Screening for Cardiovascular Disease Risk With Electrocardiography: US Preventive Services Task Force Recommendation Statement

Effect of Intensive Blood Pressure Lowering on the Risk of Incident Silent Myocardial Infarction: A Post Hoc Analysis of a Randomized Controlled Trial

BACKGROUND

Silent myocardial infarction (SMI) frequently goes undetected, yet it is associated with increased cardiovascular morbidity and mortality. The impact of intensive systolic blood pressure (SBP) lowering on the risk of SMI in those with hypertension remains uncertain.

METHODS

In this post hoc analysis of the Systolic Blood Pressure Intervention Trial (SPRINT), participants with serial electrocardiograms (ECGs) during the trial were included. SPRINT investigated the benefit of intensive SBP lowering, aiming for < 120 mmHg compared to the standard SBP goal of < 140 mmHg. Incident SMI was defined as evidence of new MI on an ECG without adjudicated recognized myocardial infarction (RMI).

RESULTS

During a median follow-up of 3.9 years, a total of 234 MI events (55 SMI and 179 RMI) occurred. Intensive, compared to standard, SBP lowering resulted in a lower rate of SMI (incidence rate 1.1 vs. 2.3 cases per 1000 person-years, respectively; HR [95% CI]: 0.48 [0.27-0.84]). Similarly, intensive, compared to standard, BP lowering reduced the risk of RMI (incidence rate 4.6 vs. 6.5 cases per 1000 person-years, respectively; HR [95% CI]: 0.71 [0.52-0.95]). No significant differences were noted between the strength of the association of intensive BP control on lowering the risk of SMI and RMI (p-value for HR differences = 0.23).

CONCLUSIONS

This study shows that in adults with hypertension, the benefits of intensive SBP lowering, compared with standard BP lowering, go beyond the prevention of RMI to include the prevention of SMI.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01206062.

[Prevalence and incidence of electrocardiographic abnormalities in healthy population older than 49]

PURPOSE

To determine the prevalence and incidence of electrocardiographic abnormalities (EKG-abn).

MATERIAL AND METHODS

Design. 2 stage study: 1st: descriptive cross-sectional, 2nd: prospective cohort.

LOCATION

28 urban primary health centers in Barcelona city area.

PARTICIPANTS

The study population was composed by patients who had participated in the population based ARTPER multicentric study. This study has two phases where each patient had one EKG, physical examination and blood test to obtain the secondary variables. The available EKG from both phases were reviewed, achieving a total of 2,727 patients with EKG. 601 (22%) individuals with CV disease were excluded. A total of 2.126 patients were studied of whom 392 (18%) had 2 EKG, one at each phase. Mean measurements. The association between the presence of EKG-abn and explanatory variables was performed using multivariate logistic regression, obtaining odds ratios (OR) and their 95% confidence intervals (95%CI).

RESULTS

58% women and average age 66years. 32% (95%CI: 30%-34%) (n=671) presented EKG-abn of whom 60% (n=402) were minor and 40% (n=269) major disorders. The most common EKG-abn were the conduction disturbances. EKG-abn were associated with males (OR: 1.7; 95%CI: 1.4-2.1) and patients older than 65 (OR: 1.6; 95%CI: 1.4-2.0). Among the 345 people without EKG-abn at baseline and with 2 available EKG, 71 showed an EKG-abn at the second EKG (incidence in 4.7years of 21% (95%CI: 16%-25%).

CONCLUSION

EKG abnormalities are common in healthy general population. EKG-abn are more common in male and elderly patients.

Cardiovascular risk and its influencing factors during exercise in apparently healthy Chinese adult population

BACKGROUND

There are few studies on the safety of sub-maximal exercise testing of aerobic exercise in apparently healthy Chinese populations. The purpose of this study was to explore the frequency of exercise electrocardiography (ECG) abnormalities and the corresponding exercise intensities, as well as the associated influencing factors, during a symptom-limited stepwise incremental cardiopulmonary exercise test (CPET) in an apparently healthy Chinese population.

METHODS

A cross-sectional study was done in four communities, one urban and one rural in the North (Beijing) and in the South (Hezhou, Guangxi) of China from 1 January 2017 to 31 December 2018, respectively. Total of 1642 participants was recruited, 918 were eligible and completed demographic indicators, routine blood indicators, physical activity status, symptom-limited CPET and exercise ECG were included in the analysis.

RESULTS

Of the exercise ECG outcomes, 10 (1.1%) were positive and occurred at exercise intensities ≥ 62.50% heart rate reserve (HRR); 44 (4.8%) were equivocal and 864 (94.1%) were normal. Individuals with Cardiovascular Disease Risk Factor (CVDRF) = 3-4 were 2.6 times more likely to have a equivocal and abnormal exercise ECG than those with CVDRF = 0-2. Exercise ECGs of individuals with CVDRF = 5-7 were 5.4 times more likely to be positive and abnormal than exercise ECGs of individuals with CVDRF = 0-2.

CONCLUSIONS

The exercise intensity of 62.5% HRR can be used as a safe upper limit for safe participation in exercise in apparently healthy Chinese population; the greater the number of CVDRFs, the greater the likelihood of cardiovascular risk during exercise.

Routine Electrocardiogram Screening and Cardiovascular Disease Events in Adults

Importance

The resting electrocardiogram (ECG) is commonly performed for cardiovascular disease (CVD) screening purposes in Japan. However, evidence is limited regarding the prognostic significance of ECG in clinical practice settings.

Objective

To investigate the association between ECG abnormalities and CVD outcomes in a working-age population.

Design, Setting, and Participants

This nationwide cohort study included individuals aged 35 to 65 years from the Japan Health Insurance Association database, which covers approximately 40% (30 million) of the working-age population in Japan. Data from April 1, 2015, to March 31, 2022, were included, and analysis was conducted from October 1, 2022, to April 11, 2024.

Exposures

Baseline ECG status (normal, 1 minor abnormality, ≥2 minor abnormalities, or major abnormality).

Main Outcomes and Measures

The primary outcome was a composite of overall death and CVD hospital admission due to myocardial infarction, stroke, or heart failure. The secondary outcome was developing a new major ECG abnormality over the years of screening.

Results

Of 3 698 429 individuals enrolled in the nationwide annual health check program (mean [SD] age, 47.1 [8.5] years; 66.6% male), 623 073 (16.8%) had 1 minor ECG abnormality, 144 535 (3.9%) had 2 or more minor ECG abnormalities, and 56 921 (1.5%) had a major ECG abnormality. During a median follow-up of 5.5 (IQR, 3.4-5.7) years, baseline ECG abnormality was independently associated with an increased incidence of the composite end points of overall death and CVD admission compared with normal ECG (incidence rates per 10 000 person-years: 92.7 [95% CI, 92.2-93.2] for normal ECG, 128.5 [95% CI, 127.2-129.9] for 1 minor ECG abnormality, 159.7 [95% CI, 156.6-162.9] for ≥2 minor ECG abnormalities, and 266.3 [95% CI, 259.9-272.3] for a major ECG abnormality; adjusted hazard ratios: 1.19 [95% CI, 1.18-1.20] for 1 minor ECG abnormality, 1.37 [95% CI, 1.34-1.39] for ≥2 minor ECG abnormalities, and 1.96 [95% CI, 1.92-2.02] for a major ECG abnormality). Furthermore, the presence and number of minor ECG abnormalities were associated with an increased incidence of developing new major ECG abnormalities (incidence rates per 10 000 person-years: 85.1 [95% CI, 84.5-85.5] for normal ECG, 217.2 [95% CI, 215.5-219.0] for 1 minor ECG abnormality, and 306.4 [95% CI, 302.1-310.7] for ≥2 minor ECG abnormalities; and adjusted hazard ratios: 2.52 [95% CI, 2.49-2.55] for 1 minor ECG abnormality and 3.61 [95% CI, 3.55-3.67] for ≥2 minor ECG abnormalities). Associations were noted regardless of baseline CVD risk.

Conclusions and Relevance

The findings of this study suggest that the potential role of routine ECG screening for early prevention of CVD events, along with the optimal follow-up strategy, should be examined in future studies.

Screening Sportsmen and Sportswomen Over Age 35: The Relevance of an Exercise Electrocardiogram. Data From the SEEPRED Study

INTRODUCTION

The importance of exercise electrocardiogram (ECG) is still controversial in the prevention of cardiovascular events among sportsmen and sportswomen. The aim of this study was to assess the relevance of exercise ECG as a screening tool to prevent cardiovascular events when any cardiovascular disease (CVD) risk factors are present.

METHODS

The study included leisure time asymptomatic sportsmen and sportswomen over age 35 evaluated from 2011 to 2016 at the University Hospital of Saint-Etienne (France). Major adverse cardiovascular events (MACE) and atrial fibrillation were collected at 3 years.

RESULTS

Of the cohort of 2457 sportsmen and sportswomen (mean age 50.2 ± 9.4 years), 50 (2%) had a high-risk SCORE2. A total of 256 exercise ECGs (10%) were defined as positive, most of them due to silent myocardial ischemia (SMI) (n = 196; 8%). These 196 SMI cases led to 33 coronary angiograms (1%), which revealed 23 significant coronary stenoses requiring revascularization. In multivariate logistic regression analysis, having at least two CVD risk factors was independently associated with (1) positive exercise ECG (OR = 1.80 [95% CI: 1.29-2.52], p = 0.0006), with (2) suspected SMI (OR = 2.57 [95% CI: 1.10-6.02], p = 0.0304), with (3) confirmed SMI (OR = 8.20 [95% CI: 3.46-19.46], p < 0.0001) and with (4) cardiovascular events (MACE or atrial fibrillation) (OR = 6.95 [95% CI: 3.49-13.81], p < 0.0001) at 3 years (median).

CONCLUSIONS

The study supports the European recommendations for the use of exercise ECG in evaluation of asymptomatic leisure time sportsmen over age 35. Having at least two CVD risk factors was the best predictor for presence of coronary artery stenosis that may increase the risk for adverse events.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT06024863.

Artificial intelligence-enhanced electrocardiography analysis as a promising tool for predicting obstructive coronary artery disease in patients with stable angina

Aims

The clinical feasibility of artificial intelligence (AI)-based electrocardiography (ECG) analysis for predicting obstructive coronary artery disease (CAD) has not been sufficiently validated in patients with stable angina, especially in large sample sizes.

Methods and results

A deep learning framework for the quantitative ECG (QCG) analysis was trained and internally tested to derive the risk scores (0-100) for obstructive CAD (QCGObstCAD) and extensive CAD (QCGExtCAD) using 50 756 ECG images from 21 866 patients who underwent coronary artery evaluation for chest pain (invasive coronary or computed tomography angiography). External validation was performed in 4517 patients with stable angina who underwent coronary imaging to identify obstructive CAD. The QCGObstCAD and QCGExtCAD scores were significantly increased in the presence of obstructive and extensive CAD (all P < 0.001) and with increasing degrees of stenosis and disease burden, respectively (all P trend < 0.001). In the internal and external tests, QCGObstCAD exhibited a good predictive ability for obstructive CAD [area under the curve (AUC), 0.781 and 0.731, respectively] and severe obstructive CAD (AUC, 0.780 and 0.786, respectively), and QCGExtCAD exhibited a good predictive ability for extensive CAD (AUC, 0.689 and 0.784). In the external test, the QCGObstCAD and QCGExtCAD scores demonstrated independent and incremental predictive values for obstructive and extensive CAD, respectively, over that with conventional clinical risk factors. The QCG scores demonstrated significant associations with lesion characteristics, such as the fractional flow reserve, coronary calcification score, and total plaque volume.

Conclusion

The AI-based QCG analysis for predicting obstructive CAD in patients with stable angina, including those with severe stenosis and multivessel disease, is feasible.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram

BACKGROUND

Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling.

METHODS

This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices.

RESULTS

The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]).

CONCLUSION

ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Association Between ECG Abnormalities and Mortality in a Low-Risk Population

BACKGROUND

The ECG is a simple, noninvasive screening method for cardiovascular disease and arrhythmia. The impact of ECG abnormality on mortality is not certain in low-risk populations. To address this, we evaluated the association between ECG abnormality and mortality.

METHODS AND RESULTS

We retrospectively assessed baseline ECG and all-cause mortality and cardiovascular mortality in 660 383 patients presenting for medical check-ups. Baseline ECG abnormalities were classified according to the Minnesota Code. Among the total 660 383 participants, 23 609 (3.6%) had major and 110 038 (16.7%) had minor ECG abnormalities. All-cause mortality occurred in 7751 patients (1.1%) and cardiovascular mortality in 1180 (0.18%) over a median follow-up period of 8.8 years. Major ECG abnormalities were associated with all-cause mortality (hazard ratio [HR], 1.11 [95%, 1.03-1.2]) and cardiovascular mortality (HR, 1.92 [95% CI, 1.63-2.27]) compared with no ECG abnormalities. All-cause mortality was associated with right atrial enlargement (HR, 2.11 [95% CI, 1.1-4.07]), left atrial enlargement (HR, 1.76 [95% CI, 1.1-2.84]), sinus tachycardia (HR, 1.52 [95% CI, 1.15-2.01]), complete atrioventricular block (HR, 2.1 [95% CI, 1.05-4.2]), atrial fibrillation (HR, 1.52 [95% CI, 1.26-1.84]), and left ventricular hypertrophy (HR, 1.15 [95% CI, 1.02-1.3]). Cardiovascular mortality was associated with left atrial enlargement (HR, 4.52 [95% CI, 2.15-9.5]), atrial fibrillation (HR, 3.22 [95% CI, 2.33-4.46]), left ventricular hypertrophy (HR, 1.72 [95% CI, 1.35-2.19]), major Q-wave abnormality (HR, 1.6 [95% CI, 1.08-2.39]), and major ST-T abnormality (HR, 1.76 [95% CI, 1.01-3.04]).

CONCLUSIONS

ECG abnormalities, including left atrial enlargement, left ventricular hypertrophy, atrial fibrillation, and major Q-wave and ST-T abnormalities, were associated with cardiovascular mortality in a low-risk population.

Arrhythmias and Sudden Cardiac Death

Ventricular tachyarrhythmias remain a major cause of sudden cardiac arrest (SCA) that leads to sudden cardiac death (SCD). Primary prevention strategies to prevent SCD include promoting a healthy lifestyle, following United States Preventive Service Task Force recommendations related to cardiovascular disease, and controlling comorbid conditions. For a patient experiencing SCA, early cardiopulmonary resuscitation and defibrillation should be performed. Implantable cardioverter defibrillators are more effective at secondary prevention compared with drug therapy but medications such as amiodarone, beta-blockers, and sotalol may be helpful adjuncts to reduce the risk of SCD or improve a patient's symptoms (eg, palpitations and inappropriate defibrillator shocks).

The Danish Nationwide Electrocardiogram (ECG) Cohort

The electrocardiogram (ECG) is a non-invasive diagnostic tool holding significant clinical importance in the diagnosis and risk stratification of cardiac disease. However, access to large-scale, population-based digital ECG data for research purposes remains limited and challenging. Consequently, we established the Danish Nationwide ECG Cohort to provide data from standard 12-lead digital ECGs in both pre- and in-hospital settings, which can be linked to comprehensive Danish nationwide administrative registers on health and social data with long-term follow-up. The Danish Nationwide ECG Cohort is an open real-world cohort including all patients with at least one digital pre- or in-hospital ECG in Denmark from January 01, 2000, to December 31, 2021. The cohort includes data on standardized and uniform ECG diagnostic statements and ECG measurements including global parameters as well as lead-specific measures of waveform amplitudes, durations, and intervals. Currently, the cohort comprises 2,485,987 unique patients with a median age at the first ECG of 57 years (25th-75th percentiles, 40-71 years; males, 48%), resulting in a total of 11,952,430 ECGs. In conclusion, the Danish Nationwide ECG Cohort represents a novel and extensive population-based digital ECG dataset for cardiovascular research, encompassing both pre- and in-hospital settings. The cohort contains ECG diagnostic statements and ECG measurements that can be linked to various nationwide health and social registers without loss to follow-up.

Correlates and consequences of atrial fibrillation in a prospective study of 25 000 participants in the China Kadoorie Biobank

Aims

The prevalence of atrial fibrillation (AF) is positively correlated with prior cardiovascular diseases (CVD) and CVD risk factors but is lower in Chinese than Europeans despite their higher burden of CVD. We examined the prevalence and prognosis of AF and other electrocardiogram (ECG) abnormalities in the China Kadoorie Biobank.

Methods and results

A random sample of 25 239 adults (mean age 59.5 years, 62% women) had a 12-lead ECG recorded and interpreted using a Mortara VERITAS™ algorithm in 2013-14. Participants were followed up for 5 years for incident stroke, ischaemic heart disease, heart failure (HF), and all CVD, overall and by CHA2DS2-VASc scores, age, sex, and area. Overall, 1.2% had AF, 13.6% had left ventricular hypertrophy (LVH), and 28.1% had ischaemia (two-thirds of AF cases also had ischaemia or LVH). The prevalence of AF increased with age, prior CVD, and levels of CHA₂DS₂-VASc scores (0.5%, 1.3%, 2.1%, 2.9%, and 4.4% for scores <2, 2, 3, 4, and ≥5, respectively). Atrial fibrillation was associated with two-fold higher hazard ratios (HR) for CVD (2.15; 95% CI, 1.71-2.69) and stroke (1.88; 1.44-2.47) and a four-fold higher HR for HF (3.79; 2.21-6.49). The 5-year cumulative incidence of CVD was comparable for AF, prior CVD, and CHA₂DS₂-VASc scores ≥ 2 (36.7% vs. 36.2% vs. 37.7%, respectively) but was two-fold greater than for ischaemia (19.4%), LVH (18.0%), or normal ECG (14.1%), respectively.

Conclusion

The findings highlight the importance of screening for AF together with estimation of CHA₂DS₂-VASc scores for prevention of CVD in Chinese adults.

An association between heart rate variability and incident heart failure in an elderly cohort

BACKGROUND

Early identification of individuals at risk of developing heart failure (HF) may improve poor prognosis. A dominant sympathetic activity is common in HF and associated with worse outcomes; however, less is known about the autonomic balance before HF.

HYPOTHESIS

A low frequency/high frequency (L-F/H-F) ratio, index of heart rate variability, and marker of the autonomic balance predict the development of HF and may improve the performance of the HF prediction model when added to traditional cardiovascular (CV) risk factors.

METHODS

Individuals in the PIVUS (Prospective Investigation of the Vasculature in Uppsala Seniors) study (n = 1016, all aged 70 years) were included. Exclusion criteria were prevalent HF, electrocardiographic QRS duration ≥130 millisecond, major arrhythmias, or conduction blocks at baseline. The association between the L-F/H-F ratio and incident HF was assessed using Cox proportional hazard analysis. The C-statistic evaluated whether adding the L-F/H-F-ratio to traditional CV risk factors improved the discrimination of incident HF.

RESULTS

HF developed in 107/836 study participants during 15 years of follow-up. A nonlinear, inverse association between the L-F/H-F ratio and incident HF was mainly driven by an L-F/H-F ratio of <30. The association curve was flat for higher values (hazard ratio, HR for the total curve = 0.78 [95% confidence interval, CI: 0.69-0.88, p < .001]; HR = 2 for L-F/H-F ratio = 10). The traditional prediction model improved by 3.3% (p < .03) when the L-F/H-F ratio was added.

CONCLUSIONS

An L-F/H-F ratio of <30 was related to incident HF and improved HF prediction when added to traditional CV risk factors.

Evaluation of Wrist-Worn Photoplethysmography Trackers with an Electrocardiogram in Patients with Ischemic Heart Disease: A Validation Study

PURPOSE

Photoplethysmography measurement of heart rate with wrist-worn trackers has been introduced in healthy individuals. However, additional consideration is necessary for patients with ischemic heart disease, and the available evidence is limited. The study aims to evaluate the validity and reliability of heart rate measures by a wrist-worn photoplethysmography (PPG) tracker compared to an electrocardiogram (ECG) during incremental treadmill exercise among patients with ischemic heart disease.

METHODS

Fifty-one participants performed the standard incremental treadmill exercise in a controlled laboratory setting with 12-lead ECG attached to the patient's body and wearing wrist-worn PPG trackers.

RESULTS

At each stage, the absolute percentage error of the PPG was within 10% of the standard acceptable range. Further analysis using a linear mixed model, which accounts for individual variations, revealed that PPG yielded the best performance at the baseline low-intensity exercise. As the stages progressed, heart rate validity decreased but was regained during recovery. The reliability was moderate to excellent.

CONCLUSIONS

Low-cost trackers AMAZFIT Cor and Bip validity and reliability were within acceptable ranges, especially during low-intensity exercise among patients with ischemic heart disease recovering from cardiac procedures. Though using the tracker as part of the diagnosis tool still requires more supporting studies, it can potentially be used as a self-monitoring tool with precautions.

Abnormal Glucose Tolerance in Women Diagnosed With Gestational Diabetes (WHO 2013) 10 Years After Index Pregnancy

Context

It is not clear if the risk of abnormal glucose tolerance (AGT) is attenuated in the long-term in women diagnosed with gestational diabetes (GDM) using the World Health Organization (WHO) 2013 criteria and who have received appropriate treatment during pregnancy.

Objective

We aimed to assess the long-term prevalence of AGT and other cardiovascular disease (CVD) risk factors in this cohort.

Methods

A retrospective cohort follow-up study was conducted of 37 and 107 women diagnosed with and without GDM respectively using the WHO 2013 criteria between June 2010 and December 2010. Women were invited to attend our center, where they underwent a 75-g oral glucose tolerance test, blood and urine collection, body measurements, and electrocardiography. Main outcome measure included the development of AGT using the American Diabetes Association criteria.

Results

Sixteen (43.2%) women with GDM compared to 5 (4.7%) women with normal glucose tolerance (NGT) at index pregnancy had AGT (P < .001). In the GDM group, 10 (27.0%), 7 (18.9%), and 4 (10.8%) women had impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and type 2 diabetes mellitus (T2DM), respectively. In the NGT group, 2 (1.9%), 3 (2.8%), and 1 (0.9%) woman had IFG, IGT, and T2DM, respectively. Women with AGT also had an unfavorable metabolic profile including obesity, hypertension, insulin resistance, and dyslipidemia.

Conclusion

Women treated for GDM (WHO 2013 criteria) remain at increased risk for developing AGT and adverse CVD risk factors as early as a decade after diagnosis. Continued efforts are needed to accurately follow this population to address modifiable risk factors.

Targeting Hypertensive Response to Exercise and the Association of Masked Hypertension With Subclinical Organ Damage: A Mini-Review and Meta-Analysis

BACKGROUND

Emerging evidence suggests that a hypertensive response to exercise (HRE) during dynamic or isometric stress tests assessing cardiac function is predictive of hypertension and cardiovascular events such coronary artery disease, heart failure and stroke. Whether HRE represents a marker of masked hypertension (MH) in individuals with no prior history of hypertension is still unclear. This is also the case for the association between MH and hypertension-mediated organ damage (HMOD) in the HRE setting.

METHODS

We addressed this issue through a review and a meta-analysis of studies providing data on this topic in normotensive individuals undergone both to dynamic or static exercise and to 24-h blood pressure monitoring (ABPM). A systematic search was performed using Pub-Med, OVID, EMBASE and Cochrane library databases from inception up to February 28th 2023.

RESULTS

Six studies including a total of 1,155 untreated clinically normotensive individuals were considered for the review. Data provided by the selected studies can be summarized as follows: (i) HRE is a BP phenotype linked to a high prevalence of MH (27.3% in the pooled population); (ii) MH is, in turn, associated with a greater, consistent likelihood of echocardiographic left ventricular hypertrophy (OR: 4.93, CI: 2.16-12.2, P < 0.0001) and vascular organ damage, as assessed by pulse wave velocity, (SMD: 0.34 ± 0.11, CI: 0.12-0.56, P = 0002).

CONCLUSIONS

On the basis of this, albeit limited, evidence, the diagnostic work-up in individuals with HRE should primarily be addressed to look for MH as well as for markers of HMOD, a highly prevalent alteration in MH.

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Objective.Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events.Approach.The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed.Main Results.The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals 'at risk' and 'not at risk,' respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model.Significance.This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action

Despite major advancements in cardiovascular medicine, sudden cardiac death (SCD) continues to be an enormous medical and societal challenge, claiming millions of lives every year. Efforts to prevent SCD are hampered by imperfect risk prediction and inadequate solutions to specifically address arrhythmogenesis. Although resuscitation strategies have witnessed substantial evolution, there is a need to strengthen the organisation of community interventions and emergency medical systems across varied locations and health-care structures. With all the technological and medical advances of the 21st century, the fact that survival from sudden cardiac arrest (SCA) remains lower than 10% in most parts of the world is unacceptable. Recognising this urgent need, the Lancet Commission on SCD was constituted, bringing together 30 international experts in varied disciplines. Consistent progress in tackling SCD will require a completely revamped approach to SCD prevention, with wide-sweeping policy changes that will empower the development of both governmental and community-based programmes to maximise survival from SCA, and to comprehensively attend to survivors and decedents' families after the event. International collaborative efforts that maximally leverage and connect the expertise of various research organisations will need to be prioritised to properly address identified gaps. The Commission places substantial emphasis on the need to develop a multidisciplinary strategy that encompasses all aspects of SCD prevention and treatment. The Commission provides a critical assessment of the current scientific efforts in the field, and puts forth key recommendations to challenge, activate, and intensify efforts by both the scientific and global community with new directions, research, and innovation to reduce the burden of SCD worldwide.

Holistic Approaches to Arrhythmia Management: Combining Medication, Ablation, and Device Interventions

This narrative review investigates the severe health issue of arrhythmias, which affects millions of people worldwide. A multifaceted strategy incorporating medicine, catheter ablation, and advanced device interventions is necessary to manage these disorders effectively. Medication is the cornerstone, and as antiarrhythmic medications develop, their efficacy and side effects are reduced. Success depends on having individualized treatment strategies that consider patient profiles and arrhythmia type. Catheter ablation, a minimally invasive surgery that targets and removes faulty heart electrical circuits, has become a potent therapy when drugs are ineffective. Technological developments, including high-resolution mapping systems and customized catheters, improve precision. Pacemakers and implantable cardioverter-defibrillators (ICDs) are two examples of implantable cardiac devices essential to managing all types of arrhythmias. Pacemakers provide a regular heartbeat when the body's natural pacing mechanism fails. At the same time, ICDs, with cutting-edge algorithms, can identify and stop life-threatening arrhythmias and offer high-risk patients vital protection. As device technology advances, smaller, more durable devices become available, improving patient comfort and lowering the need for replacements. The seamless fusion of these three strategies is where holistic arrhythmia management shines. Even for difficult instances, customized combination therapy combining medicine, ablation, and device interventions offers complete solutions. Healthcare providers must collaborate for this integrated strategy to deliver personalized, efficient, and holistic care. In conclusion, the management of arrhythmias has developed into a dynamic, synergistic discipline where drugs, catheter ablation, and devices all work in concert to deliver comprehensive care. For those with arrhythmias, a patient-centered strategy that considers their particular patient features and best integrates different modalities can significantly enhance their quality of life. The effectiveness and accessibility of holistic arrhythmia management could be further improved because of ongoing developments in these fields, which is encouraging for patients and medical professionals.

Feasibility of Artificial Intelligence-Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study

BACKGROUND

Despite accumulating research on artificial intelligence-based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated.

OBJECTIVE

We evaluated the utility of an existing artificial intelligence-based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina.

METHODS

This single-center study comprised consecutive patients with stable angina. The independent and incremental value of QCG scores for coronary artery disease (CAD)-related conditions (ACS, myocardial injury, critical status, ST-elevation myocardial infarction, and left ventricular dysfunction) for predicting obstructive CAD confirmed by invasive angiography was examined. Additionally, ECG signals extracted by the QCG analyzer were used as input to develop a new QCG score.

RESULTS

Among 723 patients with stable angina (median age 68 years; male: 470/723, 65%), 497 (69%) had obstructive CAD. QCG scores for ACS and myocardial injury were independently associated with obstructive CAD (odds ratio [OR] 1.09, 95% CI 1.03-1.17 and OR 1.08, 95% CI 1.02-1.16 per 10-point increase, respectively) but did not significantly improve prediction performance compared to clinical features. However, our new QCG score demonstrated better prediction performance for obstructive CAD (area under the receiver operating characteristic curve 0.802) than the original QCG scores, with incremental predictive value in combination with clinical features (area under the receiver operating characteristic curve 0.827 vs 0.730; P<.001).

CONCLUSIONS

QCG scores developed for acute conditions show limited performance in identifying obstructive CAD in stable angina. However, improvement in the QCG analyzer, through training on comprehensive ECG signals in patients with stable angina, is feasible.

Extensive deep learning model to enhance electrocardiogram application via latent cardiovascular feature extraction from identity identification

BACKGROUND AND OBJECTIVE

Deep learning models (DLMs) have been successfully applied in biomedicine primarily using supervised learning with large, annotated databases. However, scarce training resources limit the potential of DLMs for electrocardiogram (ECG) analysis.

METHODS

We have developed a novel pre-training strategy for unsupervised identity identification with an area under the receiver operating characteristic curve (AUC) >0.98. Accordingly, a DLM pre-trained with identity identification can be applied to 70 patient characteristic predictions using transfer learning (TL). These ECG-based patient characteristics were then used for cardiovascular disease (CVD) risk prediction. The DLMs were trained using 507,729 ECGs from 222,473 patients and validated using two independent validation sets (n = 27,824/31,925).

RESULTS

The DLMs using our method exhibited better performance than directly trained DLMs. Additionally, our DLM performed better than those of previous studies in terms of gender (AUC [internal/external] = 0.982/0.968), age (correlation = 0.886/0.892), low ejection fraction (AUC = 0.942/0.951), and critical markers not addressed previously, including high B-type natriuretic peptide (AUC = 0.921/0.899). Additionally, approximately 50% of the ECG-based characteristics provided significantly more prediction information for cardiovascular risk than real characteristics.

CONCLUSIONS

This is the first study to use identity identification as a pre-training task for TL in ECG analysis. An extensive exploration of the relationship between ECG and 70 patient characteristics was conducted. Our DLM-enhanced ECG interpretation system extensively advanced ECG-related patient characteristic prediction and mortality risk management for cardiovascular diseases.

Bimodal CNN for cardiovascular disease classification by co-training ECG grayscale images and scalograms

This study aimed to develop a bimodal convolutional neural network (CNN) by co-training grayscale images and scalograms of ECG for cardiovascular disease classification. The bimodal CNN model was developed using a 12-lead ECG database collected from Chapman University and Shaoxing People's Hospital. The preprocessed database contains 10,588 ECG data and 11 heart rhythms labeled by a specialist physician. The preprocessed one-dimensional ECG signals were converted into two-dimensional grayscale images and scalograms, which are fed simultaneously to the bimodal CNN model as dual input images. The proposed model aims to improve the performance of CVDs classification by making use of ECG grayscale images and scalograms. The bimodal CNN model consists of two identical Inception-v3 backbone models, which were pre-trained on the ImageNet database. The proposed model was fine-tuned with 6780 dual-input images, validated with 1694 dual-input images, and tested on 2114 dual-input images. The bimodal CNN model using two identical Inception-v3 backbones achieved best AUC (0.992), accuracy (95.08%), sensitivity (0.942), precision (0.946) and F1-score (0.944) in lead II. Ensemble model of all leads obtained AUC (0.994), accuracy (95.74%), sensitivity (0.950), precision (0.953), and F1-score (0.952). The bimodal CNN model showed better diagnostic performance than logistic regression, XGBoost, LSTM, single CNN model training with grayscale images alone or with scalograms alone. The proposed bimodal CNN model would be of great help in diagnosing cardiovascular diseases.

Carotid and femoral bruits as cardiovascular risk indicators in a middle-aged Finnish population: A 20-year prospective study

BACKGROUND

Effective treatment and prevention of cardiovascular (CV) diseases requires reliable methods of assessing individual CV event risk. Although standardized risk calculators like Systematic Coronary Risk Evaluation (SCORE) are sufficient in most instances, sometimes more specific clinical examination is needed to determine the most optimal intervention and its intensity.

AIM

To study whether carotid and femoral bruits provide prognostic information on CV events, CV mortality and all-cause mortality beyond traditional CV risk factors.

METHODS

1045 subjects (49.8% men), aged 51.3 ± 5.97 years were clinically examined in the beginning of 1990's. The subjects were followed for over 20 years and data on CV events and causes of deaths was collected.

RESULTS

During the follow-up period, 241 (23.1%) of the subjects died and 82 (34.6%) of the deaths were of CV origin. Carotid bruits were a significant risk factor for CV deaths only if subjects with previous CV events were included. After adjusting for age, sex, systolic blood pressure, smoking, diabetes, LDL cholesterol, coronary artery disease and stroke, carotid bruits posed a hazard ratio (HR) (95% confidence interval) of 4.15 (2.39-8.52) p<0.001 for CV deaths. After excluding subjects with previous CV events (after which n = 941) neither carotid nor femoral bruits were statistically associated with CV events or all-cause mortality. Adding carotid or femoral bruits in the baseline risk model with traditional CV risk factors did not improve C-statistic, reclassification, or discrimination of the subjects.

CONCLUSIONS

Carotid and femoral bruits do not provide clinically useful information in a pure primary prevention setting. Carotid bruits might be useful in evaluating the overall CV risk in a population where recurrent CV events may occur.

Physical activity, cardiorespiratory fitness, and cardiovascular health: A clinical practice statement of the American Society for Preventive Cardiology Part II: Physical activity, cardiorespiratory fitness, minimum and goal intensities for exercise training, prescriptive methods, and special patient populations

The prescription of exercise for individuals with and without cardiovascular disease (CVD) should be scientifically-based yet adapted to the patient. This scientific statement reviews the clinical and physiologic basis for the prescription of exercise, with specific reference to the volume of physical activity (PA) and level of cardiorespiratory fitness (CRF) that confer significant and optimal cardioprotective benefits. Recommendations are provided regarding the appropriate intensity, frequency, and duration of training; the concept of MET-minutes per week; critical components of the exercise session (warm-up, conditioning phase, cool-down); methodologies for establishing the training intensity, including oxygen uptake reserve (V̇O2R), target heart rate derivation and rating perceived exertion; minimum and goal intensities for exercise training; and, types of training activities, including resistance training, adjunctive lifestyle PA, marathon/triathlon training, and high-intensity interval training. In addition, we discuss the rationale for and value of exercise training programs for patients with peripheral artery disease, diabetes mellitus, and heart failure.

Is Low-value Testing Before Low-risk Hand Surgery Associated With Increased Downstream Healthcare Use and Reimbursements? A National Claims Database Analysis

BACKGROUND

Minor hand procedures can often be completed in the office without any laboratory testing. Preoperative screening tests before minor hand procedures are unnecessary and considered low value because they can lead to preventable invasive confirmatory tests and/or procedures. Prior studies have shown that low-value testing before low-risk hand surgery is still common, yet little is known about their downstream effects and associated costs. Assessing these downstream events can elucidate the consequences of obtaining a low-value test and inform context-specific interventions to reduce their use.

QUESTIONS/PURPOSES

(1) Among healthy adults undergoing low-risk hand surgery, are patients who receive a preoperative low-value test more likely to have subsequent diagnostic tests and procedures than those who do not receive a low-value test? (2) What is the increased 90-day reimbursement associated with subsequent diagnostic tests and procedures in patients who received a low-value test compared with those who did not?

METHODS

In this retrospective, comparative study using a large national database, we queried a large health insurance provider's administrative claims data to identify adult patients undergoing low-risk hand surgery (carpal tunnel release, trigger finger release, Dupuytren fasciectomy, de Quervain release, thumb carpometacarpal arthroplasty, wrist ganglion cyst, or mass excision) between 2011 and 2017. This database was selected for its ability to track patient claims longitudinally with direct provision of reimbursement data in a large, geographically diverse patient population. Patients who received at least one preoperative low-value test, including complete blood count, basic metabolic panel, electrocardiogram, chest radiography, pulmonary function test, and urinalysis within the 30-day preoperative period, were matched with propensity scores to those who did not. Among the 73,112 patients who met our inclusion criteria (mean age 57 ± 14 years; 68% [49,847] were women), 27% (19,453) received at least one preoperative low-value test and were propensity score-matched to those who did not. Multivariable regression analyses were performed to assess the frequency and reimbursements of subsequent diagnostic tests and procedures in the 90 days after surgery while controlling for potentially confounding variables such as age, sex, comorbidities, and baseline healthcare use.

RESULTS

When controlling for covariates such as age, sex, comorbidities, and baseline healthcare use, patients in the low-value test cohort had an adjusted odds ratio of 1.57 (95% confidence interval [CI] 1.50 to 1.64; p < 0.001) for a postoperative use event (a downstream diagnostic test or procedure) compared with those who did not have a low-value test. The median (IQR) per-patient reimbursements associated with downstream utilization events in patients who received a low-value test was USD 231.97 (64.37 to 1138.84), and those who did not receive a low-value test had a median of USD 191.52 (57.1 to 899.42) (adjusted difference when controlling for covariates: USD 217.27 per patient [95% CI 59.51 to 375.03]; p = 0.007). After adjusting for inflation, total additional reimbursements for patients in the low-value test cohort increased annually.

CONCLUSION

Low-value tests generate downstream tests and procedures that are known to provide minimal benefit to healthy patients and may expose patients to potential harms associated with subsequent, unnecessary invasive tests and procedures in response to false positives. Nevertheless, low-value testing remains common and the rising trend in low-value test-associated spending demonstrates the need for multicomponent interventions that target change at both the payer and health system level. Such interventions should disincentivize the initial low-value test and the cascade that may follow. Future work to identify the barriers and facilitators to reduce low-value testing in hand surgery can inform the development and revision of deimplementation strategies.

LEVEL OF EVIDENCE

Level III, therapeutic study.

Associations between sleep duration and cardiovascular diseases: A meta-review and meta-analysis of observational and Mendelian randomization studies

The associations between sleep duration and cardiovascular diseases (CVDs) have been explored in many observational studies. However, the causality of sleep duration and many CVDs, such as coronary artery disease (CAD), heart failure (HF), and stroke, remains unclear. In this study, we conducted a systematic meta-review and meta-analysis of the results of observational and Mendelian randomization (MR) studies to examine how sleep duration impacts the risk of CVDs. We searched articles published in English and before 10 September 2021 in PubMed, Web of Science, and Embase. The articles were screened independently by two reviewers to minimize potential bias. We combined the meta-analyses of observational studies and 11 MR studies and summarized evidence of the effect of sleep duration on the risk of CAD, HF, stroke, and cardiovascular and all-cause mortality. Results showed that (a) evidence is accumulating that short sleep duration is a causal risk factor for CAD and HF; (b) abundant evidence from observational studies supports that long sleep duration is associated with the risk of CAD, stroke, and mortality, and long sleep duration has no causal associations with stroke and CAD in the MR studies; the causation of long sleep duration and other CVDs should be further studied; and (c) emerging evidence indicates that an increase in hours of sleep is associated with a decreased risk of CAD. Finally, we discussed the underlying pathophysiological mechanisms underlying short sleep duration and CVDs and suggested that increasing sleep duration benefits cardiovascular health.

Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine

Purpose

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

Design/methodology/approach

This study is a narrative review of the literature.

Findings

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

Originality/value

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

Stage 2 Hypertension and Electrocardiogram Abnormality: Evaluating the Risk Factors of Cardiovascular Diseases in Nigeria

INTRODUCTION

This study evaluated the status of stage 2 hypertension, abnormal ECG and their co-occurrence as possible risk factors of cardiovascular disease and their predictors in a Nigerian University population.

METHODS

A total of 717 subjects participated in this study. Blood pressure (BP), resting electrocardiogram (ECG) and other clinical parameters were measured and categorised according to standard organisational guidelines. Bivariate correlation and multivariate logistic regression model were used to determine covariates and clinical parameter association at a 95 % significant level.

RESULTS

Stage 2 hypertension and abnormal ECG respectively occurred in 264 (37 %) and 217 (39.2 %) subjects, with co-occurrence and abnormal BMI in 85 (11.8 %) and 459 (64.8%) subjects, respectively. Sex (p = 0.001) and occupation (p = 0.022) were independently associated with abnormal BP and ECG, respectively, while age was independently associated (p < 0.001) with abnormal BP, ECG and co-occurrence of these conditions. Predictors of stage 2 hypertension and abnormal ECG were sex (adjusted odds ratio [aOR] = 1.652, 95 % CI 1.097-2.488) and occupation (aOR = 0.411, 95 % CI 0.217-0.779), respectively, while age was a predictor for stage 2 hypertension (aOR = 0.065, 95 % CI 0.015-0.283), abnormal ECG (aOR = 0.137, 95 % CI 0.053-0.351) and their co-occurrence (aOR = 0.039, 95 % CI 0.014-0.113).

CONCLUSIONS

This study shows prevalence rates of these risk factors are on the increase. It also suggests that ECG abnormality is a public health issue among stage 2 hypertensive patients that must be monitored. Therefore, appropriate interventions that prevent and control hypertension and identified risk factors should be put in place in addition to lifestyle changes, regular screening and surveillance.

Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence

Background

Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction.

Objective

The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction.

Methods

The study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction.

Results

The area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set.

Conclusion

The deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.

Cost-Effectiveness of Portable Electrocardiogram for Screening Cardiovascular Diseases at a Primary Health Center in Ahmedabad District, India

Background: District Health Authority in Ahmedabad, Gujarat has introduced Project Lifeline, 12-lead portable ECG devices across all primary health centers (PHC) in the district to screen cardiac abnormalities among high-risk and symptomatic adults for providing primary management and proper timely referral. The prime purpose of the study was to assess the cost-effectiveness of portable ECG for the screening of cardiovascular diseases (CVD) among high-risk and symptomatic adults at the PHC in Ahmedabad, Gujarat. Methods: Cost-effective analysis was conducted using a societal perspective. An incremental costing approach was adapted, and cost-effectiveness analysis was done using a decision-analytic model. We surveyed 73 patients who screened positive for cardiac abnormality, documented the type of ECG abnormalities, and diagnosed CVD. The program cost was obtained from the implementers. Transition probabilities were derived from primary data supported by expert opinion for the intervention arm, while a systematic search of the literature was undertaken to derive transition probabilities for the control arm. Results: The ECG screening at PHC saves 2.90 life years at an incremental cost of 89.97 USD (6657.47 INR), yielding a cost-effectiveness ratio of 31.07 USD (2,299.06 INR) per life-year saved, which is below the willingness to pay threshold. The budget impact analysis was also performed. Results are sensitive to the relative risk reduction associated with the non-participation and the cost of initial screening. Conclusion: Cost-effectiveness analysis clearly shows that the facility to screen cardiac abnormality at the PHC level is highly recommended for high-risk adults and symptomatic cases.

Evaluation of Cardiac Scan in Diagnosing Coronary-artery Disease

BACKGROUND

With the evaluation of focal epicardial coronary stenosis and non-obstructive atherosclerosis, the cardiac scans play a significant role in diagnosing coronary artery disease (CAD). Moreover, the advancements in the imaging techniques leading to improved risk assessment and timely therapies help in early diagnosis of CAD with greater accuracy.

AIMS

To evaluate the role of cardiac scan in diagnosing CAD.

METHODS

Recruited 100 individuals without any history of CAD that refers to the assessment of suspected angina, conducted the prospective study. Electrocardiogram (ECG) findings assisted in the evaluation of left bundle branch blockage, abnormalities of ST-segment, and pathological Q waves.

RESULTS

The results depicted negative N.M findings among 38 respondents; whereas, ischemia and myocardial infarctions were diagnosed in 26% and 19% of the respondents, respectively. The majority of the males (59) were positive in contrast to 37 females with positive results. Similarly, 24 respondents were presented with mild dilated left atria (LA), 37 respondents suffered from impaired relaxation pattern of left ventricular (LV) diastolic filling; while, 40 of the respondents had normal global LV systolic function.

CONCLUSION

The study results have concluded that non-invasive, low-risk, and cost-effective technique like ECG is an important beneficial advancement in the diagnosis of CAD.

Improving early diagnosis of cardiovascular disease in patients with type 2 diabetes and COPD: protocol of the RED-CVD cluster randomised diagnostic trial

INTRODUCTION

The early stages of chronic progressive cardiovascular disease (CVD) generally cause non-specific symptoms that patients often do not spontaneously mention to their general practitioner, and are therefore easily missed. A proactive diagnostic strategy has the potential to uncover these frequently missed early stages, creating an opportunity for earlier intervention. This is of particular importance for chronic progressive CVDs with evidence-based therapies known to improve prognosis, such as ischaemic heart disease, atrial fibrillation and heart failure.Patients with type 2 diabetes or chronic obstructive pulmonary disease (COPD) are at particularly high risk of developing CVD. In the current study, we will demonstrate the feasibility and effectiveness of screening these high-risk patients with our early diagnosis strategy, using tools that are readily available in primary care, such as symptom questionnaires (to be filled out by the patients themselves), natriuretic peptide measurement and electrocardiography.

METHODS AND ANALYSIS

The Reviving the Early Diagnosis-CVD trial is a multicentre, cluster randomised diagnostic trial performed in primary care practices across the Netherlands. We aim to include 1300 (2×650) patients who participate in a primary care disease management programme for COPD or type 2 diabetes. Practices will be randomised to the intervention arm (performing the early diagnosis strategy during the routine visits that are part of the disease management programmes) or the control arm (care as usual). The main outcome is the number of newly detected cases with CVDs in both arms, and the subsequent therapies they received. Secondary endpoints include quality of life, cost-effectiveness and the added diagnostic value of family and reproductive history questionnaires and three (novel) biomarkers (high-sensitive troponin-I, growth differentiation factor-15 and suppressor of tumourigenicity 2). Finally newly initiated treatments will be compared in both groups.

ETHICS AND DISSEMINATION

The protocol was approved by the Medical Ethical Committee of the University Medical Center Utrecht, the Netherlands. Results are expected in 2022 and will be disseminated through international peer-reviewed publications.

TRIAL REGISTRATION NUMBER

NTR7360.

Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients

Computerized electrocardiography (ECG) has been widely used and allows linkage to electronic medical records. The present study describes the development and clinical applications of an electronic cohort derived from a digital ECG database obtained by the Telehealth Network of Minas Gerais, Brazil, for the period 2010–2017, linked to the mortality data from the national information system, the Clinical Outcomes in Digital Electrocardiography (CODE) dataset. From 2,470,424 ECGs, 1,773,689 patients were identified. A total of 1,666,778 (94%) underwent a valid ECG recording for the period 2010 to 2017, with 1,558,421 patients over 16 years old; 40.2% were men, with a mean age of 51.7 [SD 17.6] years. During a mean follow-up of 3.7 years, the mortality rate was 3.3%. ECG abnormalities assessed were: atrial fibrillation (AF), right bundle branch block (RBBB), left bundle branch block (LBBB), atrioventricular block (AVB), and ventricular pre-excitation. Most ECG abnormalities (AF: Hazard ratio [HR] 2.10; 95% CI 2.03–2.17; RBBB: HR 1.32; 95%CI 1.27–1.36; LBBB: HR 1.69; 95% CI 1.62–1.76; first degree AVB: Relative survival [RS]: 0.76; 95% CI0.71–0.81; 2:1 AVB: RS 0.21 95% CI0.09–0.52; and RS 0.36; third degree AVB: 95% CI 0.26–0.49) were predictors of overall mortality, except for ventricular pre-excitation (HR 1.41; 95% CI 0.56–3.57) and Mobitz I AVB (RS 0.65; 95% CI 0.34–1.24). In conclusion, a large ECG database established by a telehealth network can be a useful tool for facilitating new advances in the fields of digital electrocardiography, clinical cardiology and cardiovascular epidemiology.

Deep neural network-estimated electrocardiographic age as a mortality predictor

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient's age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients). On a 15% hold-out split, patients with ECG-age more than 8 years greater than the chronological age have a higher mortality rate (hazard ratio (HR) 1.79, p < 0.001), whereas those with ECG-age more than 8 years smaller, have a lower mortality rate (HR 0.78, p < 0.001). Similar results are obtained in the external cohorts ELSA-Brasil (n = 14,236) and SaMi-Trop (n = 1,631). Moreover, even for apparent normal ECGs, the predicted ECG-age gap from the chronological age remains a statistically significant risk predictor. These results show that the AI-enabled analysis of the ECG can add prognostic information.

Pre-participation Cardiovascular Screening in Young Competitive Athletes

Purpose of Review

The purpose of this review was to highlight the current recommendations, data, and limitations for methods of cardiovascular screening in athletes.

Recent Findings

While the history and physical (H&P) alone remains the cornerstone for preparticipation cardiovascular screening (PPCS) in athletes, the advent of modern electrocardiographic (ECG) screening criteria has drastically increased sensitivity and decreased false positive rates for screening. Advanced imaging techniques remain an important component of secondary testing after an athlete has an abnormal initial screening exam, however, the use of imaging for universal screening has not been rigorously tested to date. Current disqualification guidelines have now begun to emphasize shared decision making between the provider and athlete in situations of clinical equipoise.

Summary

All major medical and sporting societies recommend PPCS using a focused medical history and physical examination for all competitive athletes, but there remains controversy about the role of ECG and advanced imaging in PPCS. Future research should focus on the creation of a randomized trial that is powered for mortality that can truly assess the utility of PPCS in athletes.

Associations between left bundle branch block with different PR intervals, QRS durations, heart rates and the risk of heart failure: a register-based cohort study using ECG data from the primary care setting

AIM

Left bundle branch block (LBBB) is associated with an increased risk of heart failure (HF). We assessed the impact of common ECG parameters on this association using large-scale data.

METHODS AND RESULTS

Using ECGs recorded in a large primary care population from 2001 to 2011, we identified HF-naive patients with a first-time LBBB ECG. We obtained information on sex, age, emigration, medication, diseases and death from Danish registries. We investigated the association between the PR interval, QRS duration, and heart rate and the risk of HF over a 2-year follow-up period using Cox regression analysis.Of 2471 included patients with LBBB, 464 (18.8%) developed HF during follow-up. A significant interaction was found between QRS duration and heart rate (p<0.01), and the analyses were stratified on these parameters. Using a QRS duration <150 ms and a heart rate <70 beats per minute (bpm) as the reference, all groups were statistically significantly associated with the development of HF. Patients with a QRS duration ≥150 ms and heart rate ≥70 bpm had the highest risk of developing HF (HR 3.17 (95% CI 2.41 to 4.18, p<0.001). There was no association between the PR interval and HF after adjustment.

CONCLUSION

Prolonged QRS duration and higher heart rate were associated with increased risk of HF among primary care patients with LBBB, while no association was observed with PR interval. Patients with LBBB with both a prolonged QRS duration (≥150 ms) and higher heart rate (≥70 bpm) have the highest risk of developing HF.

Association Between ECG Abnormalities and Fatal Cardiovascular Disease Among Patients With and Without Severe Mental Illness

Background

ECG abnormalities are associated with adverse outcomes in the general population, but their prognostic significance in severe mental illness (SMI) remains unexplored. We investigated associations between no, minor, and major ECG abnormalities and fatal cardiovascular disease (CVD) among patients with SMI compared with controls without mental illness.

Methods and Results

We cross‐linked data from Danish nationwide registries and included primary care patients with digital ECGs from 2001 to 2015. Patients had SMI if they were diagnosed with schizophrenia, bipolar disorder, or severe depression before ECG recording. Controls were required to be without any prior mental illness or psychotropic medication use. Fatal CVD was assessed using hazard ratios (HRs) with 95% CIs and standardized 10‐year absolute risks. Of 346 552 patients, 10 028 had SMI (3%; median age, 54 years; male, 45%), and 336 524 were controls (97%; median age, 56 years; male, 48%). We observed an interaction between SMI and ECG abnormalities on fatal CVD (P<0.001). Severe mental illness was associated with fatal CVD across no (HR, 2.17; 95% CI, 1.95–2.43), minor (HR, 1.90; 95% CI, 1.49–2.42), and major (HR, 1.40; 95% CI, 1.26–1.55) ECG abnormalities compared with controls. Across age‐ and sex‐specific subgroups, SMI patients with ECG abnormalities but no CVD at baseline had highest standardized 10‐year absolute risks of fatal CVD.

Conclusions

ECG abnormalities conferred a poorer prognosis among patients with SMI compared with controls without mental illness. SMI patients with ECG abnormalities but no CVD represent a high‐risk population that may benefit from greater surveillance and risk management.

Electrocardiographic Left Atrial Abnormality and B-Type Natriuretic Peptide in a General Japanese Population: NIPPON DATA2010

AIMS

P-wave terminal force in lead V1 (PTFV1) is an electrocardiogram marker of increased left atrial pressure and may be a noninvasive and early detectable marker for future cardiovascular events in the general population compared to serum B-type natriuretic peptide (BNP) concentration. The clinical significance of PTFV1 in the contemporary general population is an area of unmet need. We aimed to demonstrate the correlation between PTFV1 and BNP concentrations in a contemporary representative Japanese population.

METHODS

Among 2,898 adult men and women from 300 randomly selected districts throughout Japan (NIPPON DATA2010), we analyzed 2,556 participants without cardiovascular disease (stroke, myocardial infarction, and atrial fibrillation). Elevated BNP was defined as a value of ≥ 20 pg/mL based on the definition from the Japanese Circulation Society guidelines.

RESULTS

In total, 125 (4.9%) participants had PTFV1. Participants with PTFV1 were older with a higher prevalence of hypertension, major electrocardiographic findings, and elevated BNP concentrations (13.5 [6.9, 22.8] versus 7.8 [4.4, 14.5] pg/mL; P＜0.001). After adjustment for confounders, PTFV1 was correlated with elevated BNP (odds ratio, 1.66; 95% confidence interval, 1.05-2.62; P=0.030). This correlation was consistent among various subgroups and was particularly evident in those aged ＜65 years or those without a history of hypertension.

CONCLUSIONS

In the contemporary general population cohort, PTFV1 was independently related to high BNP concentration. PTFV1 may be an alternative marker to BNP in identifying individuals at a higher risk of future cardiovascular events in the East Asian population.

Heart Rate Influence on the QT Variability Risk Factors

QT interval variability, mostly expressed by QT variability index (QTVi), has repeatedly been used in risk diagnostics. Physiologic correlates of QT variability expressions have been little researched especially when measured in short 10-second electrocardiograms (ECGs). This study investigated different QT variability indices, including QTVi and the standard deviation of QT interval durations (SDQT) in 657,287 10-second ECGs recorded in 523 healthy subjects (259 females). The indices were related to the underlying heart rate and to the 10-second standard deviation of RR intervals (SDRR). The analyses showed that both QTVi and SDQT (as well as other QT variability indices) were highly statistically significantly (p < 0.00001) influenced by heart rate and that QTVi showed poor intra-subject reproducibility (coefficient of variance approaching 200%). Furthermore, sequential analysis of regression variance showed that SDQT was more strongly related to the underlying heart rate than to SDRR, and that QTVi was influenced by the underlying heart rate and SDRR more strongly than by SDQT (p < 0.00001 for these comparisons of regression dependency). The study concludes that instead of QTVi, simpler expressions of QT interval variability, such as SDQT, appear preferable for future applications especially if multivariable combination with the underlying heart rate is used.

Assessment of Prevalence and Cost of Care Cascades After Routine Testing During the Medicare Annual Wellness Visit

IMPORTANCE

For healthy adults, routine testing during annual check-ups is considered low value and may trigger cascades of medical services of unclear benefit. It is unknown how often routine tests are performed during Medicare annual wellness visits (AWVs) or whether they are associated with cascades of care.

OBJECTIVE

To estimate the prevalence of routine electrocardiograms (ECGs), urinalyses, and thyrotropin tests and of cascades (further tests, procedures, visits, hospitalizations, and new diagnoses) that might follow among healthy adults receiving AWVs.

DESIGN, SETTING, AND PARTICIPANTS

Observational cohort study using fee-for-service Medicare claims data from beneficiaries aged 66 years and older who were continuously enrolled in fee-for-service Medicare between January 1, 2013, and March 31, 2015; received an AWV in 2014; had no test-relevant prior conditions; did not receive 1 of the 3 tests in the 6 months before the AWV; and had no test-relevant symptoms or conditions in the AWV testing period. Data were analyzed from February 13, 2019, to June 8, 2020.

EXPOSURE

Receipt of a given test within 1 week before or after the AWV.

MAIN OUTCOMES AND MEASURES

Prevalence of routine tests during AWVs and cascade-attributable event rates and associated spending in the 90 days following the AWV test period. Patient, clinician, and area-level characteristics associated with receiving routine tests were also assessed.

RESULTS

Among 75 275 AWV recipients (mean [SD] age, 72.6 [6.1] years; 48 107 [63.9%] women), 18.6% (14 017) received at least 1 low-value test including an ECG (7.2% [5421]), urinalysis (10.0% [7515]), or thyrotropin test (8.7% [6534]). Patients were more likely to receive a low-value test if they were younger (adjusted odds ratio [aOR], 1.69 for ages 66-74 years vs ages ≥85 years [95% CI, 1.53-1.86]), White (aOR, 1.32 compared with Black [95% CI, 1.16-1.49]), lived in urban areas (aOR, 1.29 vs rural [95% CI, 1.15-1.46]), and lived in high-income areas (aOR, 1.26 for >400% of the federal poverty level vs <200% of the federal poverty level [95% CI, 1.16-1.37]). A total of 6.1 (95% CI, 4.8-7.5) cascade-attributable events per 100 beneficiaries occurred in the 90 days following routine ECGs and 5.4 (95% CI, 4.2-6.5) following urinalyses, with cascade-attributable cost per beneficiary of $9.62 (95% CI, $6.43-$12.80) and $7.46 (95% CI, $5.11-$9.81), respectively. No cascade-attributable events or costs were found to be associated with thyrotropin tests.

CONCLUSIONS AND RELEVANCE

In this study, 19% of healthy Medicare beneficiaries received routine low-value ECGs, urinalyses, or thyrotropin tests during their AWVs, more often those who were younger, White, and lived in urban, high-income areas. ECGs and urinalyses were associated with cascades of modest but notable cost.

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.

Bioelectronic properties of DNA, protein, cells and their applications for diagnostic medical devices

From a couple of centuries ago, understanding physical properties of biological material, their interference with their natural host and their potential manipulation for employment as a conductor in medical devices, has gathered substantial interest in the field of bioelectronics. With the fast-emerging technologies for fabrication of diagnostic modalities, wearable biosensors and implantable devices, which electrical components are of essential importance, a need for developing novel conductors within such devices has evolved over the past decades. As the possibility of electron transport within small biological molecules, such as DNA and proteins, as well as larger elements such as cells was established, several discoveries of the modern charge characterization technologies were evolved. Development of Electrochemical Scanning Tunneling Microscopy and Nuclear Magnetic Resonance among many other techniques were of vital importance, following the discoveries made in sub-micron scales of biological material. This review covers the most recent understandings of electronic properties within different scale of biological material starting from nanometer range to millimeter-sized organs. We also discuss the state-of-the-art technology that's been made taking advantage of electronic properties of biological material for addressing diseases like Parkinson's Disease and Epilepsy.