Corrigendum to 'Alerting trends in epidemiology for non-rheumatic degenerative mitral valve disease, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease Study 2019' [Int. J. Cardiol. 395(2024) 131561].

Alerting trends in epidemiology for non-rheumatic degenerative mitral valve disease, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease Study 2019.

BACKGROUND:The global and national burden of rheumatic mitral valve disease (MVD) has been well studied and estimated before. However, little is known about non-rheumatic degenerative MVD. Therefore, this study aimed to assess the trends in non-rheumatic degenerative MVD (NRDMVD) epidemiology, with an emphasis on NRDMVD mortality, leading risk factors, and their associations with age, period, and birth cohort.METHODS:Using the data derived from the Global Burden of Disease Study 2019, including prevalence, mortality, and disability-adjusted life years, we analyzed the burden of NRDMVD and the detailed trends of NRDMVD mortality over the past 30 years in 204 countries and territories by implementing the age-period-cohort framework.RESULTS:Globally, the number of deaths due to NRDMVD increased from 5695.89 (95% uncertainty interval [UI]: 5405.19 to 5895.4) × 1000 in 1990 to 9137.79 (95% UI: 8395.68 to 9743.55) × 1000 in 2019. The all-age mortality rate increased from 106.47 (95% UI: 101.03 to 110.2) per 100,000 to 118.1 (95% UI: 108.51 to 125.93) per 100,000, whereas the age-standardized mortality rate decreased from 170.45 (95% UI: 159.61 to 176.94) per 100,000 to 117.95 (95% UI: 107.83 to 125.92) per 100,000. The estimated net drift of mortality per year was -1.1% (95% confidence interval: -1.17 to -1.04). The risk of death due to NRDMVD increased with age, reaching its peak after 85 years old globally. Despite female patients being associated with lower local drift than male patients, no significant gender differences were observed in the age effect across countries and regions for all sociodemographic index (SDI) levels, except low-SDI regions.CONCLUSIONS:We estimated the global disease prevalence of and mortality due to NRDMVD over approximately a 30-year period. The health-related burden of NRDMVD has declined worldwide; however, the condition persisted in low-SDI regions. Moreover, higher attention should be paid to female patients.

Worldwide trends in mortality for hypertensive heart disease from 1990 to 2019 with projection to 2034: data from the Global Burden of Disease 2019 study.

AIMS:This study aims to analyse the worldwide trends in hypertensive heart disease (HHD) mortality and associations with age, period, and birth cohort and predict the future burden of HHD deaths.METHODS AND RESULTS:Mortality estimates were obtained from Global Burden of Disease 2019 study. We used age-period-cohort (APC) model to examine the age, period, and cohort effects on HHD mortality between 1990 and 2019. Bayesian APC model was utilized to predict HHD deaths to 2034. The global HHD deaths were 1.16 million in 2019 and were projected to increase to 1.57 million in 2034, with the largest increment in low- and middle-income countries (LMICs). Between 1990 and 2019, middle/high-middle socio-demographic index (SDI) countries had the largest mortality reductions (annual percentage change = -2.06%), whereas low SDI countries saw a lagging performance (annual percentage change = -1.09%). There was a prominent transition in the age distribution of deaths towards old-age population in middle/high-middle SDI countries, while the proportion of premature deaths (aged under 60 years) remained at 24% in low SDI countries in 2019. Amongst LMICs, Brazil, China, and Ethiopia showed typically improving trends both over time and in recent birth cohorts, whereas 63 countries including Indonesia, the Philippines, and Pakistan had unfavourable or worsening risks for recent periods and birth cohorts.CONCLUSION:The HHD death burden in 2019 is vast and is expected to increase rapidly in the next decade, particularly for LMICs. Limited progress in HHD management together with high premature mortality would exact huge human and medical costs in low SDI countries. The examples from Brazil, China, and Ethiopia suggest that efficient health systems with action on improving hypertension care can reduce HHD mortality effectively in LMICs.

A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound.

Objective:Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data.Methods:A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of "regression training combined with classification application," which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results.Results:The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008).Conclusion:A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry "on-the-go" static ultrasound reports for referral and reexamination, thus saving healthcare resources.

Global, regional, and national burdens of atrial fibrillation/flutter from 1990 to 2019: An age-period-cohort analysis using the Global Burden of Disease 2019 study.

Background:Atrial fibrillation/flutter (AF/AFL) significantly impacts countries with varying income levels. We aimed to present worldwide estimates of its burden from 1990 to 2019 using data from the Global Burden of Disease (GBD) study.Methods:We derived cause-specific AF/AFL mortality and disability-adjusted life-year (DALY) estimates from the GBD 2019 study data. We used an age-period-cohort (APC) model to predict annual changes in mortality (net drifts), annual percentage changes from 50-55 to 90-95 years (local drifts), and period and cohort relative risks (period and cohort effects) between 1990 and 2019 by sex and sociodemographic index (SDI) quintiles. This allowed us to determine the impacts of age, period, and cohort on mortality and DALY trends and the inequities and treatment gaps in AF/AFL management.Results:Based on GBD data, our estimates showed that 59.7 million cases of AF/AFL occurred worldwide in 2019, while the number of AF/AFL deaths rose from 117 000 to 315 000 (61.5% women). All-age mortality and DALYs increased considerably from 1990 to 2019, and there was an increase in age risk and a shift in death and DALYs toward the older (>80) population. Although the global net drift mortality of AF/AFL decreased overall (-0.16%; 95% confidence interval (CI) = -0.20, 0.12 per year), we observed an opposite trend in the low-middle SDI (0.53%; 95% CI = 0.44, 0.63) and low SDI regions (0.32%; 95% CI = 0.18, 0.45). Compared with net drift among men (-0.08%; 95% CI = -0.14, -0.02), women had a greater downward trend or smaller upward trend of AF/AFL (-0.21%; 95% CI = -0.26, -0.16) in mortality in middle- and low-middle-SDI countries (P < 0.001). Uzbekistan had the largest net drift of mortality (4.21%; 95% CI = 3.51, 4.9) and DALYs (2.16%; 95% CI = 2.05, 2.27) among all countries. High body mass index, high blood pressure, smoking, and alcohol consumption were more prevalent in developed countries; nevertheless, lead exposure was more prominent in developing countries and regions.Conclusions:The burden of AF/AFL in 2019 and its temporal evolution from 1990 to 2019 differed significantly across SDI quintiles, sexes, geographic locations, and countries, necessitating the prioritisation of health policies based on risk-differentiated, cost-effective AF/AFL management.

Global, regional, and national time trends in cancer mortality attributable to high fasting plasma glucose: an age-period cohort analysis.

BACKGROUND:High fasting plasma glucose (HFPG) is the fastest-growing risk factor for cancer deaths worldwide. We reported the cancer mortality attributable to HFPG at global, regional, and national levels over the past three decades and associations with age, period, and birth cohort.METHODS:Data for this study were retrieved from the Global Burden of Disease Study 2019, and we used age-period-cohort modelling to estimate age, cohort and period effects, as well as net drift (overall annual percentage change) and local drift (annual percentage change in each age group).RESULTS:Over the past 30 years, the global age-standardized mortality rate (ASMR) attributable to HFPG has increased by 27.8%. The ASMR in 2019 was highest in the male population in high sociodemographic index (SDI) areas (8.70; 95% CI, 2.23-18.04). The net drift for mortality was highest in the female population in low SDI areas (2.33; 95% CI, 2.12-2.55). Unfavourable period and cohort effects were found across all SDI quintiles. Cancer subtypes such as "trachea, bronchus, and lung cancers", "colon and rectal cancers", "breast cancer" and "pancreatic cancer" exhibited similar trends.CONCLUSIONS:The cancer mortality attributable to HFPG has surged during the past three decades. Unfavourable age-period-cohort effects on mortality were observed across all SDI quintiles, and the cancer mortality attributable to HFPG is expected to continue to increase rapidly in the future, particularly in lower SDI locations. This is a grim global public health issue that requires immediate attention.

Global, regional, and national time trends in disability-adjusted life years, mortality, and variable risk factors of non-rheumatic calcified aortic valve disease, 1990-2019: an age-period-cohort analysis of the Global Burden of Disease 2019 study.

Background:Non-rheumatic heart valve disease (NRVD) is a common cardiovascular disease, whereas calcific aortic valve disease (CAVD) is a type of disease with the fastest-growing mortality and disability-adjusted life years (DALYs). This study presents an overview of the trends noted in the DALY, CAVD mortality, and the modifiable risk factors in the last 30 years, across 204 countries and territories, and their relationship with the period, age, and birth cohort.Methods:Data were obtained from the Global Burden of Disease (GBD) 2019 database. An age-period-cohort (APC) model was used to assess general annual percentage changes in DALYs and mortality over the past 30 years in 204 countries and territories.Results:In 2019, the age-standardized mortality rate for the entire population in areas with a high socio-demographic index (SDI) was more than 4 times higher than that in low-SDI areas. From 1990 to 2019, the net drift in mortality for the whole population was from -2.1% [95% confidence interval (CI): -2.39% to -1.82%] per year in high-SDI regions to 0.05% (95% CI: -0.13% to 0.23%) per year in low- to medium-SDI regions. The trend of DALYs was similar to that of mortality. The age-wise distribution of deaths exhibited a shift toward older populations in high-SDI regions globally, except for Qatar, Saudi Arabia, and the United Arab Emirates. Over time, in most medium, medium-low, and low SDI regions, there was no significant improvement in the period and birth cohort or even an unfavorable or worsening risk. The main variable risk factors of CAVD death and DALYs lost were high sodium diet, high systolic blood pressure, and lead exposure. Those risk factors only showed a significant downward trend in middle- and high-SDI regions.Conclusions:Health disparities between regions for CAVD are widening and could lead to a heavy disease burden in the future. Health authorities and policymakers in low SDI areas, in particular, need to consider improving resource allocation, increasing access to medical resources, and controlling variable risk factors to stem the growth of the disease burden.

Flexible electronics for cardiovascular healthcare monitoring.

Cardiovascular diseases (CVDs) are one of the most urgent threats to humans worldwide, which are responsible for almost one-third of global mortality. Over the last decade, research on flexible electronics for monitoring and treatment of CVDs has attracted tremendous attention. In contrast to conventional medical instruments in hospitals that are usually bulky, hard to move, monofunctional, and time-consuming, flexible electronics are capable of continuous, noninvasive, real-time, and portable monitoring. Notable progress has been made in this emerging field, and thus a number of significant achievements and concomitant research prospects deserve attention for practical implementation. Here, we comprehensively review the latest progress of flexible electronics for CVDs, focusing on new functions provided by flexible electronics. First, the characteristics of CVDs and flexible electronics and the foundation of their combination are briefly reviewed. Then, four representative applications of flexible electronics for CVDs are elaborated: blood pressure (BP) monitoring, electrocardiogram (ECG) monitoring, echocardiogram monitoring, and direct epicardium monitoring. Their operational principles, progress, merits and demerits, and future efforts are discussed. Finally, the remaining challenges and opportunities for flexible electronics for cardiovascular healthcare are outlined.

Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.

PURPOSE:Studies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities.METHODS:The Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110 mL/m2 or RV ejection fraction which is lower than 40%) and 20 normal subjects who suffered from both cardiac MRI. The subjects were separated into training and validation sets in a ratio of 7:3 and were modeled by utilizing a nerve net of deep-learning and six machine-learning algorithms. Eight MRI specialists from multiple centers independently determined whether each subject in the validation group had RV abnormalities. Model performance was evaluated based on the AUC, accuracy, recall, sensitivity and specificity. Furthermore, a preliminary assessment of patient disease risk was performed based on clinical information using a nomogram.RESULTS:The deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8.CONCLUSIONS:A deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening.

Background:As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model.Methods:The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms.Results:A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994).Conclusions:This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

Corrigendum to 'Alerting trends in epidemiology for non-rheumatic degenerative mitral valve disease, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease Study 2019' [Int. J. Cardiol. 395(2024) 131561].

Alerting trends in epidemiology for non-rheumatic degenerative mitral valve disease, 1990-2019: An age-period-cohort analysis for the Global Burden of Disease Study 2019.

BACKGROUND:The global and national burden of rheumatic mitral valve disease (MVD) has been well studied and estimated before. However, little is known about non-rheumatic degenerative MVD. Therefore, this study aimed to assess the trends in non-rheumatic degenerative MVD (NRDMVD) epidemiology, with an emphasis on NRDMVD mortality, leading risk factors, and their associations with age, period, and birth cohort.METHODS:Using the data derived from the Global Burden of Disease Study 2019, including prevalence, mortality, and disability-adjusted life years, we analyzed the burden of NRDMVD and the detailed trends of NRDMVD mortality over the past 30 years in 204 countries and territories by implementing the age-period-cohort framework.RESULTS:Globally, the number of deaths due to NRDMVD increased from 5695.89 (95% uncertainty interval [UI]: 5405.19 to 5895.4) × 1000 in 1990 to 9137.79 (95% UI: 8395.68 to 9743.55) × 1000 in 2019. The all-age mortality rate increased from 106.47 (95% UI: 101.03 to 110.2) per 100,000 to 118.1 (95% UI: 108.51 to 125.93) per 100,000, whereas the age-standardized mortality rate decreased from 170.45 (95% UI: 159.61 to 176.94) per 100,000 to 117.95 (95% UI: 107.83 to 125.92) per 100,000. The estimated net drift of mortality per year was -1.1% (95% confidence interval: -1.17 to -1.04). The risk of death due to NRDMVD increased with age, reaching its peak after 85 years old globally. Despite female patients being associated with lower local drift than male patients, no significant gender differences were observed in the age effect across countries and regions for all sociodemographic index (SDI) levels, except low-SDI regions.CONCLUSIONS:We estimated the global disease prevalence of and mortality due to NRDMVD over approximately a 30-year period. The health-related burden of NRDMVD has declined worldwide; however, the condition persisted in low-SDI regions. Moreover, higher attention should be paid to female patients.

Worldwide trends in mortality for hypertensive heart disease from 1990 to 2019 with projection to 2034: data from the Global Burden of Disease 2019 study.

AIMS:This study aims to analyse the worldwide trends in hypertensive heart disease (HHD) mortality and associations with age, period, and birth cohort and predict the future burden of HHD deaths.METHODS AND RESULTS:Mortality estimates were obtained from Global Burden of Disease 2019 study. We used age-period-cohort (APC) model to examine the age, period, and cohort effects on HHD mortality between 1990 and 2019. Bayesian APC model was utilized to predict HHD deaths to 2034. The global HHD deaths were 1.16 million in 2019 and were projected to increase to 1.57 million in 2034, with the largest increment in low- and middle-income countries (LMICs). Between 1990 and 2019, middle/high-middle socio-demographic index (SDI) countries had the largest mortality reductions (annual percentage change = -2.06%), whereas low SDI countries saw a lagging performance (annual percentage change = -1.09%). There was a prominent transition in the age distribution of deaths towards old-age population in middle/high-middle SDI countries, while the proportion of premature deaths (aged under 60 years) remained at 24% in low SDI countries in 2019. Amongst LMICs, Brazil, China, and Ethiopia showed typically improving trends both over time and in recent birth cohorts, whereas 63 countries including Indonesia, the Philippines, and Pakistan had unfavourable or worsening risks for recent periods and birth cohorts.CONCLUSION:The HHD death burden in 2019 is vast and is expected to increase rapidly in the next decade, particularly for LMICs. Limited progress in HHD management together with high premature mortality would exact huge human and medical costs in low SDI countries. The examples from Brazil, China, and Ethiopia suggest that efficient health systems with action on improving hypertension care can reduce HHD mortality effectively in LMICs.

A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound.

Objective:Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data.Methods:A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of "regression training combined with classification application," which can be generalized to dynamic ECG and static cardiac ultrasound views to identify HF with a reduced ejection fraction (EF < 40%). Additionally, three independent datasets containing 8976 cardiac ultrasound views and 10085 cardiac ultrasound videos were established. Subsequently, a multinational, multi-center dataset of EF was labeled. Furthermore, model training and independent validation were performed. Finally, 15 registered ultrasonographers and cardiologists with different working years in three regional hospitals specialized in cardiovascular disease were recruited to compare the results.Results:The proposed deep spatio-temporal convolutional model achieved an area under the receiveroperating characteristic curve (AUC) value of 0.95 (95% confidence interval [CI]: 0.947 to 0.953) on the training set of dynamic ultrasound data and an AUC of 1 (95% CI, 1 to 1) on the independent validation set. Subsequently, the model was applied to the static cardiac ultrasound view (validation set) with simultaneous input of 1, 2, 4, and 8 images of the same heart, with classification accuracies of 85%, 81%, 93%, and 92%, respectively. On the static data, the classification accuracy of the artificial intelligence (AI) model was comparable with the best performance of ultrasonographers and cardiologists with more than 3 working years (P = 0.344), but significantly better than the median level (P = 0.0000008).Conclusion:A new deep spatio-temporal convolution model was constructed to identify patients with HF with reduced EF accurately (< 40%) using dynamic and static cardiac ultrasound images. The model outperformed the diagnostic performance of most senior specialists. This may be the first HF-related AI diagnostic model compatible with multi-dimensional cardiac ultrasound data, and may thereby contribute to the improvement of HF diagnosis. Additionally, the model enables patients to carry "on-the-go" static ultrasound reports for referral and reexamination, thus saving healthcare resources.

Global, regional, and national burdens of atrial fibrillation/flutter from 1990 to 2019: An age-period-cohort analysis using the Global Burden of Disease 2019 study.

Background:Atrial fibrillation/flutter (AF/AFL) significantly impacts countries with varying income levels. We aimed to present worldwide estimates of its burden from 1990 to 2019 using data from the Global Burden of Disease (GBD) study.Methods:We derived cause-specific AF/AFL mortality and disability-adjusted life-year (DALY) estimates from the GBD 2019 study data. We used an age-period-cohort (APC) model to predict annual changes in mortality (net drifts), annual percentage changes from 50-55 to 90-95 years (local drifts), and period and cohort relative risks (period and cohort effects) between 1990 and 2019 by sex and sociodemographic index (SDI) quintiles. This allowed us to determine the impacts of age, period, and cohort on mortality and DALY trends and the inequities and treatment gaps in AF/AFL management.Results:Based on GBD data, our estimates showed that 59.7 million cases of AF/AFL occurred worldwide in 2019, while the number of AF/AFL deaths rose from 117 000 to 315 000 (61.5% women). All-age mortality and DALYs increased considerably from 1990 to 2019, and there was an increase in age risk and a shift in death and DALYs toward the older (>80) population. Although the global net drift mortality of AF/AFL decreased overall (-0.16%; 95% confidence interval (CI) = -0.20, 0.12 per year), we observed an opposite trend in the low-middle SDI (0.53%; 95% CI = 0.44, 0.63) and low SDI regions (0.32%; 95% CI = 0.18, 0.45). Compared with net drift among men (-0.08%; 95% CI = -0.14, -0.02), women had a greater downward trend or smaller upward trend of AF/AFL (-0.21%; 95% CI = -0.26, -0.16) in mortality in middle- and low-middle-SDI countries (P < 0.001). Uzbekistan had the largest net drift of mortality (4.21%; 95% CI = 3.51, 4.9) and DALYs (2.16%; 95% CI = 2.05, 2.27) among all countries. High body mass index, high blood pressure, smoking, and alcohol consumption were more prevalent in developed countries; nevertheless, lead exposure was more prominent in developing countries and regions.Conclusions:The burden of AF/AFL in 2019 and its temporal evolution from 1990 to 2019 differed significantly across SDI quintiles, sexes, geographic locations, and countries, necessitating the prioritisation of health policies based on risk-differentiated, cost-effective AF/AFL management.

Global, regional, and national time trends in cancer mortality attributable to high fasting plasma glucose: an age-period cohort analysis.

BACKGROUND:High fasting plasma glucose (HFPG) is the fastest-growing risk factor for cancer deaths worldwide. We reported the cancer mortality attributable to HFPG at global, regional, and national levels over the past three decades and associations with age, period, and birth cohort.METHODS:Data for this study were retrieved from the Global Burden of Disease Study 2019, and we used age-period-cohort modelling to estimate age, cohort and period effects, as well as net drift (overall annual percentage change) and local drift (annual percentage change in each age group).RESULTS:Over the past 30 years, the global age-standardized mortality rate (ASMR) attributable to HFPG has increased by 27.8%. The ASMR in 2019 was highest in the male population in high sociodemographic index (SDI) areas (8.70; 95% CI, 2.23-18.04). The net drift for mortality was highest in the female population in low SDI areas (2.33; 95% CI, 2.12-2.55). Unfavourable period and cohort effects were found across all SDI quintiles. Cancer subtypes such as "trachea, bronchus, and lung cancers", "colon and rectal cancers", "breast cancer" and "pancreatic cancer" exhibited similar trends.CONCLUSIONS:The cancer mortality attributable to HFPG has surged during the past three decades. Unfavourable age-period-cohort effects on mortality were observed across all SDI quintiles, and the cancer mortality attributable to HFPG is expected to continue to increase rapidly in the future, particularly in lower SDI locations. This is a grim global public health issue that requires immediate attention.

Global, regional, and national time trends in disability-adjusted life years, mortality, and variable risk factors of non-rheumatic calcified aortic valve disease, 1990-2019: an age-period-cohort analysis of the Global Burden of Disease 2019 study.

Background:Non-rheumatic heart valve disease (NRVD) is a common cardiovascular disease, whereas calcific aortic valve disease (CAVD) is a type of disease with the fastest-growing mortality and disability-adjusted life years (DALYs). This study presents an overview of the trends noted in the DALY, CAVD mortality, and the modifiable risk factors in the last 30 years, across 204 countries and territories, and their relationship with the period, age, and birth cohort.Methods:Data were obtained from the Global Burden of Disease (GBD) 2019 database. An age-period-cohort (APC) model was used to assess general annual percentage changes in DALYs and mortality over the past 30 years in 204 countries and territories.Results:In 2019, the age-standardized mortality rate for the entire population in areas with a high socio-demographic index (SDI) was more than 4 times higher than that in low-SDI areas. From 1990 to 2019, the net drift in mortality for the whole population was from -2.1% [95% confidence interval (CI): -2.39% to -1.82%] per year in high-SDI regions to 0.05% (95% CI: -0.13% to 0.23%) per year in low- to medium-SDI regions. The trend of DALYs was similar to that of mortality. The age-wise distribution of deaths exhibited a shift toward older populations in high-SDI regions globally, except for Qatar, Saudi Arabia, and the United Arab Emirates. Over time, in most medium, medium-low, and low SDI regions, there was no significant improvement in the period and birth cohort or even an unfavorable or worsening risk. The main variable risk factors of CAVD death and DALYs lost were high sodium diet, high systolic blood pressure, and lead exposure. Those risk factors only showed a significant downward trend in middle- and high-SDI regions.Conclusions:Health disparities between regions for CAVD are widening and could lead to a heavy disease burden in the future. Health authorities and policymakers in low SDI areas, in particular, need to consider improving resource allocation, increasing access to medical resources, and controlling variable risk factors to stem the growth of the disease burden.

Flexible electronics for cardiovascular healthcare monitoring.

Cardiovascular diseases (CVDs) are one of the most urgent threats to humans worldwide, which are responsible for almost one-third of global mortality. Over the last decade, research on flexible electronics for monitoring and treatment of CVDs has attracted tremendous attention. In contrast to conventional medical instruments in hospitals that are usually bulky, hard to move, monofunctional, and time-consuming, flexible electronics are capable of continuous, noninvasive, real-time, and portable monitoring. Notable progress has been made in this emerging field, and thus a number of significant achievements and concomitant research prospects deserve attention for practical implementation. Here, we comprehensively review the latest progress of flexible electronics for CVDs, focusing on new functions provided by flexible electronics. First, the characteristics of CVDs and flexible electronics and the foundation of their combination are briefly reviewed. Then, four representative applications of flexible electronics for CVDs are elaborated: blood pressure (BP) monitoring, electrocardiogram (ECG) monitoring, echocardiogram monitoring, and direct epicardium monitoring. Their operational principles, progress, merits and demerits, and future efforts are discussed. Finally, the remaining challenges and opportunities for flexible electronics for cardiovascular healthcare are outlined.

Development of an Expert-Level Right Ventricular Abnormality Detection Algorithm Based on Deep Learning.

PURPOSE:Studies relating to the right ventricle (RV) are inadequate, and specific diagnostic algorithms still need to be improved. This essay is designed to make exploration and verification on an algorithm of deep learning based on imaging and clinical data to detect RV abnormalities.METHODS:The Automated Cardiac Diagnosis Challenge dataset includes 20 subjects with RV abnormalities (an RV cavity volume which is higher than 110 mL/m2 or RV ejection fraction which is lower than 40%) and 20 normal subjects who suffered from both cardiac MRI. The subjects were separated into training and validation sets in a ratio of 7:3 and were modeled by utilizing a nerve net of deep-learning and six machine-learning algorithms. Eight MRI specialists from multiple centers independently determined whether each subject in the validation group had RV abnormalities. Model performance was evaluated based on the AUC, accuracy, recall, sensitivity and specificity. Furthermore, a preliminary assessment of patient disease risk was performed based on clinical information using a nomogram.RESULTS:The deep-learning neural network outperformed the other six machine-learning algorithms, with an AUC value of 1 (95% confidence interval: 1-1) on both training group and validation group. This algorithm surpassed most human experts (87.5%). In addition, the nomogram model could evaluate a population with a disease risk of 0.2-0.8.CONCLUSIONS:A deep-learning algorithm could effectively identify patients with RV abnormalities. This AI algorithm developed specifically for right ventricular abnormalities will improve the detection of right ventricular abnormalities at all levels of care units and facilitate the timely diagnosis and treatment of related diseases. In addition, this study is the first to validate the algorithm's ability to classify RV abnormalities by comparing it with human experts.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening.

Background:As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model.Methods:The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms.Results:A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994).Conclusions:This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.