Compression stockings for treating vasovagal syncope (COMFORTS-II) trial: Rationale and design of a triple-blind, multi-center, randomized controlled trial.

BACKGROUND: Reduced venous return is an important trigger of vasovagal syncope (VVS). Elastic compression stockings (ECS) can modify venous return and be of therapeutic interest; however, evidence for ECS efficacy in VVS is scarce. This randomized controlled trial was designed to address the issue. METHODS: COMFORTS-II is a multicenter, triple-blind, parallel design, randomized controlled trial aimed to assess the efficacy of ECS in preventing VVS recurrences. Using central online randomization, 268 participants will be allocated to 2 arms (1:1 ratio), wearing intervention ECS (25-30 mm Hg pressure) or sham ECS (≤10 mm Hg pressure). All participants will receive standard VVS treatment in the form of education, and lifestyle modification recommendations (drinking 2-3 l/d of fluids and consuming 10 g/d-roughly half a tablespoon-of table salt). Adherence to ECS treatment will be evaluated through diary sheets, and compared between study arms. Follow-up continues for 1 year, and is conducted via a 24/7 phone line available to patients and trimonthly visits. The co-primary outcomes are proportion of participants with any syncopal recurrence and time to first syncopal episode. Secondary outcomes include frequency of VVS spells, time intervals between recurrences, and incidence of any patient-reported adverse effects. CONCLUSION: To the best of our knowledge, COMFORTS-II is the first clinical trial to assess ECS efficacy among patients with VVS, addressing an important gap in evidence for VVS treatments.

Global and regional burden and quality of care of non-rheumatic valvular heart diseases: a systematic analysis of Global Burden of Disease 1990-2017.

BACKGROUND: With an increase in the incidence and prevalence of non-rheumatic valvular heart diseases (NRVHDs), having a proper understanding of the disease current status in terms of quality of care and healthcare access can considerably affect further planning for the healthcare system. OBJECTIVE: In this study, we aimed to evaluate and compare the quality and equity of care concerning NRVHDs in terms of gender and sociodemographic index (SDI) using a newly proposed index. METHODS: We obtained the primary measures (e.g. incidence) from the Global Burden of Disease (GBD) data about NRVHD from 1990 to 2017 to calculate the subsequent secondary indices (e.g. mortality-to-incidence ratio) with close association to quality of care. Then, using principal component analysis (PCA), quality of care index (QCI) was calculated as a novel index from the secondary indices, rescaled to 0-100. QCI was calculated for all age groups and both genders, globally, regionally and nationally between 1990 and 2017. RESULTS: Globally, the QCI for NRVHDs in 2017 was 87.3, and it appears that gender inequity was unremarkable (gender disparity ratio = 1.00, female QCI: 90.2, male QCI: 89.7) in 2017 similar to the past three decades. Among WHO world regions, the Western Pacific Region and Eastern Mediterranean Region showed the highest (90.1) and lowest (74.0) QCI scores. Regarding SDI, the high-middle-SDI quintile with a QCI of 89.4 and the low-SDI quintile with a QCI of 77.8 were the two extremes of healthcare quality in 2017. CONCLUSION: Although global status regarding the NRVHD's quality of care is acceptable, higher attention is required for lower SDI countries.

Comparison of Outcomes with Midodrine and Fludrocortisone for Objective Recurrence in Treating Syncope (COMFORTS trial): Rationale and design for a multi-center randomized controlled trial.

BACKGROUND: The cornerstone of the treatment of vasovagal syncope (VVS) is lifestyle modifications; however, some patients incur life-disturbing attacks despite compliance with these treatments which underscores the importance of pharmacological interventions. METHODS: In this open-label multi-center randomized controlled trial, we are going to randomize 1375 patients with VVS who had ≥2 syncopal episodes in the last year into three parallel arms with a 2:2:1 ratio to receive midodrine, fludrocortisone, or no medication. All patients will be recommended to drink 2 to 3 liters of fluids per day, consume 10 grams of NaCl per day, and practice counter-pressure maneuvers. In medication arms, patients will start on 5 mg of midodrine TDS or 0.05 mg of fludrocortisone BD. After one week the dosage will be up-titrated to midodrine 30 mg/day and fludrocortisone 0.2 mg/day. Patient tolerance will be the principal guide to dosage adjustments. We will follow-up the patients on 3, 6, 9, and 12 months after randomization. The primary outcome is the time to first syncopal episode. Secondary outcomes include the recurrence rate of VVS, time interval between first and second episodes, changes in quality of life (QoL), and major and minor adverse drug reactions. QoL will be examined by the 36-Item Short Form Survey questionnaire at enrollment and 12 months after randomization. CONCLUSION: The COMFORTS trial is the first study that aims to make a head-to-head comparison between midodrine and fludrocortisone, against a background of lifestyle modifications for preventing recurrences of VVS and improving QoL in patients with VVS.

Prevalence and trends of coronary artery disease risk factors and their effect on age of diagnosis in patients with established coronary artery disease: Tehran Heart Center (2005-2015).

BACKGROUND: Coronary artery disease (CAD) is a universal public health challenge, more prominently so in the low- and middle-income countries. In this study, we aimed to determine prevalence and trends of CAD risk factors in patients with documented CAD and to determine their effects on the age of CAD diagnosis. MATERIALS AND METHODS: We conducted a registry-based, serial cross-sectional study using the coronary angiography data bank of the Tehran Heart Center. Adult patients who had obstructive (> 50% stenosis) CAD were included in the study. The prevalence and 11-year trends of conventional CAD risk factors were analyzed by sex and age, and their adjusted effects on the age of CAD diagnosis were calculated. RESULTS: From January 2005 to December 2015, data for 90,094 patients were included in this analysis. A total of 61,684 (68.5%) were men and 28,410 (31.5%) were women. Men were younger at diagnosis than women, with a mean age of 60.1 in men and 63.2 in women (p < 0.001), and had fewer risk factors at the time of diagnosis. Mean age at diagnosis had an overall increasing trend during the study period. Increasing trend was seen in body-mass index, hypertension prevalence, diabetes mellitus. All lipid profile components (total cholesterol, low-density lipoprotein cholesterol, triglycerides, and high-density lipoprotein cholesterol) decreased over time. Of particular interest, opium consumption was associated with 2.2 year earlier age of CAD diagnosis. CONCLUSION: The major results of this study (lower age of CAD diagnosis in men, lower age of diagnosis associated with most risk factors, and lower prevalence of serum lipids over time) were expected. A prominent finding of this study is confirming opium use was associated with a much younger age of CAD onset, even after adjusting for all other risk factors. In addition to recommendations for control of the traditional risk factors, spreading information about the potential adverse effect of opium use, which has only recently been associated with higher risk of CAD, may be necessary.

Disparities and spatial variations of high salt intake in Iran: a subnational study of districts based on the small area estimation method.

OBJECTIVE: High salt intake is one of the leading diet-related risk factors for several non-communicable diseases. We aimed to estimate the prevalence of high salt intake in Iran. DESIGN: A modelling study by the small area estimation method, based on a nationwide cross-sectional survey, Iran STEPwise approach to risk factor Surveillance (STEPS) 2016. The modelling estimated the prevalence of high salt intake, defined as a daily salt intake ≥ 5 g in all districts of Iran based on data from available districts. The modelling results were provided in different geographical and socio-economic scales to make the comparison possible across the country. SETTING: 429 districts of all provinces of Iran, 2016. PARTICIPANTS: 18 635 salt intake measurements from individuals 25 years old and above who participated in the Iran STEPS 2016 survey. RESULTS: All districts in Iran had a high prevalence of high salt intake. The estimated prevalence of high salt intake among females of all districts ranged between 72·68 % (95 % UI 58·48, 84·81) and 95·04 % (95 % UI 87·10, 100). Estimated prevalence for males ranged between 88·44 % (95 % UI 80·29, 96·15) and 98·64 % (95 % UI 94·97, 100). In all categorisations, males had a significantly higher prevalence of high salt intake. Among females, the population with the lower economic status had a higher salt consumption than the participants with higher economic status by investigating the concentration index. CONCLUSIONS: Findings of this study highlight the high salt intake as a prominent risk factor in all Iran regions, despite some variations in different scales. More suitable population-wide policies are warranted to handle this public health issue in Iran.

Anti-modified citrullinated vimentin antibody: a novel biomarker associated with cardiac systolic dysfunction in patients with rheumatoid arthritis.

BACKGROUND: Studies have demonstrated that seropositive patients with rheumatoid arthritis (RA) are susceptible to cardiovascular diseases (CVDs). In this study, we aimed to determine the association of autoantibodies with the echocardiographic parameters of systolic and diastolic dysfunction in such patients. METHODS: In this cross-sectional study, we evaluated patients with RA who were referred to our clinic from October 2017 to August 2018. After the exclusion of patients with concomitant CVD, all patients underwent transthoracic echocardiography and measurement of plasma autoantibodies. Moreover, possible confounders-including medications, CVD risk factors, Framingham risk score, disease activity score-28, duration of disease, simple disease activity index, and functional status-were assessed. RESULTS: We studied 135 patients with RA (mean age = 52.3 years; 111 (82.2%) females). We had missing data rates of up to 8.9% for some characteristics. E velocity was inversely correlated with rheumatoid factor (P = 0.009). Furthermore, the plasma levels of anti-citrullinated protein and anti-modified citrullinated vimentin (anti-MCV) antibodies were negatively correlated with left ventricular ejection fraction (LVEF) (P = 0.019 and P<0.001, respectively). After an adjustment for possible confounders, the linear regression model demonstrated that the anti-MCV level and the patient's age are significant predictors of LVEF. The receiver operating characteristic curve showed that anti-MCV antibody titer≥547.5 (IU/mL) signifies reduced LVEF (<50%) with a sensitivity of 85.7% and specificity of 93% (C-statistic = 0.843). CONCLUSIONS: Our findings showed a significant inverse correlation between anti-MCV antibody titer and LVEF. These results indicate that the application of anti-MCV is promising for the screening and early detection of cardiac systolic dysfunction. Future prospective studies will determine its role.

Global, Regional and National Burden of Bladder Cancer, 1990 to 2016: Results from the GBD Study 2016.

PURPOSE: Bladder cancer is among the leading causes of cancer death worldwide. Data on the bladder cancer burden are valuable for policy-making. We aimed to estimate the burden of bladder cancer by country, age group, gender and sociodemographic status between 1990 and 2016. MATERIALS AND METHODS: Data from vital registration systems and cancer registries were the input to estimate the bladder cancer burden. Mortality was estimated in an ensemble model approach, incidence was estimated by dividing mortality by the mortality-to-incidence ratio and prevalence was estimated using the mortality-to-incidence ratio as a surrogate for survival. We modeled the years lived with disability using disability weights of bladder cancer sequelae. Years of life lost were calculated by multiplying the number of deaths by age by the standard life expectancy at that age. Disability adjusted life-years were calculated by summing the years lived with disability and the years of life lost. Moreover, we also estimated the burden attributable to bladder cancer risk factors, smoking and high fasting plasma glucose using the comparative risk assessment framework of the Global Burden of Disease study. RESULTS: In 2016 there were 437,442 incident cases (95% UI 426,709-447,912) of bladder cancer with an age standardized incidence rate of 6.69/100,000 (95% UI 6.52-6.85). Bladder cancer led to 186,199 deaths (95% UI 180,453-191,686) in 2016 with an age standardized rate of 2.94/100,000 (95% UI 2.85-3.03). Bladder cancer was responsible for 3,315,186 disability adjusted life-years (95% UI 3,193,248-3,425,530) in 2016 with an age standardized rate of 49.45/100,000 (95% UI 47.68-51.11). Of bladder cancer deaths 26.84% (95% UI 19.78-33.91) and 7.29% (95% UI 1.49-16.19) were due to smoking and high fasting glucose, respectively, in 2016. CONCLUSIONS: Although the number of bladder cancer incident cases is growing globally, the age standardized incidence and number of deaths are decreasing, as mirrored by a decreasing smoking contribution.

Correlation between hospital rates of survival to discharge and long-term survival for in-hospital cardiac arrest: Insights from Get With The Guidelines®-Resuscitation registry.

AIM: Given challenges in collecting long-term outcomes for survivors of in-hospital cardiac arrest (IHCA), most studies have focused on in-hospital survival. We evaluated the correlation between a hospital's risk-standardized survival rate (RSSR) at hospital discharge for IHCA with its RSSR for long-term survival. METHODS: We identified patients ≥65 years of age with IHCA at 472 hospitals in Get With The Guidelines®-Resuscitation registry during 2000-2012, who could be linked to Medicare files to obtain post-discharge survival data. We constructed hierarchical logistic regression models to compute RSSR at discharge, and 30-day, 1-year, and 3-year RSSRs for each hospital. The association between in-hospital and long-term RSSR was evaluated with weighted Kappa coefficients. RESULTS: Among 56,231 Medicare beneficiaries (age 77.2 ± 7.5 years and 25,206 [44.8%] women), 10,536 (18.7%) survived to discharge and 8,485 (15.1%) survived to 30 days after discharge. Median in-hospital, 30-day, 1-year, and 3-year RSSRs were 18.6% (IQR, 16.7-20.4%), 14.9% (13.2-16.7%), 10.3% (9.1-12.1%), and 7.6% (6.8-8.8%), respectively. The weighted Kappa coefficient for the association between a hospital's RSSR at discharge with its 30-day, 1-year, and 3-year RSSRs were 0.72 (95% CI, 0.68-0.76), 0.56 (0.50-0.61), and 0.47 (0.41-0.53), respectively. CONCLUSIONS: There was a strong correlation between a hospital's RSSR at discharge and its 30-day RSSR for IHCA, although this correlation weakens over time. Our findings suggest that a hospital's RSSR at discharge for IHCA may be a reasonable surrogate of its 30-day post-discharge survival and could be used by Medicare to benchmark hospital performance for this condition without collecting 30-day survival data.

Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study.

AIMS: Despite notable population differences in high-income and low- and middle-income countries (LMICs), national guidelines in LMICs often recommend using US-based cardiovascular disease (CVD) risk scores for treatment decisions. We examined the performance of widely used international CVD risk scores within the largest Brazilian community-based cohort study (Brazilian Longitudinal Study of Adult Health, ELSA-Brasil). METHODS: All adults 40-75 years from ELSA-Brasil (2008-2013) without prior CVD who were followed for incident, adjudicated CVD events (fatal and non-fatal MI, stroke, or coronary heart disease death). We evaluated 5 scores-Framingham General Risk (FGR), Pooled Cohort Equations (PCEs), WHO CVD score, Globorisk-LAC and the Systematic Coronary Risk Evaluation 2 score (SCORE-2). We assessed their discrimination using the area under the receiver operating characteristic curve (AUC) and calibration with predicted-to-observed risk (P/O) ratios-overall and by sex/race groups. RESULTS: There were 12 155 individuals (53.0±8.2 years, 55.3% female) who suffered 149 incident CVD events. All scores had a model AUC>0.7 overall and for most age/sex groups, except for white women, where AUC was <0.6 for all scores, with higher overestimation in this subgroup. All risk scores overestimated CVD risk with 32%-170% overestimation across scores. PCE and FGR had the highest overestimation (P/O ratio: 2.74 (95% CI 2.42 to 3.06)) and 2.61 (95% CI 1.79 to 3.43)) and the recalibrated WHO score had the best calibration (P/O ratio: 1.32 (95% CI 1.12 to 1.48)). CONCLUSION: In a large prospective cohort from Brazil, we found that widely accepted CVD risk scores overestimate risk by over twofold, and have poor risk discrimination particularly among Brazilian women. Our work highlights the value of risk stratification strategies tailored to the unique populations and risks of LMICs.

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.

Computational Phenomapping of Randomized Clinical Trials to Enable Assessment of their Real-world Representativeness and Personalized Inference.

Importance: Randomized clinical trials (RCTs) are the standard for defining an evidence-based approach to managing disease, but their generalizability to real-world patients remains challenging to quantify. Objective: To develop a multidimensional patient variable mapping algorithm to quantify the similarity and representation of electronic health record (EHR) patients corresponding to an RCT and estimate the putative treatment effects in real-world settings based on individual treatment effects observed in an RCT. Design: A retrospective analysis of the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial (TOPCAT; 2006-2012) and a multi-hospital patient cohort from the electronic health record (EHR) in the Yale New Haven Hospital System (YNHHS; 2015-2023). Setting: A multicenter international RCT (TOPCAT) and multi-hospital patient cohort (YNHHS). Participants: All TOPCAT participants and patients with heart failure with preserved ejection fraction (HFpEF) and ≥1 hospitalization within YNHHS. Exposures: 63 pre-randomization characteristics measured across the TOPCAT and YNNHS cohorts. Main Outcomes and Measures: Real-world generalizability of the RCT TOPCAT using a multidimensional phenotypic distance metric between TOPCAT and YNHHS cohorts. Estimation of the individualized treatment effect of spironolactone use on all-cause mortality within the YNHHS cohort based on phenotypic distance from the TOPCAT cohort. Results: There were 3,445 patients in TOPCAT and 11,712 HFpEF patients across five hospital sites. Across the 63 TOPCAT variables mapped by clinicians to the EHR, there were larger differences between TOPCAT and each of the 5 EHR sites (median SMD 0.200, IQR 0.037-0.410) than between the 5 EHR sites (median SMD 0.062, IQR 0.010-0.130). The synthesis of these differences across covariates using our multidimensional similarity score also suggested substantial phenotypic dissimilarity between the TOPCAT and EHR cohorts. By phenotypic distance, a majority (55%) of TOPCAT participants were closer to each other than any individual EHR patient. Using a TOPCAT-derived model of individualized treatment benefit from spironolactone, those predicted to derive benefit and receiving spironolactone in the EHR cohorts had substantially better outcomes compared with predicted benefit and not receiving the medication (HR 0.74, 95% CI 0.62-0.89). Conclusions and Relevance: We propose a novel approach to evaluating the real-world representativeness of RCT participants against corresponding patients in the EHR across the full multidimensional spectrum of the represented phenotypes. This enables the evaluation of the implications of RCTs for real-world patients. KEY POINTS: Question: How can we examine the multi-dimensional generalizability of randomized clinical trials (RCT) to real-world patient populations?Findings: We demonstrate a novel phenotypic distance metric comparing an RCT to real-world populations in a large multicenter RCT of heart failure patients and the corresponding patients in multisite electronic health records (EHRs). Across 63 pre-randomization characteristics, pairwise assessments of members of the RCT and EHR cohorts were more discordant from each other than between members of the EHR cohort (median standardized mean difference 0.200 [0.037-0.410] vs 0.062 [0.010-0.130]), with a majority (55%) of RCT participants closer to each other than any individual EHR patient. The approach also enabled the quantification of expected real world outcomes based on effects observed in the RCT.Meaning: A multidimensional phenotypic distance metric quantifies the generalizability of RCTs to a given population while also offering an avenue to examine expected real-world patient outcomes based on treatment effects observed in the RCT.

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.

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.

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.

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.

Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

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).

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.

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.