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Objective: To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice using a pragmatic randomized controlled trial (RCT). Patients and Methods: In a post hoc analysis of the electrocardiogram (ECG) AI-guided screening for low ejection fraction trial, we developed a decision analytic model for patients aged 18 years and older without previously diagnosed heart failure and underwent a clinically indicated ECG between August 5, 2019, and March 31, 2020. In the previously published RCT, the intervention arm underwent an AI-guided targeted screening program for low EF with a workflow embedded into routine clinical practice—AI was applied to the ECG to identify patients at high-risk and recommended clinicians to order an ECG and the control arm received usual care without the screening program. We used results from the RCT for rates of low EF diagnosis and a lifetime Markov model to project the long-term outcomes. Quality-adjusted life years (QALYs), costs of intervention and treatment, disease event costs, incremental cost-effectiveness ratio (ICER), and cost for the number needed to screen. Multiple scenario and sensitivity analyses were performed. Results: Compared with usual care, AI-integrated ECG was cost effective, with an incremental cost-effectiveness ratio of $27,858/QALY. The program remained cost effective even with a change in patient age and follow-up time duration, although the specific ICER values varied for these parameters. The program was more cost effective in outpatient settings (ICER $1651/QALY) than in inpatient or emergency room settings. Conclusion: Implementing AI-guided targeted screening for low EF in routine clinical practice was cost effective.

Abstract Aims We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). Methods A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild‐type transthyretin amyloid protein (ATTRwt) and 169 with hereditary transthyretin amyloid (ATTRv)] were included. An amyloid AI ECG (A2E) score was calculated for each patient reflecting the likelihood of CA. CA stage was calculated using the European modification of the Mayo 2004 criteria for AL and Mayo stage for transthyretin amyloid (ATTR). Risk of death was modelled using Cox proportional hazards, and Kaplan–Meier was used to estimate survival. Results Median age of the cohort was 67 [inter‐quartile ratio (IQR) 59, 74], and 71.6% were male. The median overall survival for the cohort was 35.6 months [95% confidence interval (CI) 32.3, 39.5]. For AL, ATTRwt and ATTRv, respectively, median survival was 22.9 (95% CI 19.2, 28.2), 47.2 (95% CI 43.4, 52.3) and 61.4 (95% CI 48.7, 75.9) months. On univariate analysis, an increasing A2E score was associated with more than a two‐fold risk of all‐cause death. On multivariable analysis, the A2E score retained its importance with a risk ratio of 2.0 (95% CI 1.58, 2.55) in the AL group and 2.7 (95% CI 1.81, 4.24) in the ATTR group. Conclusions Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.

Objective: To develop an artificial intelligence deep neural network (AI-DNN) algorithm to analyze 12-lead electrocardiogram (ECG) for detection of acute pulmonary embolism (PE) and PE categories. Patients and Methods: A cohort of patients seen between January 1, 1999, and December 31, 2020, from across the Mayo Clinic Enterprise with computed tomography pulmonary angiogram (CTPA) and ECG performed ±6 hours was identified. Natural language processing algorithms were applied to radiology reports to determine the diagnosis of acute PE, acute right ventricular strain pulmonary embolism (RVSPE), saddle pulmonary embolism (SADPE), or no PE. Diagnostic performance parameters of the AI-DNN reported were area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: A cohort of patients with CTPA report and ECG consisted of 79,894 patients including 7423 (9.3%) with acute PE, among whom 1138 patients had RVSPE or SADPE. Artificial intelligence deep neural network predicted acute PE with a modest accuracy of AUROC of 0.69 (95% CI, 0.68-0.71), sensitivity of 63.5%, specificity of 64.7%, PPV of 15.6%, and NPV of 94.5%. The AI-DNN prediction using the same algorithm for RVSPE or SADPE was higher (AUROC, 0.84; 95% CI, 0.81-0.86) with a sensitivity of 80.8%, specificity of 64.7.8%, PPV of 3.5%, and NPV of 99.5%. Conclusion: An AI-based analysis of 12-lead ECG shows modest detection power for acute PE in patients who underwent CTPA, with higher accuracy for high-risk PE. Moreover, with the high NPV, it has the clinical potential to exclude high-risk PE quickly and correctly.

Objective: To evaluate the effect of smart device-based telerehabilitation on Vo2peak in patients after myocardial infarction. Patients and Methods: This was a pilot, single-center, randomized, cross-over study with a 3-month intervention. One month after myocardial infarction, patients had cardiopulmonary exercise testing and a 6-minute walking test (6MWT) and were randomly assigned 1:1. In the intervention group, patients received a smartwatch to track the recommended number of steps, which was individualized and derived from the 6MWT. A study nurse telemonitored adherence to the recommended number of steps a day. In the control group, 150 minutes a week of moderate-intensity physical activity was recommended. After 3 months study arms were crossed over, and study procedures were repeated after 3 months. Results: Between June 1, 2019, and February 28, 2023, 64 patients were randomized, of which 61 (aged 51±10 years, 10% women) completed the study. Overall, the smart device-based telerehabilitation led to 2.31 mL/kg/min (95% CI, 1.25-3.37; P

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF

Background A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small‐vessel disease, implicating vascular disease in the development of depression. Cross‐sectional studies demonstrate a link between endothelial dysfunction and MDD, but the prospective association between peripheral endothelial dysfunction (PED) and an incident diagnosis of MDD is unknown. Methods and Results Patients undergoing a baseline assessment of cardiovascular risk were evaluated for PED using reactive hyperemia–peripheral arterial tonometry (≤1.8 consistent with PED). Patient medical records were reviewed to identify those who underwent a formal clinical evaluation of MDD after the index PED evaluation. The frequency of PED was compared in those with and without MDD. Logistic regression analyses were performed to assess the association between baseline PED and incident MDD. Between January 2006 and December 2020, 1614 patients underwent testing for PED. Four hundred eighty‐four (30.1%) patients underwent a formal evaluation for MDD after (0–15 years) the index procedure (mean±SD age, 52.8±13.8 years; 65.2% women). Of these, 157 (32.4%) had PED and 108 (31.0%) were diagnosed with MDD. Individuals with MDD had a higher frequency of PED (40.2% versus 30.2%; P=0.034) compared with those without MDD. In multivariable analyses, PED was significantly associated with MDD (odds ratio, 2.3 [95% CI, 1.4–3.8]; P

Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

Abstract Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645–1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298–1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.

Objective: To develop and validate a machine learning model that predicts the most successful antihypertensive treatment for an individual. Patients and Methods: The causal, deep neural network-based model was trained on data from 16,917 newly diagnosed hypertensive patients attending Mayo Clinic’s primary care practices from January 1, 2005, to December 31, 2021. Eligibility criteria included a diagnosis of primary hypertension, blood pressure and creatinine measurements before antihypertensive treatment, treatment within 9 months of diagnosis, and at least 1 year of follow up. The primary outcome was model performance in predicting the likelihood of a successful antihypertensive treatment 1 year from the start of treatment. Treatment success was defined as achieving blood pressure control with no moderate or severe adverse effects. Model validation and guideline agreement was assessed on 1000 patients. Results: In the training set of 16,917 participants (60.8±14.7 years; 8344 [49.3%] women), 33.8% achieved blood pressure control without moderate or severe adverse effects for at least a year with initial treatment. The most common treatment was angiotensin-converting enzyme inhibitor (39.1% average success), and the most successful was angiotensin-converting enzyme inhibitor-thiazide combination (44.4% average success). Our custom-built causal, deep neural network-based model exhibited the highest accuracy in predicting individualized treatment success with a precision of 51.7%, recall of 44.4%, and F1 score of 47.8%. Compared with actual physician practice on the validation set (77.9% agreement), the algorithm aligned with the Eighth Joint National Committee hypertension guidelines 95.7% of the time. Conclusion: A machine learning algorithm can accurately predict the likelihood of antihypertensive treatment success and help personalize hypertension management.

Background Extended sedentary behavior is a risk factor for chronic disease and mortality, even among those who exercise regularly. Given the time constraints of incorporating physical activity into daily schedules, and the high likelihood of sitting during office work, this environment may serve as a potentially feasible setting for interventions to reduce sedentary behavior. Methods and Results A randomized cross‐over clinical trial was conducted at an employee wellness center. Four office settings were evaluated on 4 consecutive days: stationary or sitting station on day 1 (referent), and 3 subsequent active workstations (standing, walking, or stepper) in randomized order. Neurocognitive function (Selective Attention, Grammatical Reasoning, Odd One Out, Object Reasoning, Visuospatial Intelligence, Limited‐Hold Memory, Paired Associates Learning, and Digit Span) and fine motor skills (typing speed and accuracy) were tested using validated tools. Average scores were compared among stations using linear regression with generalized estimating equations to adjust standard errors. Bonferroni method adjusted for multiple comparisons. Healthy subjects were enrolled (n=44), 28 (64%) women, mean±SD age 35±11 years, weight 75.5±17.1 kg, height 168.5±10.0 cm, and body mass index 26.5±5.2 kg/m2. When comparing active stations to sitting, neurocognitive test either improved or remained unchanged, while typing speed decreased without affecting typing errors. Overall results improved after day 1, suggesting habituation. We observed no major differences across active stations, except decrease in average typing speed 42.5 versus 39.7 words per minute with standing versus stepping (P=0.003). Conclusions Active workstations improved cognitive performance, suggesting that these workstations can help decrease sedentary time without work performance impairment. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT06240286.

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer‐assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. Methods and Results Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle–brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all‐cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow‐up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78–3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49–2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43–22.39]) at 5 years. Conclusions An artificial intelligence–enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Background Although several studies have documented the impact of the COVID-19 pandemic on mental health, the long-term effects remain unclear. Aims To examine longitudinal changes in mental health before and during the consecutive COVID-19 waves in a well-established probability sample. Method An online survey was completed by the participants of the COVID-19 add-on study at four time points: pre-COVID-19 period (2014–2015, n = 1823), first COVID-19 wave (April to May 2020, n = 788), second COVID-19 wave (August to October 2020, n = 532) and third COVID-19 wave (March to April 2021, n = 383). Data were collected via a set of validated instruments, and analysed with latent growth models. Results During the pandemic, we observed a significant increase in stress levels (standardised β = 0.473, P < 0.001) and depressive symptoms (standardised β = 1.284, P < 0.001). The rate of increase in depressive symptoms (std. covariance = 0.784, P = 0.014), but not in stress levels (std. covariance = 0.057, P = 0.743), was associated with the pre-pandemic mental health status of the participants. Further analysis showed that secondary stressors played a predominant role in the increase in mental health difficulties. The main secondary stressors were loneliness, negative emotionality associated with the perception of COVID-19 disease, lack of resilience, female gender and younger age. Conclusions The surge in stress levels and depressive symptoms persisted across all three consecutive COVID-19 waves. This persistence is attributable to the effects of secondary stressors, and particularly to the status of mental health before the COVID-19 pandemic. Our findings reveal mechanisms underlying the surge in mental health difficulties during the COVID-19 waves, with direct implications for strategies promoting mental health during pandemics.

Summary: Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods: Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023. Findings: ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14–2.71), 4.23 (3.74–4.78), and 11.75 (10.2–13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age. Interpretation: ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired. Funding: Anumana.

Abstract Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ( $$p\le 5 \times 10^{-8}$$ p ≤ 5 × 10 - 8 ), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.

Objective: To characterize the utility of an existing electrocardiogram (ECG)-artificial intelligence (AI) algorithm of left ventricular dysfunction (LVD) in immune-mediated necrotizing myopathy (IMNM). Patients and Methods: A retrospective cohort observational study was conducted within our tertiary-care neuromuscular clinic for patients with IMNM meeting European Neuromuscular Centre diagnostic criteria (January 1, 2000, to December 31, 2020). A validated AI algorithm using 12-lead standard ECGs to detect LVD was applied. The output was presented as a percent probability of LVD. Electrocardiograms before and while on immunotherapy were reviewed. The LVD-predicted probability scores were compared with echocardiograms, immunotherapy treatment response, and mortality. Results: The ECG-AI algorithm had acceptable accuracy in LVD prediction in 74% (68 of 89) of patients with IMNM with available echocardiograms (discrimination threshold, 0.74; 95% CI, 0.6-0.87). This translates into a sensitivity of 80.0% and specificity of 62.8% to detect LVD. Best cutoff probability prediction was 7 times more likely to have LVD (odds ratio, 6.75; 95% CI, 2.11-21.51; P=.001). Early detection occurred in 18% (16 of 89) of patients who initially had normal echocardiograms and were without cardiorespiratory symptoms, of which 6 subsequently advanced to LVD cardiorespiratory failure. The LVD probability scores improved for patients on immunotherapy (median slope, −3.96; R = −0.12; P=.002). Mortality risk was 7 times greater with abnormal LVD probability scores (hazard ratio, 7.33; 95% CI, 1.63-32.88; P=.009). Conclusion: In IMNM, an AI-ECG algorithm assists detection of LVD, enhancing the decision to advance to echocardiogram testing, while also informing on mortality risk, which is important in the decision of immunotherapy escalation and monitoring.

Obesity remains a major public health problem, affecting almost half of adults in the United States. Increased risk of cardiovascular disease (CVD) and CVD mortality are major obesity‐related complications, and management guidelines now recommend weight loss as a key strategy for the primary prevention of CVD in patients with overweight or obesity. The recently demonstrated efficacy of some pharmacologic therapies for chronic weight management may encourage health care professionals to recognize obesity as a treatable serious chronic disease and motivate patients to re‐engage with weight loss when previous attempts have been ineffective or unsustainable. This review article summarizes the benefits and challenges associated with lifestyle changes, bariatric surgery, and historical pharmacologic interventions in the treatment of obesity, and focuses on the current evidence for the efficacy and safety of the newer glucagon‐like peptide‐1 receptor agonist medications in the management of obesity and potential reduction of CVD risk. We conclude that the available evidence demonstrates glucagon‐like peptide‐1 receptor agonists should be strongly considered in clinical practice for the treatment of obesity and reduction of CVD risk in people with type 2 diabetes. If ongoing research proves glucagon‐like peptide‐1 receptor agonists to be effective in reducing the risk of CVD onset in patients with obesity, irrespective of type 2 diabetes status, it will herald a new treatment paradigm in this setting, and now is the time for health care professionals to better recognize the benefits of these agents.

Background There is a scarcity of validated rapid dietary screening tools for patient use in the clinical setting to improve health and reduce cardiovascular risk. The Healthy Eating Index (HEI) 2015 measures compliance with the 2015 to 2020 Dietary Guidelines for Americans but requires completion of an extensive diet assessment to compute, which is time consuming and impractical. The authors hypothesize that a 19‐item dietary survey assessing consumption of common food groups known to affect health will be correlated with the HEI‐2015 assessed by a validated food frequency questionnaire and can be further reduced without affecting validity. Methods and Results A 19‐item Eating Assessment Tool (EAT) of common food groups was created through literature review and expert consensus. A cross‐sectional survey was then conducted in adult participants from a preventive cardiology clinic or cardiac rehabilitation and in healthy volunteers (n=661, mean age, 36 years; 76% women). Participants completed an online 156‐item food frequency questionnaire, which was used to calculate the HEI score using standard methods. The association between each EAT question and HEI group was analyzed by Kruskal‐Wallis test. Linear regression models were subsequently used to identify univariable and multivariable predictors for HEI score for further reduction in the number of items. The final 9‐item model of Mini‐EAT was validated by 5‐fold cross validation. The 19‐item EAT had a strong correlation with the HEI score (r=0.73) and was subsequently reduced to the 9 items independently predictive of the HEI score: fruits, vegetables, whole grains, refined grains, fish or seafood, legumes/nuts/seeds, low‐fat dairy, high‐fat dairy, and sweets consumption, without affecting the predictive ability of the tool (r=0.71). Conclusions Mini‐EAT is a 9‐item validated brief dietary screener that correlates well with a comprehensive food frequency questionnaire. Future studies to test the Mini‐EAT's validity in diverse populations and for development of clinical decision support systems to capture changes over time are needed.

Objective: With the emergence of artificial intelligence (AI)-based health interventions, systemic racism remains a concern as these advancements are frequently developed without race-specific data analysis or validation. To evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-concept ancillary study for community-based screening. Methods: Enrollees completed cardiovascular testing including standard 12-lead ECG and a limited echocardiogram (TTE). All ECGs were analyzed using previously published institution-based AI algorithms. AI-ECG predictions were generated for age, sex, and decreased left ventricular ejection fraction (LVEF). Diagnostic accuracy of the AI-ECG for decreased LVEF and sex was quantified using area under the receiver operating characteristic curve (AUC). Correlation between actual age and AI-ECG predicted age was assessed using Pearson correlation coefficients. Results: Fifty-four participants completed both an ECG and TTE (mean age 55 years [range 31-87 years]; 66.7% female). All participants were in sinus rhythm, and the median LVEF of the cohort was 60-65%. The AI-ECG for decreased LVEF demonstrated excellent performance with an AUC of 0.892 (95% confidence interval [CI] 0.708-1); sensitivity=50% (95% CI 9.5-90.5%; n=1/2) and specificity=96% (95% CI 86.8-98.9%; n=49/51). The AI-ECG for participant sex demonstrated similar performance with AUC of 0.944 (95% CI 0.891-0.998); sensitivity=100% (95% CI 82.4-100.0%; n=18/18) and specificity=77.8% (95% CI 61.9-88.3%; n=28/36). The AI-ECG predicted mean age was 55 years (range 26.9-72.6 years) with a strong correlation to actual age (R=0.769; p

Abstract Aims/Hypothesis The need for understanding obesity as a chronic disease, its stigmatization, and the lack of actionability related to it demands a new approach. The adiposity‐based chronic disease (ABCD) model is based on adiposity amount, distribution, and function, with a three stage complication‐centric rather than a body mass index (BMI)‐centric approach. The prevalence rates and associated risk factors are presented. Methods In total, 2159 participants were randomly selected from Czechia. ABCD was established as BMI ≥ 25 kg/m2 or high body fat percent, or abdominal obesity and then categorized by their adiposity‐based complications: Stage 0: none; Stage 1: mild/moderate; Stage 2: severe. Results ABCD prevalence was 62.8%. Stage 0 was 2.3%; Stage 1 was 31.4%; Stage 2 was 29.1%. Comparing with other classifiers, participants in Stage 2 were more likely to have diabetes, hypertension, and metabolic syndrome than those with overweight, obesity, abdominal obesity, and increased fat mass. ABCD showed the highest sensitivity and specificity to detect participants with peripheral artery disease, increased intima media, and vascular disease. Conclusion/Interpretation The ABCD model provides a more sensitive approach that facilitates the early detection and stratification of participants at risk compared to traditional classifiers.

Abstract Background Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. Methods This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011–2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. Results Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all P trend

More than 500 million people worldwide live with cardiovascular disease (CVD). Health systems today face fundamental challenges in delivering optimal care due to ageing populations, healthcare workforce constraints, financing, availability and affordability of CVD medicine, and service delivery. Digital health technologies can help address these challenges. They may be a tool to reach Sustainable Development Goal 3.4 and reduce premature mortality from non-communicable diseases (NCDs) by a third by 2030. Yet, a range of fundamental barriers prevents implementation and access to such technologies. Health system governance, health provider, patient and technological factors can prevent or distort their implementation. World Heart Federation (WHF) roadmaps aim to identify essential roadblocks on the pathway to effective prevention, detection, and treatment of CVD. Further, they aim to provide actionable solutions and implementation frameworks for local adaptation. This WHF Roadmap for digital health in cardiology identifies barriers to implementing digital health technologies for CVD and provides recommendations for overcoming them.

BackgroundThe prevalence of obesity continues to increase in spite of substantial efforts towards its prevention, posing a major threat to health globally. Circadian disruption has been associated with a wide range of preclinical and clinical disorders, including obesity. However, whether rest-activity rhythm (RAR), an expression of the endogenous circadian rhythm, is associated with excess adiposity is poorly understood. Here we aimed to assess the association of RAR with general and abdominal obesity.MethodsNon-institutionalized adults aged ≥20 years participating in the US National Health and Nutrition Examination Survey (NHANES) 2011-2014 who wore accelerometers for at least four 24-hour periods were included (N=7,838). Amplitude, mesor, acrophase and pseudo-F statistic of RAR were estimated using extended cosinor model, and interdaily stability (IS) and intradaily variability (IV) were computed by nonparametric methods. We tested the association between rest-activity rhythm and general obesity defined by body mass index and abdominal obesity by waist circumference. Waist-to-height ratio, sagittal abdominal diameter, and total and trunk fat percentages measured by imaging methods were also analyzed.ResultsIn multivariable analysis, low amplitude (magnitude of the rhythm), mesor (rhythm-corrected average activity level), pseudo-F statistic (robustness of the rhythm), IS (day-to-day rhythm stability), or high IV (rhythm fragmentation) were independently associated with higher likelihood of general or abdominal obesity (all Ps

Objective: To investigate the effect of replacing sitting with standing on cardiovascular risk factors tested in clinical trials. Methods: We searched databases from inception up to August 28, 2019, for studies examining the effect of replacing sitting with standing on fasting blood glucose, fasting insulin, and lipid levels; blood pressure; body fat mass; weight; and waist circumference in healthy adults. Differences in mean ± SD values were used for pooling the data and calculating the mean differences and CIs. Results: The search found 3507 abstracts. Nine clinical trials (8 randomized and 1 nonrandomized) with 877 (64.4% [n=565] women) participants met all inclusion criteria. The mean ± SD age was 45.34±5.41 years; mean follow-up was 3.81 months, and mean difference in standing time between the intervention and control groups was 1.33 hours per day. The follow-up fasting blood glucose and body fat mass values were slightly but significantly lower than baseline records in the intervention groups compared with control groups (−2.53; 95% CI, −4.27 to −0.79 mg/dL; and −0.75; 95% CI, −0.91 to −0.59 kg). The analysis for fasting insulin levels, lipid levels, blood pressure, weight, and waist circumference revealed no significant differences. Conclusion: Replacing sitting with standing can result in very small but statistically significant decreases in fasting blood glucose levels and body fat mass with no significant effect on lipid levels, blood pressure, weight, and waist circumference. Replacing sitting with standing can be used as an adjunctive intervention to decrease the burden of cardiovascular risk factors but cannot be used as an alternative to physical activity to decrease sedentary time.

Abstract Background The term arterial stiffness (ArSt) describes structural changes in arterial wall related to the loss of elasticity and is known as an independent predictor of cardiovascular diseases (CVD). The evidence relating to ArSt and triglycerides (TG) shows contradictory results. This paper means to survey the association between high TG and ArSt, utilizing the cardio-ankle vascular index (CAVI). Methods Subjects aged between 25 and 64 years from a random population-based sample were evaluated between 2013 and 2016. Data from questionnaires, blood pressure, anthropometric measures, and blood samples were collected and analyzed. CAVI was measured using VaSera VS-1500 N devise. Subjects with a history of CVD or chronic renal disease were excluded. Results One thousand nine hundred thirty-four participants, 44.7% of males, were included. The median age was 48 (Interquartile Range [IQR] 19) years, TG levels were 1.05 (0.793) mmol/L, and CAVI 7.24 (1.43) points. Prevalence of high CAVI was 10.0% (14.5% in males and 6.4% in females; P

Background: Ambulatory overnight oximetry (OXI) has emerged as a cost-effective initial test for sleep disordered breathing. Obesity is closely associated with obstructive sleep apnea (OSA); however, whether body mass index (BMI) or waist-to-hip ratio (WHR) predicts abnormal overnight OXI remains unknown.Methods: We performed a retrospective cross-sectional study of 393 men seen in the Executive Health Program at Mayo Clinic in Rochester, Minnesota who underwent ambulatory overnight OXI ordered by preventive medicine physicians between January 1, 2004 through December 31, 2010. We compared participant/spouse-reported symptoms (sleepiness, snoring), physician indications for OXI (obesity, fatigue), Epworth Sleepiness Scale scores, anthropomorphic measurements (WHR, BMI), and comorbid medical conditions (hypertension, diabetes) with OXI results.Results: 295 of the 393 men who completed OXI had abnormal results. During multivariate analysis, the strongest independent predictor of abnormal OXI for men was WHR (≥1.0, OR = 5.59) followed by BMI (≥30.0 kg/m2, OR = 2.75), age (≥55 yrs, OR = 2.06), and the presence of snoring (OR = 1.91, P < 0.05 for all). A strong association was observed between WHR and abnormal OXI in obese (BMI ≥ 30.0 kg/m2, OR = 6.28) and non-obese (BMI < 29.9 kg/m2, OR = 6.42, P < 0.01 for both) men. Furthermore, 88 men with abnormal OXI underwent polysomnography with 91% being subsequently diagnosed with OSA.Conclusions: In ambulatory, predominantly middle-aged men undergoing preventive services evaluation many physician indications for OXI were not predictors of abnormal results; however, WHR strongly predicted abnormal OXI in obese and non-obese men. As such, we suggest middle-aged men who snore and have a WHR ≥1.0 should be directly referred to a sleep clinic for polysomnography.

BackgroundLeft ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested.ObjectiveTo analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%.Methodology/principal findingsThis is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction ConclusionThe AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.

Objective: To investigate the association of pet ownership, and specifically dog ownership, with cardiovascular diseases (CVD) risk factors and cardiovascular health (CVH) in the Kardiovize Brno 2030 study, a randomly selected prospective cohort in Central Europe. Patients and Methods: We included 1769 subjects (aged from 25 to 64 years; 44.3% males) with no history of CVD who were recruited from January 1, 2013, to December 19, 2014. We compared sociodemographic characteristics, CVD risk factors, CVH metrics (ie, body mass index, healthy diet, physical activity level, smoking status, blood pressure, fasting glucose, and total cholesterol), and score between pet owners and non-pet owners or dog owners and several other subgroups. Results: Approximately 42% of subjects owned any type of pet: 24.3% owned a dog and 17.9% owned another animal. Pet owners, and specifically dog owners, were more likely to report physical activity, diet, and blood glucose at ideal level, and smoking at poor level, which resulted in higher CVH score than non-pet owners (median, 10; interquartile range = 3 vs median, 9; interquartile range = 3; P=0.006). Compared with owners of other pets, dog owners were more likely to report physical activity and diet at ideal level. The comparison of dog owners with non-dog owners yielded similar results. After adjustment for covariates, dog owners exhibited higher CVH scores than non-pet owners (β=0.342; SE=0.122; P=0.005), other pet-owners (β=0.309; SE=0.151; P=0.041), and non-dog owners (β=0.341; SE=0.117; P=0.004). Conclusion: Except for smoking, dog owners were more likely to achieve recommended level of behavioral CVH metrics (physical activity and diet) than non-dog owners, which translated into better CVH.

Background: Cardiac rehabilitation (CR) is a clinically-effective but complex model of care. The purpose of this study was to characterize the nature of CR programs around the world, in relation to guideline recommendations, and compare this by World Health Organization (WHO) region. Methods: In this cross-sectional study, a piloted survey was administered online to CR programs globally. Cardiac associations and local champions facilitated program identification. Quality (benchmark of ≥75% of programs in a given country meeting each of 20 indicators) was ranked. Results were compared by WHO region using generalized linear mixed models. Findings: 111/203 (54.7%) countries in the world offer CR; data were collected in 93 (83.8%; N = 1082 surveys, 32.1% program response rate). The most commonly-accepted indications were: myocardial infarction (n = 832, 97.4%), percutaneous coronary intervention (n = 820, 96.1%; 0.10), and coronary artery bypass surgery (n = 817, 95.8%). Most programs were led by physicians (n = 680; 69.1%). The most common CR providers (mean = 5.9 ± 2.8/program) were: nurses (n = 816, 88.1%; low in Africa, p

Background: Despite the epidemic of cardiovascular disease and the benefits of cardiac rehabilitation (CR), availability is known to be insufficient, although this is not quantified. This study ascertained CR availability, volumes and its drivers, and density. Methods: A survey was administered to CR programs globally. Cardiac associations and local champions facilitated program identification. Factors associated with volumes were assessed using generalized linear mixed models, and compared by World Health Organization region. Density (i.e. annual ischemic heart disease [IHD] incidence estimate from Global Burden of Disease study divided by national CR capacity) was computed. Findings: CR was available in 111/203 (54.7%) countries; data were collected in 93 (83.8% country response; N = 1082 surveys, 32.1% program response rate). Availability by region ranged from 80.7% of countries in Europe, to 17.0% in Africa (p

Objective: To evaluate the potential impact of a workplace well-being champion on employee and organizational measures of well-being. Patients and Methods: Baseline well-being measures were collected in October 2-20, 2017 and analyzed from January 1, 2018 through June 30, 2018 by incorporating a focused question set (addressing meaning in work, work-life integration, and physical, social, financial, emotional, and general well-being) into the biennial Mayo Clinic All-Staff Survey. Results: The survey was distributed to 64,059 employees, with a response rate of 73%. Employees with a work unit well-being champion had more favorable responses overall than did employees reporting no well-being champion. The percentage responding “favorably” to each well-being measure differed from 2 to 12 percentage points and were all highly statistically significant (P

Objective: To investigate provider opinions regarding a clinical decision support (CDS) system for cardiovascular risk assessment and for the creation of a replacement system. Methods: From March to April 2018, an invitation letter with a link to a self-administered web-based survey was sent via e-mail to 279 providers with primary appointment in the Department of Cardiovascular Medicine, Mayo Clinic, Rochester. The e-mail was sent to providers on March 8, 2018 and the survey closed on April 16, 2018. Results: One hundred providers responded to the survey yielding an overall response rate of 35.8%. Of these, 52 (52%) indicated they had used the cardiovascular (CV) risk profile CDS system and were classified as users and prompted to continue the survey. Among users, 42 (80.8%) indicated use of the CDS was either important (25; 48.1%) or very important (17; 32.7%) in their clinical practice; 45 (86.5%) responded that the system was very easy (17; 32.7%) or easy (28; 53.8%) to use. In addition, 48 (96.0%) users indicated that the CV risk profile supported their thought process at the point-of-care; 47 (97.9%) users indicated similar functionalities should be implemented into the new electronic health record system and 41 (85.4%) users reported new functionalities should also be incorporated. Conclusions: For most users, the CDS system was easy to use and supported clinical thought process at the point-of-care. Users also felt their practice was supported and should continue to be supported by CDS systems providing individualized patient information at the point-of-care.

Objective: To determine whether early Roux-en-Y gastric bypass surgery (RYGB) reduces the risk of Major adverse cardiovascular events (MACE) in patients with obesity.Patients and Methods: We conducted a study of patients with class II and III obesity [body mass index (BMI) > 35 kg/m2] from Olmsted County, Minnesota, who underwent obesity clinic consultation between the years 1993–2012, and had either RYGB surgery within 1 year (RYGB-1Y group), or medically managed (No-RYGB group). The composite endpoint of MACE (all-cause mortality, stroke, heart failure admission and acute myocardial infarction) was the primary endpoint, with new-onset AF as the secondary endpoint.Results: Of the 1,009 study patients, 308 had RYGB-1Y and 701 were medically managed (No-RYGB). Overall, the age was 44.0 ± 12.4 (mean ± SD) years; BMI was 45.0 ± 6.8 kg/m2. The RYGB-1Y group had a lower rate of MACE (adjusted hazard ratio (HR), 0.62; 95% CI, 0.44–0.88; P = 0.008) and lower mortality (adjusted HR, 0.51; 95% CI, 0.26–0.96; P = 0.04) than the No-RYGB group. The RYGB-1Y surgery was not associated with lower AF occurrence (HR, 0.66; 95% CI, 0.40–1.10; P = 0.11).Conclusion: An early RYGB approach for BMI reduction was associated with lower rates of MACE.

Background An artificial intelligence algorithm that detects age using the 12‐lead ECG has been suggested to signal “physiologic age.” This study aimed to investigate the association of peripheral microvascular endothelial function (PMEF) as an index of vascular aging, with accelerated physiologic aging gauged by ECG‐derived artificial intelligence–estimated age. Methods and Results This study included 531 patients who underwent ECG and a noninvasive PMEF assessment using reactive hyperemia peripheral arterial tonometry. Abnormal PMEF was defined as reactive hyperemia peripheral arterial tonometry index ≤2.0. Accelerated or delayed physiologic aging was calculated by the Δ age (ECG‐derived artificial intelligence–estimated age minus chronological age), and the association between Δ age and PMEF as well as its impact on composite major adverse cardiovascular events were investigated. Δ age was higher in patients with abnormal PMEF than in patients with normal PMEF (2.3±7.8 versus 0.5±7.7 years; P=0.01). Reactive hyperemia peripheral arterial tonometry index was negatively associated with Δ age after adjustment for cardiovascular risk factors (standardized β coefficient, –0.08; P=0.048). The highest quartile of Δ age was associated with an increased risk of major adverse cardiovascular events compared with the first quartile of Δ age in patients with abnormal PMEF, even after adjustment for cardiovascular risk factors (hazard ratio, 4.72; 95% CI, 1.24–17.91; P=0.02). Conclusions Vascular aging detected by endothelial function is associated with accelerated physiologic aging, as assessed by the artificial intelligence–ECG Δ age. Patients with endothelial dysfunction and the highest quartile of accelerated physiologic aging have a marked increase in risk for cardiovascular events.

Abstract We here posit that measurements of midlife cognition can be instructive in understanding cognitive disorders. Even though molecular events signal possible onset of cognitive disorders decades prior to their clinical diagnoses, cognition and its possible early changes in midlife remain poorly understood. We characterize midlife cognition in a cognitively healthy population‐based sample using the Cogstate Brief Battery and test for associations with cardiovascular, adiposity‐related, lifestyle‐associated, and psychosocial variables. Learning and working memory showed significant variability and vulnerability to psychosocial influences in midlife. Furthermore, midlife aging significantly and progressively increased prevalence of suboptimal cognitive performance. Our findings suggest that physiological changes in cognition, measured with simple tests suitable for use in everyday clinical setting, may signal already in midlife the first clinical manifestations of the presymptomatic biologically defined cognitive disorders. This pilot study calls for longitudinal studies investigating midlife cognition to identify clinical correlates of biologically defined cognitive disorders.

Recent reports suggest that the COVID-19 lockdown resulted in changes in mental health, however, potential age-related changes and risk factors remain unknown. We measured COVID-19 lockdown-induced stress levels and the severity of depressive symptoms prior to and during the COVID-19 lockdown in different age groups and then searched for potential risk factors in a well-characterized general population-based sample. A total of 715 participants were tested for mental distress and related risk factors at two time-points, baseline testing prior to COVID-19 and follow-up testing during COVID-19, using a battery of validated psychological tests including the Perceived Stress Scale and the Patient Health Questionnaire. Longitudinal measurements revealed that the prevalence of moderate to high stress and the severity of depressive symptoms increased 1.4- and 5.5-fold, respectively, during the COVID-19 lockdown. This surge in mental distress was more severe in women, but was present in all age groups with the older age group exhibiting, cross-sectionally, the lowest levels of mental distress prior to and during the lockdown. Illness perception, personality characteristics such as a feeling of loneliness, and several lifestyle components were found to be associated with a significant increase in mental distress. The observed changes in mental health and the identified potential risk factors underlying these changes provide critical data justifying timely and public emergency-tailored preventive, diagnostic, and therapeutic mental health interventions, which should be integrated into future public health policies globally.

Comparisons among countries can help to identify opportunities for the reduction of inequalities in cardiometabolic health. The present cross-sectional analysis and meta-analysis aim to address to what extent obesity traits, socioeconomic, and behavioral factors determine poor metabolic health across body mass index (BMI) categories in two urban population-based samples from Central Europe. Data from the CoLaus (~6,000 participants; Lausanne, Switzerland) and the Kardiovize Brno 2030 (~2,000 participants; Brno, Czech Republic) cohorts. For each cohort, logistic regression analyses were performed to identify the main determinants of poor metabolic health overall and stratified by body mass index (BMI) categories. The results of each cohort were then combined in a meta-analysis. We first observed that waist circumference and body fat mass were associated with metabolic health, especially in non-obese individuals. Moreover, increasing age, being male, having low-medium educational level, abdominal obesity, and high body fat mass were the main determinants of the metabolically unhealthy profile in both cohorts. Meta-analysis stratified by BMI categories confirmed the previous results with slight differences across BMI categories. In fact, increasing age and being male were the main determinants of poor metabolic health independent of obesity status. In contrast, low educational level and current smoking were associated with poor metabolic health only in non-obese individuals. In line, public health strategies against obesity and related comorbidities should aim to improve social conditions and to promote healthy lifestyles before the progression of metabolic disorders.

Introduction Severe obesity is a growing epidemic that causes significant morbidity and mortality, and is particularly difficult to reverse. Efficacious and cost-effective interventions are needed to combat this epidemic. This study hypothesized that obese people (body mass index (BMI) ≥35 kg/m 2 ) using a remote weight-loss program combining a mobile application, wireless scales, and low-calorie meal replacement would experience clinically significant weight loss. Methods This study was a retrospective observational analysis of 8275 individuals with a baseline BMI ≥35 kg/m 2 who used a remote weight-loss program combining mobile applications, frequent self-weighing, and calorie restriction via meal replacement for a minimum of 35 days. Weight changes were evaluated at multiple intervals (42, 60, 90, and 120 days), and weight loss was evaluated for all and for pre-specified subgroups based on demographic features and frequency of self-weighing. Results Mean weight loss at 42 days ( N = 6781) was 8.1 kg (margin of error (MOE) = 0.126 kg) with 73.6% of users experiencing >5% total body weight loss. Both men (9.1 kg; MOE = 0.172 kg; 7.9% from baseline) and women (7.1 kg; MOE = 0.179 kg; 7.2% from baseline) experienced significant weight loss. At the 120-day interval ( N = 2914), mean weight loss was 14 kg (MOE = 0.340 kg), 13% total body weight loss from baseline, and 82.3% of participants had lost >5% of their initial body weight. The decrease in body-fat percent correlated well with weight loss ( R = 0.92; p

Objectives: The paper aims to identify the priorities for cardiovascular health promotion research in Central and Eastern Europe (CEE), the region with the highest cardiovascular diseases (CVD) burden in the world. Methods: This narrative review covered peer-reviewed publications and online databases using a nonsystematic purposive approach. Results: In despite of a steady decrease in CVD burden in the region, the East-West disparities are still significant. There is minimal continuity in the past and current CVD prevention efforts in the region. Many challenges still exist, including an opportunity gap in research funding, surveillance and population-based preventive interventions. A comprehensive approach focusing on multisectoral cooperation, quality and accessibility of healthcare and equity-oriented public policies and supported by well-designed epidemiologic studies is needed to overcome these challenges. Conclusion: The current level of effort is not adequate to address the magnitude of the CVD epidemic in CEE. It is imperative to strengthen the epidemiological base concerning cardiovascular health in the region, to foster surveillance and progress in implementation of CVD preventive strategies in the most affected populations of Europe.

Ideal cardiovascular health (CVH) is defined for the presence of ideal behavioral and health metrics known to prevent cardiovascular disease (CVD). The association of circulatory phospho- and sphingo-lipids to primary reduction in cardiovascular risk is unclear. Our aim was to determine the association of CVH metrics with the circulating lipid profile of a population-based cohort. Serum sphingolipid and phospholipid species were extracted from 461 patients of the randomly selected prospective Kardiovize study based on Brno, Czech Republic. Lipids species were measured by a hyphenated mass spectrometry technique, and were associated with poor CVH scores, as defined by the American Heart Association. Phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE) species were significantly lower in ideal and intermediate scores of health dietary metric, blood pressure, total cholesterol and blood fasting glucose compared to poor scores. Current smokers presented higher levels of PC, PE and LPE individual species compared to non-smokers. Ceramide (Cer) d18:1/14:0 was altered in poor blood pressure, total cholesterol and fasting blood glucose metrics. Poor cardiovascular health metric is associated with a specific phospho- and sphingolipid pattern. Circulatory lipid profiling is a potential biomarker to refine cardiovascular health status in primary prevention strategies.

Importance. Obesity is a worsening epidemic worldwide. Effective and accessible weight loss programs to combat obesity on a large scale are warranted, but a need for frequent face-to-face care might impose a limitation. Objective. To evaluate whether individuals following a weight loss program based on a mobile application, wireless scale, and nutritional program but no face-to-face care can achieve clinically significant weight loss in a large cohort. Design. Retrospective observational analysis. Setting. China from October 2016 to December 2017. Participants. Mobile application users with a minimum of 2 weights (baseline and ≥35 days). Intervention. A commercial (Weijian Technologies) weight loss program consisting of a dietary replacement, self-monitoring using a wireless home scale, and frequent guidance via mobile application. Main Outcome. Mean weight change around 42, 60, 90, and 120 days after program initiation with subgroup analysis by gender, age, and frequency of use. Results. 251,718 individuals, with a mean age of 37.3 years (SD: 9.86) (79% female), were included with a mean weight loss of 4.3 kg (CI: ±0.02) and a mean follow-up of 120 days (SD: 76.8 days). Mean weight loss at 42, 60, 90, and 120 d was 4.1 kg (CI: ±0.02), 4.9 kg (CI: ±0.02), 5.6 kg (CI: ±0.03), and 5.4 kg (CI: ±0.04), respectively. At 120 d, 62.7% of participants had lost at least 5% of their initial weight. Both genders and all usage frequency tertiles showed statistically significant weight loss from baseline at each interval (P

Abstract Background Many studies have examined the socioeconomic variations in smoking and quitting rates across the European region; however, data from Central and East European countries, where the tobacco burden is especially high, are sparse. This study aimed to assess the patterns in current and past smoking prevalence based on cross-sectional data from a Central European urban population sample. Methods Data from 2160 respondents aged 25–64 years in Brno, Czech Republic were collected in 2013–2014 using the Czech post-MONICA survey questionnaire to assess the prevalence of cardiovascular risk factors, including smoking status. The age- and sex-stratified randomized sample was drawn using health insurance registries. Descriptive statistics and quit ratios were calculated, and chi-square and multivariate logistic analyses conducted to examine relationships between current and past smoking and demographic (age, gender, marital status) and socioeconomic variables (education, income, occupation). Results The prevalence of current and past smoking was 23.6 and 31.3 % among men and 20.5 and 23.2 % among women, respectively. Education reliably predicted smoking and quitting rates in both genders. Among men, being unemployed was associated with greater odds of smoking (OR 3.6; 1.6–8.1) and lower likelihood of quitting (OR 0.2: 0.1–0.6); the likelihood of quitting also increased with age (OR 1.8; 1.2–2.8). Among women, marital status (being married) decreased the odds of current smoking (OR 0.6; 0.4–0.9) and increased the odds of quitting (OR 2.2; 1.2–3.9). Quit ratios were the lowest in the youngest age group (25–34 years) where quitting was more strongly associated with middle income (OR 2.7; 95 % CI 1.2–5.9) than with higher education (OR 2.9; 95 % CI 0.9–8.2). Conclusions Interventions to increase cessation rates and reduce smoking prevalence need to be gender-specific and carefully tailored to the needs of the disadvantaged groups of the population, especially the less well-off young adults. Future studies should examine the equity impact of the tobacco control policies and be inclusive of the Central and East European countries.

BackgroundPrognosis based on body fat percentage (BF%) in patients with coronary artery disease has not been extensively studied. We tested the hypothesis that patients with coronary artery disease and increased BF% have a higher risk for major adverse cardiovascular events (MACEs) and that fat‐free mass is associated with better prognosis. Methods and ResultsWe included 717 patients referred to cardiac rehabilitation after coronary artery disease events or procedures who underwent air displacement plethysmography to assess BF%; 75% were men, with a mean age 61.4±11.4 years and a mean body mass index of 30±5.4 kg/m2. Follow‐up was performed using a record linkage system. Patients were classified in sex‐specific quartiles of BF% and fat‐free mass index. The composite outcome of MACEs included acute coronary syndromes, coronary revascularization, stroke, or death from any cause. After a median follow‐up of 3.9 years, 201 patients had a MACE. After adjusting for covariates, body mass index was not associated with MACEs (P=0.12). However, the risk of MACEs for those in the highest BF% quartile was nearly double when compared with those in the lowest quartile (hazard ratio, 1.89; 95% confidence interval, 1.30–2.77; P=0.0008). In contrast, fat‐free mass was inversely associated with MACEs. The risk of MACEs for those in the fourth fat‐free mass quartile was lower (adjusted hazard ratio, 0.53; 95% confidence interval, 0.35–0.82; P=0.004), when compared with those in the first quartile. ConclusionsIn patients with coronary artery disease, there is no obesity paradox when measuring BF% instead of body mass index. BF% is associated with a higher risk of MACEs, whereas fat‐free mass is associated with a lower risk of MACEs. Body mass index was not associated with MACEs.

BackgroundHeart rate (HR) recovery has been investigated in specific patient cohorts, but there is less information about the role of HR recovery in general populations. We investigated whether HR recovery has long‐term prognostic significance in primary prevention. Methods and ResultsExercise tests performed between 1993 and 2010 on patients aged 30 to 79 years without cardiovascular disease were included. Mortality was determined from Mayo Clinic records and Minnesota Death Index. Total, cardiovascular, and non‐cardiovascular mortality was reported according to HR recovery

BackgroundParticipation in cardiac rehabilitation (CR) is an essential component of care for patients with coronary artery disease. However, little is known about its benefit on cardiovascular outcomes in patients with diabetes mellitus (DM) who have undergone percutaneous coronary intervention. The aim of our study was to evaluate the impact of CR in this high‐risk group of patients. Methods and ResultsWe performed a retrospective analysis of all patients with DM who underwent percutaneous coronary intervention in Olmsted County (Minnesota) between 1994 and 2010, assessing the impact of CR participation on clinical outcomes. CR participation was significantly lower in patients with DM (38%, 263/700) compared with those who did not have DM (45%, 1071/2379; P=0.004). Using propensity score adjustment, we found that in patients with DM, CR participation was associated with significantly reduced all‐cause mortality (hazard ratio, 0.56; 95% confidence interval, 0.39–0.80; P=0.002) and composite end point of mortality, myocardial infarction, or revascularization (hazard ratio, 0.77; 95% confidence interval, 0.60–0.98; P=0.037), during a median follow‐up of 8.1 years. In patients without DM, CR participation was associated with a significant reduction in all‐cause mortality (hazard ratio, 0.67; 95% confidence interval, 0.55–0.82; P