibility remains uncertain. We evaluated DCT feasibility for atrial fibrillation (AF) patients on oral anticoagulation (OAC). DeTAP (Decentralized Trial in Afib Patients, NCT04471623) was a 6-month, single-arm, 100% virtual study of 100 AF patients on OAC aged >55 years, recruited traditionally and through social media. Participants enrolled and participated virtually using a mobile application and remote blood pressure (BP) and six-lead electrocardiogram (ECG) sensors. Four engagement-based primary endpoints included changes in pre- versus end-of-study OAC adherence (OACA), and % completion of televisits, surveys, and ECG and BP measurements. Secondary endpoints included survey-based nuisance bleeding and patient feedback. 100 subjects (mean age 70 years, 44% women, 90% White) were recruited in 28 days (traditional: 6 pts; social media: 94 pts in 12 days with >300 waitlisted). Study engagement was high: 91% televisits, 85% surveys, and 99% ECG and 99% BP measurement completion. OACA was unchanged at 6 months (baseline: 97 ± 9%, 6 months: 96 ± 15%, p = 0.39). In patients with low baseline OACA (

Abstract Statin therapy is the cornerstone of preventing atherosclerotic cardiovascular disease (ASCVD), primarily by reducing low density lipoprotein cholesterol (LDL-C) levels. Optimal statin therapy decisions rely on shared decision making and may be uncertain for a given patient. In areas of clinical uncertainty, personalized approaches based on real-world data may help inform treatment decisions. We sought to develop a personalized statin recommendation approach for primary ASCVD prevention based on historical real-world outcomes in similar patients. Our retrospective cohort included adults from a large Northern California electronic health record (EHR) aged 40–79 years with no prior cardiovascular disease or statin use. The cohort was split into training and test sets. Weighted-K-nearest-neighbor (wKNN) regression models were used to identify historical EHR patients similar to a candidate patient. We modeled four statin decisions for each patient: none, low-intensity, moderate-intensity, and high-intensity. For each candidate patient, the algorithm recommended the statin decision that was associated with the greatest percentage reduction in LDL-C after 1 year in similar patients. The overall cohort consisted of 50,576 patients (age 54.6 ± 9.8 years) with 55% female, 48% non-Hispanic White, 32% Asian, and 7.4% Hispanic patients. Among 8383 test-set patients, 52%, 44%, and 4% were recommended high-, moderate-, and low-intensity statins, respectively, for a maximum predicted average 1-yr LDL-C reduction of 16.9%, 20.4%, and 14.9%, in each group, respectively. Overall, using aggregate EHR data, a personalized statin recommendation approach identified the statin intensity associated with the greatest LDL-C reduction in historical patients similar to a candidate patient. Recommendations included low- or moderate-intensity statins for maximum LDL-C lowering in nearly half the test set, which is discordant with their expected guideline-based efficacy. A data-driven personalized statin recommendation approach may inform shared decision making in areas of uncertainty, and highlight unexpected efficacy-effectiveness gaps.

IntroductionDiverse 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.MethodsWe 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.ResultsFor 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).ConclusionWe 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.

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). [ABSTRACT FROM AUTHOR]

Introduction: The COVID‐19 pandemic disrupted clinical research. CLEAR Outcomes investigated the effect of bempedoic acid (BA) versus placebo in 13 970 patients with statin intolerance and high cardiovascular (CV) risk. BA reduced the risk of the primary endpoint (composite of CV death, nonfatal myocardial infarction, nonfatal stroke, or coronary revascularization) by 13%. CLEAR Outcomes began before and continued for 2.7 years after the start of the pandemic. Methods: The impact of the COVID‐19 pandemic on patient disposition, adverse events, and major adverse CV events (MACE) in CLEAR Outcomes was assessed. Results: Rates of severe infection, hospitalization, or first MACE associated with a positive COVID‐19 test were low and balanced between treatment groups. Rates of all‐cause death, non‐CV death, and undetermined death increased in the pandemic period compared with the pre‐pandemic period, while rates of CV death with a known etiology remained stable. A sensitivity analysis excluding undetermined deaths occurring after the onset of the pandemic from the CV death designation yielded hazard ratios of 0.84 (95% CI, 0.76–0.93) for the primary endpoint and 0.94 (95% CI, 0.76–1.16) for the secondary endpoint of CV death, compared with 0.87 (95% CI, 0.79–0.96) and 1.04 (95% CI, 0.88–1.24), respectively, in the original analysis. Conclusion: The CLEAR Outcomes trial continued uninterrupted throughout the COVID‐19 pandemic. Certain trial endpoints may have been impacted by the pandemic. Specifically, the classification of undetermined deaths as CV deaths may have attenuated the effect of BA on key efficacy endpoints. [ABSTRACT FROM AUTHOR]

This article discusses a study that examines the use of aspirin for cardiovascular disease prevention among adults in the United States. The study found that the use of aspirin for primary prevention has declined, especially among adults aged 60 and older. However, the use of aspirin for secondary prevention remained stable. The study suggests that physicians should discuss the risks and benefits of aspirin use with their patients to reduce inappropriate use for primary prevention in older adults. [Extracted from the article]