Hypertension is a preventable risk factor for cardiovascular disease and mortality yet is often undiagnosed and uncontrolled. Federally Qualified Health Centers (FQHC) serve vulnerable, understudied populations having characteristics that are associated with greater risk for undiagnosed and uncontrolled hypertension. This study aimed to determine the burden and risk factors associated with undiagnosed and uncontrolled hypertension at a large FQHC, with the co-primary outcomes of yearly rates of undiagnosed and uncontrolled hypertension. The design was a retrospective cross-sectional study of adult patients seen at an FQHC between 2019–2023. Undiagnosed hypertension was defined as an elevated average blood pressure reading ( ≥ 130/80) over at least two encounters within the same year. Hypertension was defined according to the 2025 ACC/AHA guidelines, ≥130/80. Crude and age-adjusted rates for the co-primary outcomes were calculated and adjusted mixed-effects logistic regression identified risk factors. From 2019 to 2023, the yearly percent of age-adjusted undiagnosed hypertension ranged from 28.7–34.7

Mitral valve prolapse (MVP) affects 1–3% of the general population and is largely considered benign. However, a small subset of MVP patients develops complex ventricular arrhythmias (VAs) and sudden cardiac death, including patients with nonsignificant mitral regurgitation (MR). This subgroup of arrhythmic MVP (aMVP) is variably associated with features such as mitral annular disjunction, bileaflet myxomatous prolapse, and regional myocardial fibrosis, with arrhythmic risk disproportionate to hemodynamic burden. The EHRA defines aMVP as MVP with complex VAs (frequent premature ventricular contractions, non-sustained or sustained ventricular tachycardia, ventricular fibrillation, or aborted sudden cardiac arrest) in the absence of another defined arrhythmic substrate. This narrative review reframes aMVP as a regional, stretch-induced cardiomyopathy with a distinct fibro-inflammatory signature. We further characterize a two-hit pathogenesis in which abnormal valvular mechanics create regional myocardial stress, while patient-specific vulnerability shapes variable fibro-inflammatory, fibrotic, and electromechanical responses. This narrative review synthesizes contemporary evidence across multimodal cardiac imaging, electrophysiology, surgical outcomes, mechanotransduction biology, and pharmacology, emphasizing a modifiable fibro-inflammatory trajectory underlying the intermediate risk aMVP phenotype and supporting a clinical shift from device-based rescue to substrate-directed prevention. Expanding on this model, we evaluate substrate-modifying pharmacotherapies for mechanistic fit, human cardiac evidence, and trial feasibility. Among these, mineralocorticoid receptor antagonists (MRAs) and sodium–glucose cotransporter 2 (SGLT2) inhibitors demonstrate the strongest convergence of antifibrotic potential and trial readiness.

People with HIV (PWH), particularly women, have a high cardiovascular disease (CVD) burden compared to the general population. There is little evidence describing statin adherence among PWH, which could inform interventions to reduce CVD disparities. Observational cohort of privately insured PWH under age 65 who initiated statin therapy during 2015–2022 in MarketScan data. We used outpatient pharmacy claims to examine (1) statin discontinuation, defined as a gap > 90 days, and (2) proportion of days covered (PDC) by a statin in 90-day intervals. We estimated hazard ratios (HRs) using Cox models to compare discontinuation rates and prevalence ratios (PRs) from log-binomial regression to compare the probability of having low adherence (PDC < 80 We included 9522 PWH who initiated a statin (median age 52 years, 17.3

BACKGROUND:People with HIV experience conventional and HIV-specific risk factors for increased blood pressure and may have different trajectories than people without HIV. Using data from the Multicenter AIDS Cohort Study (MACS) and Women's Interagency HIV Study (WIHS), we describe longitudinal patterns in blood pressure, hypertension, and vital status for people with HIV and without HIV. METHODS:We estimated longitudinal trajectories of systolic and diastolic blood pressure, pulse pressure, and mean arterial pressure using generalized estimating equations. Using multinomial logistic regression and Kaplan-Meier curves, we estimated the proportion of participants in four states corresponding to vital and hypertensive status. RESULTS:We included men and women with HIV who reported antiretroviral therapy use (MACS: n  = 1555; WIHS: n  = 2765) and men and women without HIV (MACS: n  = 1671; WIHS: n  = 1145) between ages 20 and 70 from 1998 to 2019. Trajectory shapes were similar between people with and without HIV within cohorts. Men with and without HIV had similar blood pressure across ages. Women with HIV had lower blood pressure than those without HIV (average systolic difference -4.7 mmHg; 95% CI: -5.6, -3.8). Despite comparable average time alive without hypertension, people with HIV experienced higher mortality than those without HIV (risk at age 50, MACS: 13.1% vs. 8.1%; WIHS 33.3% vs. 9.6%). CONCLUSION:Blood pressure trajectories were similar between people with and without HIV, although blood pressure was slightly lower for women with HIV. High mortality among people with HIV (vs. without) may have resulted in a lower proportion of people with hypertension at older ages.

BackgroundDespite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion. ObjectiveWe aimed to estimate the proportion of AI/ML research that reported results through ClinicalTrials.gov or peer-reviewed publications indexed in PubMed or Scopus. MethodsUsing data from the Clinical Trials Transformation Initiative Aggregate Analysis of ClinicalTrials.gov, we identified studies initiated and completed between January 2010 and December 2023 that contained AI/ML-specific terms in the official title, brief summary, interventions, conditions, detailed descriptions, primary outcomes, or keywords. For 842 completed studies, we searched PubMed and Scopus for publications containing study identifiers and AI/ML-specific terms in relevant fields, such as the title, abstract, and keywords. We calculated disclosure rates within 3 years of study completion and median times to disclosure—from the “primary completion date” to the “results first posted date” on ClinicalTrials.gov or the earliest date of journal publication. ResultsOf 842 completed studies (n=357 interventional; n=485 observational), 5.5% (46/842) disclosed results on ClinicalTrials.gov, 13.9% (117/842) in journal publications, and 17.7% (149/842) through either route within 3 years of completion. Higher disclosure rates were observed for trials: 10.4% (37/357) on ClinicalTrials.gov, 19.3% (69/357) in journal publications, and 26.1% (93/357) through either route. Randomized controlled trials had even higher disclosure rates: 11.3% (23/203) on ClinicalTrials.gov, 24.6% (50/203) in journal publications, and 32% (65/203) through either route. Nevertheless, most study findings (82.3%; 693/842) remained undisclosed 3 years after study completion. Trials using randomization (vs nonrandomized) or masking (vs open label) had higher disclosure rates and shorter times to disclosure. Most trials (85%; 305/357) had sample sizes of ≤1000, yet larger trials (n>1000) had higher publication rates (30.8%; 16/52) than smaller trials (n≤1000) (17.4%; 53/305). Hospitals (12.4%; 42/340), academia (15.1%; 39/259), and industry (13.7%; 20/146) published the most. High-income countries accounted for 82.4% (89/108) of all published studies. Of studies with disclosed results, the median times to report through ClinicalTrials.gov and in journal publications were 505 days (IQR 399-676) and 407 days (IQR 257-674), respectively. Open-label trials were common (60%; 214/357). Single-center designs were prevalent in both trials (83.3%; 290/348) and observational studies (82.3%; 377/458). ConclusionsFor over 80% of AI/ML studies completed during 2010-2023, study findings remained undisclosed even 3 years after study completion, raising questions about the representativeness of publicly available evidence. While methodological rigor was generally associated with higher publication rates, the predominance of single-center designs and high-income countries may limit the generalizability of the results currently accessible.

Background and Aims:Improving care of patients with hyperlipidemia requires an understanding of the barriers physicians perceive in prescribing low-density lipoprotein cholesterol (LDL-C)-lowering therapies. This study explores physicians' perceptions of time and resource burdens, identify perceived patient adherence barriers, and examine factors influencing physicians' decision-making in LDL-C management. Methods:This is a non-interventional, cross-sectional, online survey of US-based primary care practitioners (PCP) and cardiologists who recommended or provided lipid-lowering therapy (LLT) to ≥50 adults per month, practiced for ≥2 years, and completed the survey in English. The survey comprised multiple-choice, constant sum, and numerical questions about physician decision-making, patient management, and perceptions of patient attitudes/behaviors regarding LDL-C management. Descriptive univariate analyses were conducted. Results:200 PCPs and 200 cardiologists completed the survey. Most physicians reported prescribing lipid-lowering therapy (LLT) and that patients declined injectable proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i). They attributed this refusal to cost/insurance, fear/discomfort taking injections, and a preference for oral therapies. Physicians viewed patients with a history of ASCVD, with LLT experience, and those with greater understanding of ASCVD risk to have higher LLT adherence compared to those without. Most physicians spent a median of 10 min in shared decision-making conversations, regardless of therapies they prescribed. They reported needing longer to instruct patients during adherence counseling for PCSK9is than for oral therapies. Conclusions:Our findings suggest patient, clinician, and system barriers may all hinder LDL-C management and adherence. A greater understanding of the association between perceived barriers and real-world behaviors will help optimize lipid management.

PURPOSE:To describe 2-year post-myocardial infarction (MI) longitudinal patterns of guideline- directed medical therapy (GDMT) and cardiac rehabilitation (CR) participation with Sankey diagrams. METHODS:Eligible Medicare beneficiaries were aged 66 to 95 years with an acute MI (International Classification of Diseases-9-CM discharge codes of 410.xx excluding 410.x2) hospital admission between January 1, 2014 and September 30, 2015 and ≥1 follow-up CR sessions. We defined GDMT (angiotensin converting enzyme-inhibitor or angiotensin receptor blocker, statin, and β-blocker) use as having at least a 21-day supply available during a 30-day window. We stratified CR participation by days with claims (1-11, 12-23, ≥ 24). Population level trends of 6 GDMT combinations, CR participation, and death were depicted with Sankey diagrams. RESULTS:Study population consisted of 5793 beneficiaries, 72% of whom had ≥1 GDMT pre-MI, 93% had ≥1 GDMT at baseline, and 45% initiated CR by 30 days post-MI. A median 23% of CR participants did not flow from low to moderate CR participation each month. At 1-year post-MI, 37% of beneficiaries without pre-MI GDMT and 33% of beneficiaries with pre-MI GDMT concluded CR early. Between 9% and 16% of beneficiaries without pre-MI GDMT and 2% to 6% beneficiaries with pre-MI GDMT did not have a GDMT fill post-MI. On average, 4% to 5% of beneficiaries switched from β-blocker + statin to another GDMT group post-MI each month. CONCLUSIONS:Describing patterns of secondary prevention method utilization with Sankey diagrams can identify intervention populations, such as groups with inconsistent CR participation, primary nonadherence to new medications, and volatile medication persistence.

BACKGROUND:Mitral annular disjunction (MAD) is associated with ventricular arrhythmia in mitral valve prolapse (MVP). The proportional risk from MAD and other predictors of ventricular arrhythmia in MVP has not been well characterized. OBJECTIVE:This study aimed to identify predictors of complex or frequent ventricular ectopy (cfVE) in MVP and to quantify risk of cfVE and mortality in MVP with MAD. METHODS:We studied 632 adult patients with MVP on transthoracic echocardiography at the University of North Carolina Medical Center from 2016 to 2019 (median age, 64 [interquartile range, 52-74] years; 52.7% female; 16.3% African American). Resting and ambulatory electrocardiograms were used to identify cfVE. RESULTS:MAD was present in 94 (14.9%) patients. Independent associations of MAD were bileaflet prolapse (odds ratio [95% CI], 4.25 [2.47-7.33]; P < .0001), myxomatous valve (2.17 [1.27-3.71]; P = .005), absence of hypertension (2.00 [1.21-3.32]; P = .007), electrocardiogram inferior or lateral lead T-wave inversion (2.07 [1.23-3.48]; P = .006), and female sex (1.99 [1.21-3.25]; P = .006). cfVE was frequent with MAD (39 [41.5%] vs 93 [17.3%] without; P < .0001). Independent cfVE predictors were MAD (hazard ratio [95% CI], 2.23 [1.47-3.36]; P = .0001), bileaflet prolapse (1.86 [1.25-2.76]; P = .002), heart failure (1.79 [1.16-2.77]; P = .009), lower left ventricular ejection fraction (0.14 [0.03-0.61]; P = .009), coronary artery disease (1.60 [1.05-2.43]; P = .03), and inferior or lateral lead T-wave inversion (1.51 [1.03-2.22]; P = .03). After a median of 40 (33-48) months, there was increased mortality with MAD (P = .04). CONCLUSION:MAD in MVP is associated with bileaflet or myxomatous MVP, absence of hypertension, T-wave inversion, and female sex. There is increased cfVE and mortality with MAD, highlighting the need for closer follow-up of these patients.

BackgroundThe rapid growth of research in artificial intelligence (AI) and machine learning (ML) continues. However, it is unclear whether this growth reflects an increase in desirable study attributes or merely perpetuates the same issues previously raised in the literature. ObjectiveThis study aims to evaluate temporal trends in AI/ML studies over time and identify variations that are not apparent from aggregated totals at a single point in time. MethodsWe identified AI/ML studies registered on ClinicalTrials.gov with start dates between January 1, 2010, and December 31, 2023. Studies were included if AI/ML-specific terms appeared in the official title, detailed description, brief summary, intervention, primary outcome, or sponsors’ keywords. Studies registered as systematic reviews and meta-analyses were excluded. We reported trends in AI/ML studies over time, along with study characteristics that were fast-growing and those that remained unchanged during 2010-2023. ResultsOf 3106 AI/ML studies, only 7.6% (n=235) were regulated by the US Food and Drug Administration. The most common study characteristics were randomized (56.2%; 670/1193; interventional) and prospective (58.9%; 1126/1913; observational) designs; a focus on diagnosis (28.2%; 335/1190) and treatment (24.4%; 290/1190); hospital/clinic (44.2%; 1373/3106) or academic (28%; 869/3106) sponsorship; and neoplasm (12.9%; 420/3245), nervous system (12.2%; 395/3245), cardiovascular (11.1%; 356/3245) or pathological conditions (10%; 325/3245; multiple counts per study possible). Enrollment data were skewed to the right: maximum 13,977,257; mean 16,962 (SD 288,155); median 255 (IQR 80-1000). The most common size category was 101-1000 (44.8%; 1372/3061; excluding withdrawn or missing), but large studies (n>1000) represented 24.1% (738/3061) of all studies: 29% (551/1898) of observational studies and 16.1% (187/1163) of trials. Study locations were predominantly in high-income countries (75.3%; 2340/3106), followed by upper-middle-income (21.7%; 675/3106), lower-middle-income (2.8%; 88/3106), and low-income countries (0.1%; 3/3106). The fastest-growing characteristics over time were high-income countries (location); Europe, Asia, and North America (location); diagnosis and treatment (primary purpose); hospital/clinic and academia (lead sponsor); randomized and prospective designs; and the 1-100 and 101-1000 size categories. Only 5.6% (47/842) of completed studies had results available on ClinicalTrials.gov, and this pattern persisted. Over time, there was an increase in not only the number of newly initiated studies, but also the number of completed studies without posted results. ConclusionsMuch of the rapid growth in AI/ML studies comes from high-income countries in high-resource settings, albeit with a modest increase in upper-middle-income countries (mostly China). Lower-middle-income or low-income countries remain poorly represented. The increase in randomized or prospective designs, along with 738 large studies (n>1000), mostly ongoing, may indicate that enough studies are shifting from an in silico evaluation stage toward a prospective comparative evaluation stage. However, the ongoing limited availability of basic results on ClinicalTrials.gov contrasts with this field’s rapid advancements and the public registry’s role in reducing publication and outcome reporting biases.

Introduction: Evidence on drivers of sudden death (SD) is needed to understand its persistently high incidence and inform prevention interventions. SD studies have been hampered by data limitations and restrictive SD criteria. We conducted a population-based study of all-cause, out-of-hospital SD using Emergency Medical Services (EMS) data from Wake County, a large North Carolina county with >1 million residents. Methods: We screened EMS records of out-of-hospital deaths between 2013-2015 among persons aged 18-64 years, collected medical and death records, and adjudicated 399 SD cases according to a standardized protocol. Deaths occurring in hospices or nursing homes were excluded, as were those due to unnatural death or cancer. Deaths were not excluded by time last seen or coronary artery disease (CAD) criteria. Frequency matched living controls were identified from the same county and time. We compiled and summarized findings based on peer-reviewed papers and published abstracts in Table 1. Results: Cardiovascular disease risk factors were common among decedents, including hypertension (60.4%, 244 of 371), though only 14.8% (55 of 371) had documented CAD. SD cases had lower adjusted mean total cholesterol, low-density lipoprotein and high-density lipoprotein levels compared to controls. Furthermore, left ventricular hypertrophy and inflammation suggested nodes on a common causal pathway. Poor health care management was frequent as well as mental health and substance use problems. Environmental factors such as poverty, air pollution and absence of nearby greenways were associated with SD. Conclusions: Conducting a population-based registry of adjudicated SD cases was feasible and provided high quality data for study. Our findings suggest that SD is not only tied to CAD but a syndrome with potentially different environmental and inflammatory etiologies. Future studies should investigate modifiable risk factors for interventions to prevent SD.

BackgroundSudden death accounts for approximately 10% of deaths among working-age adults and is associated with poor air quality. Objectives: To identify high-risk groups and potential modifiers and mediators of risk, we explored previously established associations between fine particulate matter (PM2.5) and sudden death stratified by potential risk factors.MethodsSudden death victims in Wake County, NC, from 1 March 2013 to 28 February 2015 were identified by screening Emergency Medical Systems reports and adjudicated (n = 399). Daily PM2.5 concentrations for Wake County from the Air Quality Data Mart were linked to event and control periods. Potential modifiers included greenspace metrics, clinical conditions, left ventricular hypertrophy (LVH), and neutrophil-to-lymphocyte ratio (NLR). Using a case-crossover design, conditional logistic regression estimated the OR (95%CI) for sudden death for a 5 μg/m3 increase in PM2.5 with a 1-day lag, adjusted for temperature and humidity, across risk factor strata.ResultsIndividuals having LVH or an NLR above 2.5 had PM2.5 associations of greater magnitude than those without [with LVH OR: 1.90 (1.04, 3.50); NLR > 2.5: 1.25 (0.89, 1.76)]. PM2.5 was generally less impactful for individuals living in areas with higher levels of greenspace.ConclusionLVH and inflammation may be the final step in the causal pathway whereby poor air quality and traditional risk factors trigger arrhythmia or myocardial ischemia and sudden death. The combination of statistical evidence with clinical knowledge can inform medical providers of underlying risks for their patients generally, while our findings here may help guide interventions to mitigate the incidence of sudden death.