Real-world evaluation of an algorithmic machine-learning-guided testing approach in stable chest pain: a multinational, multicohort study.

Aims: An algorithmic strategy for anatomical vs. functional testing in suspected coronary artery disease (CAD) (Anatomical vs. Stress teSting decIsion Support Tool; ASSIST) is associated with better outcomes than random selection. However, in the real world, this decision is rarely random. We explored the agreement between a provider-driven vs. simulated algorithmic approach to cardiac testing and its association with outcomes across multinational cohorts. Methods and results: In two cohorts of functional vs. anatomical testing in a US hospital health system [Yale; 2013-2023; n = 130 196 (97.0%) vs. n = 4020 (3.0%), respectively], and the UK Biobank [n = 3320 (85.1%) vs. n = 581 (14.9%), respectively], we examined outcomes stratified by agreement between the real-world and ASSIST-recommended strategies. Younger age, female sex, Black race, and diabetes history were independently associated with lower odds of ASSIST-aligned testing. Over a median of 4.9 (interquartile range [IQR]: 2.4-7.1) and 5.4 (IQR: 2.6-8.8) years, referral to the ASSIST-recommended strategy was associated with a lower risk of acute myocardial infarction or death (hazard ratioadjusted: 0.81, 95% confidence interval [CI] 0.77-0.85, P < 0.001 and 0.74 [95% CI 0.60-0.90], P = 0.003, respectively), an effect that remained significant across years, test types, and risk profiles. In post hoc analyses of anatomical-first testing in the Prospective Multicentre Imaging Study for Evaluation of Chest Pain (PROMISE) trial, alignment with ASSIST was independently associated with a 17% and 30% higher risk of detecting CAD in any vessel or the left main artery/proximal left anterior descending coronary artery, respectively. Conclusion: In cohorts where historical practices largely favour functional testing, alignment with an algorithmic approach to cardiac testing defined by ASSIST was associated with a lower risk of adverse outcomes. This highlights the potential utility of a data-driven approach in the diagnostic management of CAD.

A Deep Learning Approach for Automated Extraction of Functional Status and New York Heart Association Class for Heart Failure Patients During Clinical Encounters.

Introduction: Serial functional status assessments are critical to heart failure (HF) management but are often described narratively in documentation, limiting their use in quality improvement or patient selection for clinical trials. We developed and validated a deep learning-based natural language processing (NLP) strategy to extract functional status assessments from unstructured clinical notes. Methods: We identified 26,577 HF patients across outpatient services at Yale New Haven Hospital (YNHH), Greenwich Hospital (GH), and Northeast Medical Group (NMG) (mean age 76.1 years; 52.0% women). We used expert annotated notes from YNHH for model development/internal testing and from GH and NMG for external validation. The primary outcomes were NLP models to detect (a) explicit New York Heart Association (NYHA) classification, (b) HF symptoms during activity or rest, and (c) functional status assessment frequency. Results: Among 3,000 expert-annotated notes, 13.6% mentioned NYHA class, and 26.5% described HF symptoms. The model to detect NYHA classes achieved a class-weighted AUROC of 0.99 (95% CI: 0.98-1.00) at YNHH, 0.98 (0.96-1.00) at NMG, and 0.98 (0.92-1.00) at GH. The activity-related HF symptom model achieved an AUROC of 0.94 (0.89-0.98) at YNHH, 0.94 (0.91-0.97) at NMG, and 0.95 (0.92-0.99) at GH. Deploying the NYHA model among 166,655 unannotated notes from YNHH identified 21,528 (12.9%) with NYHA mentions and 17,642 encounters (10.5%) classifiable into functional status groups based on activity-related symptoms. Conclusions: We developed and validated an NLP approach to extract NYHA classification and activity-related HF symptoms from clinical notes, enhancing the ability to track optimal care and identify trial-eligible patients.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study.

Background: Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods: Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results: Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions: An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD).

Introduction: Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH). Research Methods and Analysis: This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD. Ethics and Dissemination: All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).

Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images.

Background: Risk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. Objectives: To examine an artificial intelligence (AI)-enhanced electrocardiographic (AI-ECG) surrogate for imaging risk biomarkers, and its association with CTRCD. Methods: Across a five-hospital U.S.-based health system (2013-2023), we identified patients with breast cancer or non-Hodgkin lymphoma (NHL) who received anthracyclines (AC) and/or trastuzumab (TZM), and a control cohort receiving immune checkpoint inhibitors (ICI). We deployed a validated AI model of left ventricular systolic dysfunction (LVSD) to ECG images (≥0.1, positive screen) and explored its association with i) global longitudinal strain (GLS) measured within 15 days (n=7,271 pairs); ii) future CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction [LVEF]<50%), and LVEF<40%. In the ICI cohort we correlated baseline AI-ECG-LVSD predictions with downstream myocarditis. Results: Higher AI-ECG LVSD predictions were associated with worse GLS (-18% [IQR:-20 to -17%] for predictions<0.1, to -12% [IQR:-15 to -9%] for ≥0.5 (p<0.001)). In 1,308 patients receiving AC/TZM (age 59 [IQR:49-67] years, 999 [76.4%] women, 80 [IQR:42-115] follow-up months) a positive baseline AI-ECG LVSD screen was associated with ~2-fold and ~4.8-fold increase in the incidence of the composite CTRCD endpoint (adj.HR 2.22 [95%CI:1.63-3.02]), and LVEF<40% (adj.HR 4.76 [95%CI:2.62-8.66]), respectively. Among 2,056 patients receiving ICI (age 65 [IQR:57-73] years, 913 [44.4%] women, follow-up 63 [IQR:28-99] months) AI-ECG predictions were not associated with ICI myocarditis (adj.HR 1.36 [95%CI:0.47-3.93]). Conclusion: AI applied to baseline ECG images can stratify the risk of CTRCD associated with anthracycline or trastuzumab exposure.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs.

Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care.

Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows rely on a computerized ECG interpretation using rule-based tools built into the ECG signal acquisition systems with limited accuracy and flexibility. In low-resource settings, specialists must review every single ECG for such decisions, as these computerized interpretations are not available. Additionally, high-quality interpretations are even more essential in such low-resource settings as there is a higher burden of accuracy for automated reads when access to experts is limited. Artificial Intelligence (AI)-based systems have the prospect of greater accuracy yet are frequently limited to a narrow range of conditions and do not replicate the full diagnostic range. Moreover, these models often require raw signal data, which are unavailable to physicians and necessitate costly technical integrations that are currently limited. To overcome these challenges, we developed and validated a format-independent vision encoder-decoder model - ECG-GPT - that can generate free-text, expert-level diagnosis statements directly from ECG images. The model shows robust performance, validated on 2.6 million ECGs across 6 geographically distinct health settings: (1) 2 large and diverse US health systems- Yale-New Haven and Mount Sinai Health Systems, (2) a consecutive ECG dataset from a central ECG repository from Minas Gerais, Brazil, (3) the prospective cohort study, UK Biobank, (4) a Germany-based, publicly available repository, PTB-XL, and (5) a community hospital in Missouri. The model demonstrated consistently high performance (AUROC≥0.81) across a wide range of rhythm and conduction disorders. This can be easily accessed via a web-based application capable of receiving ECG images and represents a scalable and accessible strategy for generating accurate, expert-level reports from images of ECGs, enabling accurate triage of patients globally, especially in low-resource settings.

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

Background: Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopathy (HCM) on 12-lead ECGs and offers a novel way to monitor treatment response. While the surgical or percutaneous reduction of the interventricular septum (SRT) represented initial HCM therapies, mavacamten offers an oral alternative. Objective: To evaluate biological response to SRT and mavacamten. Methods: We applied an AI-ECG model for HCM detection to ECG images from patients who underwent SRT across three sites: Yale New Haven Health System (YNHHS), Cleveland Clinic Foundation (CCF), and Atlantic Health System (AHS); and to ECG images from patients receiving mavacamten at YNHHS. Results: A total of 70 patients underwent SRT at YNHHS, 100 at CCF, and 145 at AHS. At YNHHS, there was no significant change in the AI-ECG HCM score before versus after SRT (pre-SRT: median 0.55 [IQR 0.24-0.77] vs post-SRT: 0.59 [0.40-0.75]). The AI-ECG HCM scores also did not improve post SRT at CCF (0.61 [0.32-0.79] vs 0.69 [0.52-0.79]) and AHS (0.52 [0.35-0.69] vs 0.61 [0.49-0.70]). Among 36 YNHHS patients on mavacamten therapy, the median AI-ECG score before starting mavacamten was 0.41 (0.22-0.77), which decreased significantly to 0.28 (0.11-0.50, p <0.001 by Wilcoxon signed-rank test) at the end of a median follow-up period of 237 days. Conclusions: The lack of improvement in AI-based HCM score with SRT, in contrast to a significant decrease with mavacamten, suggests the potential role of AI-ECG for serial monitoring of pathophysiological improvement in HCM at the point-of-care using ECG images.

Burden of Ischemic Heart Disease and Its Attributable Risk Factors in North Africa and the Middle East, 1990 to 2019: Results From the GBD Study 2019.

BACKGROUND: The North Africa and Middle East (NAME) region has one of the highest burdens of ischemic heart disease (IHD) worldwide. This study reports the contemporary epidemiology of IHD in NAME. METHODS AND RESULTS: We estimated the incidence, prevalence, deaths, years of life lost, years lived with disability, disability-adjusted life years (DALYs), and premature mortality of IHD, and its attributable risk factors in NAME from 1990 to 2019 using the results of the GBD (Global Burden of Disease study 2019). In 2019, 0.8 million lives and 18.0 million DALYs were lost due to IHD in NAME. From 1990 to 2019, the age-standardized DALY rate of IHD significantly decreased by 33.3%, mostly due to the reduction of years of life lost rather than years lived with disability. In 2019, the proportion of premature death attributable to IHD was higher in NAME compared with global measures: 26.8% versus 16.9% for women and 18.4% versus 14.8% for men, respectively. The age-standardized DALY rate of IHD attributed to metabolic risks, behavioral risks, and environmental/occupational risks significantly decreased by 28.7%, 37.8%, and 36.4%, respectively. Dietary risk factors, high systolic blood pressure, and high low-density lipoprotein cholesterol were the top 3 risks contributing to the IHD burden in most countries of NAME in 2019. CONCLUSIONS: In 2019, IHD was the leading cause of death and lost DALYs in NAME, where premature death due to IHD was greater than the global average. Despite the great reduction in the age-standardized DALYs of IHD in NAME from 1990 to 2019, this region still had the second-highest burden of IHD in 2019 globally.

Use of Smart Devices to Track Cardiovascular Health Goals in the United States.

BACKGROUND: Smartphone-based health applications are increasingly popular, but their real-world use for cardiovascular risk management remains poorly understood. OBJECTIVES: The purpose of this study was to investigate the patterns of tracking health goals using smart devices, including smartphones and/or tablets, in the United States. METHODS: Using the nationally representative Health Information National Trends Survey for 2017 to 2020, we examined self-reported tracking of health-related goals (optimizing body weight, increasing physical activity, and/or quitting smoking) using smart devices among those with cardiovascular disease (CVD) or cardiovascular risk factors of hypertension, diabetes, obesity, and/or smoking. Survey analyses were used to obtain national estimates of use patterns and identify features associated with the use of these devices for tracking health goals. RESULTS: Of 16,092 Health Information National Trends Survey participants, 10,660 had CVD or cardiovascular risk factors, representing 154.2 million (95% CI: 149.2-159.3 million) U.S. adults. Among the general U.S. adult population, 46% (95% CI: 44%-47%) tracked their health goals using their smart devices, compared with 42% (95% CI: 40%-43%) of those with or at risk of CVD. Younger age, female, Black race, higher educational attainment, and greater income were independently associated with tracking of health goals using smart devices. CONCLUSIONS: Two in 5 U.S. adults with or at risk of CVD use their smart devices to track health goals. While representing a potential avenue to improve care, the lower use of smart devices among older and low-income individuals, who are at higher risk of adverse cardiovascular outcomes, requires that digital health interventions are designed so as not to exacerbate existing disparities.

Development and Multinational Validation of a Novel Algorithmic Strategy for High Lp(a) Screening.

Importance: Elevated lipoprotein(a) [Lp(a)] is associated with atherosclerotic cardiovascular disease (ASCVD) and major adverse cardiovascular events (MACE). However, fewer than 0.5% of patients undergo Lp(a) testing, limiting the evaluation and use of novel targeted therapeutics currently under development. Objective: We developed and validated a machine learning model to enable targeted screening for elevated Lp(a). Design: Cross-sectional. Setting: 4 multinational population-based cohorts. Participants: We included 456,815 participants from the UK Biobank (UKB), the largest cohort with protocolized Lp(a) testing for model development. The model's external validity was assessed in Atherosclerosis Risk in Communities (ARIC) (N=14,484), Coronary Artery Risk Development in Young Adults (CARDIA) (N=4,124), and Multi-Ethnic Study of Atherosclerosis (MESA) (N=4,672) cohorts. Exposures: Demographics, medications, diagnoses, procedures, vitals, and laboratory measurements from UKB and linked electronic health records (EHR) were candidate input features to predict high Lp(a). We used the pooled cohort equations (PCE), an ASCVD risk marker, as a comparator to identify elevated Lp(a). Main Outcomes and Measures: The main outcome was elevated Lp(a) (≥150 nmol/L), and the number-needed-to-test (NNT) to find one case with elevated Lp(a). We explored the association of the model's prediction probabilities with all-cause and cardiovascular mortality, and MACE. Results: The Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE) used low-density lipoprotein cholesterol, statin use, triglycerides, high-density lipoprotein cholesterol, history of ASCVD, and anti-hypertensive medication use as input features. ARISE outperformed cardiovascular risk stratification through PCE for predicting elevated Lp(a) with a significantly lower NNT (4.0 versus 8.0 [with or without PCE], P<0.001). ARISE performed comparably across external validation cohorts and subgroups, reducing the NNT by up to 67.3%, depending on the probability threshold. Over a median follow-up of 4.2 years, a high ARISE probability was also associated with a greater hazard of all-cause death and MACE (age/sex-adjusted hazard ratio [aHR], 1.35, and 1.38, respectively, P<0.001), with a greater increase in cardiovascular mortality (aHR, 2.17, P<0.001). Conclusions and Relevance: ARISE optimizes screening for elevated Lp(a) using commonly available clinical features. ARISE can be deployed in EHR and other settings to encourage greater Lp(a) testing and to improve identifying cases eligible for novel targeted therapeutics in trials. KEY POINTS: Question: How can we optimize the identification of individuals with elevated lipoprotein(a) [Lp(a)] who may be eligible for novel targeted therapeutics?Findings: Using 4 multinational population-based cohorts, we developed and validated a machine learning model, Algorithmic Risk Inspection for Screening Elevated Lp(a) (ARISE), to enable targeted screening for elevated Lp(a). In contrast to the pooled cohort equations that do not identify those with elevated Lp(a), ARISE reduces the "number-needed-to-test" to find one case with elevated Lp(a) by up to 67.3%.Meaning: ARISE can be deployed in electronic health records and other settings to enable greater yield of Lp(a) testing, thereby improving the identification of individuals with elevated Lp(a).

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Risk of incident cardiovascular diseases at national and subnational levels in Iran from 2000 to 2016 and projection through 2030: Insights from Iran STEPS surveys.

BACKGROUND: Cardiovascular Disease (CVD) is the leading cause of death in developing countries. CVD risk stratification guides the health policy to make evidence-based decisions. AIM: To provide current picture and future trend of CVD risk in the adult Iranian population. METHODS: Nationally representative datasets of 2005, 2006, 2007, 2008, 2009, 2011, and 2016 STEPwise approach to non-communicable diseases risk factor surveillance (STEPS) studies were used to generate the 10-year and 30-year risks of CVD based on Framingham, Globorisk, and World Health Organization (WHO) risk estimation models. Trend of CVD risk was calculated from 2000 until 2016 and projected to 2030. RESULTS: In 2016, based on Framingham model, 14.0% of the Iranian, aged 30 to 74, were at great risk (≥20%) of CVD in the next 10 years (8.0% among females, 20.7% among males). Among those aged 25 to 59, 12.7% had ≥45% risk of CVD in the coming 30 years (9.2% among females, 16.6 among males). In 2016, CVD risk was higher among urban area inhabitants. Age-standardized Framingham 10-year CVD risk will increase 32.2% and 19%, from 2000 to 2030, in females and males, respectively. Eastern provinces had the lowest and northern provinces had the greatest risk. CONCLUSIONS: This study projected that CVD risk has increased from 2000 to 2016 in Iran. Without further risk factor modification, this trend will continue until 2030. We have identified populations at higher risks of CVD to guide future intervention.

Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.

Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020.

Importance: Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities. Objective: To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020. Design, Setting, and Participants: This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022. Exposures: Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking). Main Outcomes and Measures: Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey). Results: Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care. Conclusions and Relevance: Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

Global, Regional, and National Quality of Care Index (QCI) of Gastric Cancer: A Systematic Analysis for the Global Burden of Disease Study 1990-2017.

BACKGROUND: Gastric Cancer (GC)is the third leading cause of cancer death worldwide. We aimed to compare the quality of care of GC at global, regional, and national levels from 1990 to 2017 in different age, sex, and socio-demographic groups using the quality-of-care index. MATERIAL: METHOD: We used Mortality to Incidence Ratio, DALY to Prevalence Ratio, YLL to YLD Ratio, and Prevalence to Incidence Ratio, that all indicate the quality of care. Then, using Principal Component Analysis (PCA), these values are combined. A new index called QCI (Quality of Care Index), which indicates quality, is introduced to compare the quality of care in different countries in 1990 and 2017. Scores were calculated and scaled 0-100, with higher scores indicating better status. RESULTS: The global QCI of GC in 1990 and 2017 was 35.7 and 66.7, respectively. The QCI index is 89.6 and 16.4 in high and low SDI countries, respectively. In 2017, Japan had the highest QCI with a 100 score. Japan was followed by South Korea, Singapore, Australia, and the United States with 99.5, 98.4, 98.3, and 90.0. On the other hand, the Central African Republic, Eritrea, Papua New Guinea, Lesotho, and Afghanistan with 11.6, 13.0, 13.1, 13.5, and 13.7 had the worst QCI, respectively. CONCLUSION: The quality of care of GC has increased worldwide from 1990 to 2017. Also, higher SDI was associated with more quality of care. We recommend conducting more screening and therapeutic programs for early detection and to improve gastric cancer treatment in developing countries.

Persistence on Novel Cardioprotective Antihyperglycemic Therapies in the United States.

Selected glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is) have cardioprotective effects in patients with type 2 diabetes mellitus (T2D) and elevated cardiovascular risk. Prescription and consistent use of these medications are essential to realizing their benefits. In a nationwide deidentified United States administrative claims database of adults with T2D, the prescription practices of GLP-1RAs and SGLT-2i were evaluated across guideline-directed co-morbidity indications from 2018 to 2020. The monthly fill rates were assessed for 12 months after the initiation of therapy by calculating the proportion of days with consistent medication use. Of 587,657 subjects with T2D, 80,196 (13.6%) were prescribed GLP-1RAs and 68,149 (11.5%) SGLT-2i from 2018 to 2020, representing 12.9% and 11.6% of patients with indications for each medication, respectively. In new initiators, 1-year fill rate was 52.5% for GLP-1RAs and 52.9% for SGLT-2i, which was higher for patients with commercial insurance than those with Medicare Advantage plans for both GLP-1RAs (59.3% vs 51.0%, p <0.001) and SGLT-2i (63.4% vs 50.3%, p <0.001). After adjusting for co-morbidities, there were higher rates of prescription fills for patients with commercial insurance (odds ratio 1.17, 95% confidence interval 1.06 to 1.29 for GLP-1RAs, and 1.59 [1.42 to 1.77] for SGLT-2i); and higher income (odds ratio 1.09 [1.06 to 1.12] for GLP-1RAs, and 1.06 [1.03 to 1.1] for SGLT-2i). From 2018 to 2020, the use of GLP-1RAs and SGLT-2i remained limited to fewer than 1 in 8 patients with T2D and indications, with 1-year fill rates around 50%. The low and inconsistent use of these medications compromises their longitudinal health outcome benefits in a period of expanding indications for their use.

Pharmacologic prevention of recurrent vasovagal syncope: A systematic review and network meta-analysis of randomized controlled trials.

Vasovagal syncope (VVS) is a transient loss of consciousness that currently imposes a high burden on health care systems with limited evidence of the comparative efficacy of available pharmacologic interventions. This study aims to compare all pharmacologic therapies suggested in randomized controlled trials (RCTs) through systematic review and network meta-analysis. A systematic search in PubMed, Embase, Web of Science, and Cochrane Library was conducted to identify RCTs evaluating pharmacologic therapies for patients with VVS. The primary outcome was spontaneous VVS recurrence. The secondary outcome was a positive head-up tilt test (HUTT) after receiving intervention, regarded as a lower level of evidence. Pooled risk ratio (RR) with 95% confidence interval (CI) was calculated using random-effect network meta-analysis. Pairwise meta-analysis for comparison with placebo was also performed when applicable. The surface under the cumulative ranking curve analysis was conducted to rank the treatments for each outcome. Twenty-eight studies with 1744 patients allocated to different medications or placebo were included. Network meta-analysis of the reduction in the primary outcome showed efficacy for midodrine (RR 0.55; 95% CI 0.35-0.85) and fluoxetine (especially in patients with concomitant anxiety) (RR 0.36; 95% CI 0.16-0.84). In addition, midodrine and atomoxetine were superior to other treatment options, considering positive HUTT (RR 0.37; 95% CI 0.23-0.59; and RR 0.49; 95% CI 0.28-0.86, respectively). Overall, midodrine was the only agent shown to reduce spontaneous syncopal events. Fluoxetine also seems to be beneficial but should be studied further in RCTs. Our network meta-analysis did not find evidence of the efficacy of any other medication.

Clinical Associations of Injuries Caused by Vasovagal Syncope: A Cohort Study From a Tertiary Syncope Unit.

Background Recent research has revealed that vasovagal syncope (VVS) leads to a high incidence of injuries; however, clinical associations of injury are not well-established. We present data from an ongoing VVS cohort and aimed to determine characteristics associated with VVS-related injury. Methods and Results Between 2017 and 2020, consecutive patients ≥18 years of age presenting to a tertiary syncope unit and diagnosed with VVS were included. Clinical characteristics relevant to syncope were obtained for the index episode. The outcome was incidence of injury during VVS, documented by clinical evaluation at the syncope clinic. Among 1115 patients (mean age, 45.9 years; 48% women), 260 injuries (23%) occurred. History of VVS-related injuries (adjusted relative risk [aRR], 1.80 [95% CI, 1.42-2.29]), standing position (aRR, 1.34 [95% CI, 1.06-1.68]), and female sex (aRR, 1.30 [95% CI, 1.06-1.60]) were associated with injury, whereas recurrent VVS (aRR, 0.63 [95% CI, 0.49-0.81]) and syncope in the noon/afternoon (aRR, 0.70 [95% CI, 0.56-0.87]) and evening/night (aRR, 0.43 [95% CI, 0.33-0.57]) compared with morning hours were associated with lower risk. There was a trend for higher rates of injury with overweight/obesity (aRR, 1.23 [95% CI, 0.99-1.54]) and syncope occurring at home (aRR, 1.22 [95% CI, 0.98-1.51]). In a per-syncope analysis considering up to 3 previous episodes (n=2518, 36% traumatic), syncope at home (aRR, 1.33 [95% CI, 1.17-1.51]) and absence of prodromes (aRR, 1.34 [95% CI, 1.09-1.61]) were associated with injury. Conclusions Patient characteristics, VVS presentations, the circumstances, and surroundings can determine the risk of injury. These associations of VVS-related injury identify at-risk individuals and high-risk situations. Future prospective studies are needed to investigate potential strategies for prevention of post-VVS injury in recurrent cases.

Novel DNMT3B Mutation in a Patient with Immunodeficiency, Centromeric Instability, and Facial Anomalies (ICF) Syndrome and a Bronchopulmonary Collateral Artery.

BACKGROUND: Immunodeficiency, centromeric instability, and facial anomalies (ICF) syndrome is a rare autosomal recessive disorder. ICF1 is caused by bi-allelic mutations in the gene encoding deoxyribonucleic acid methyltransferase-3B (DNMT3B). Herein, we report a novel homozygous DNMT3B mutation in a patient with ICF1. CASE PRESENTATION: An eight-month-old Iranian Caucasian infant of consanguineous 1st-degree cousins presented to our clinic for evaluation of neutropenia. Physical examination was unremarkable except for low-set ears and a systolic cardiac murmur. He had a history of recurrent respiratory infections and oral thrush. Moreover, a collateral artery between the bronchial and pulmonary arteries was observed on the angiogram, mimicking a patent ductus arteriosus on the echocardiogram. Growth percentiles were normal; however, he had a neurodevelopmental delay. Family history was significant for a sibling who deceased at nine months of age after recurrent respiratory infections. Laboratory evaluation revealed a normal white blood cell count with neutropenia and normal bone marrow studies. He had hypogammaglobinemia with normal flow cytometric studies and was treated with prophylactic trimethoprim-sulfamethoxazole and itraconazole. After that, he was re-admitted three times due to recurrent episodes of pneumonia and an episode of pseudomonas aeruginosa meningitis. Currently, he is five years old and doing well on monthly intravenous immunoglobulin. Due to recurrent infections, hypogammaglobulinemia, and neutropenia, as well as a family history of consanguinity and a sibling who deceased during infancy, a primary immune deficiency was suspected. Genetic studies utilizing whole-exome sequencing demonstrated a homozygous missense mutation in DNMT3B (LRG_56t1:c.2008C>T; p.Arg670Trp) in the patient studied. The mutation has not been previously reported. CONCLUSION: We describe a novel homozygous DNMT3B mutation in an Iranian boy with ICF1. It is associated with recurrent infections, hypogammaglobinemia, neutropenia, mild facial anomalies, and a bronchopulmonary collateral artery.