EzMechanism: an automated tool to propose catalytic mechanisms of enzyme reactions.

Over the years, hundreds of enzyme reaction mechanisms have been studied using experimental and simulation methods. This rich literature on biological catalysis is now ripe for use as the foundation of new knowledge-based approaches to investigate enzyme mechanisms. Here, we present a tool able to automatically infer mechanistic paths for a given three-dimensional active site and enzyme reaction, based on a set of catalytic rules compiled from the Mechanism and Catalytic Site Atlas, a database of enzyme mechanisms. EzMechanism (pronounced as 'Easy' Mechanism) is available to everyone through a web user interface. When studying a mechanism, EzMechanism facilitates and improves the generation of hypotheses, by making sure that relevant information is considered, as derived from the literature on both related and unrelated enzymes. We validated EzMechanism on a set of 62 enzymes and have identified paths for further improvement, including the need for additional and more generic catalytic rules.

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.

BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.

Evaluation of patients with implantable cardioverter-defibrillator in a Latin American tertiary center.

INTRODUCTION: Despite advancements in implantable cardioverter-defibrillator (ICD) technology, sudden cardiac death (SCD) remains a persistent public health concern. Chagas disease (ChD), prevalent in Brazil, is associated with increased ventricular tachycardia (VT) and ventricular fibrillation (VF) events and SCD compared to other cardiomyopathies. METHODS: This retrospective observational study included patients who received ICDs between October 2007 and December 2018. The study aims to assess whether mortality and VT/VF events decreased in patients who received ICDs during different time periods (2007-2010, 2011-2014, and 2015-2018). Additionally, it seeks to compare the prognosis of ChD patients with non-ChD patients. Time periods were chosen based on the establishment of the Arrhythmia Service in 2011. The primary outcome was overall mortality, assessed across the entire sample and the three periods. Secondary outcomes included VT/VF events and the combined outcome of death or VT/VF. RESULTS: Of the 885 patients included, 31% had ChD. Among them, 28% died, 14% had VT/VF events, and 37% experienced death and/or VT/VF. Analysis revealed that period 3 (2015-2018) was associated with better death-free survival (p = .007). ChD was the only variable associated with a higher rate of VT/VF events (p < .001) and the combined outcome (p = .009). CONCLUSION: Mortality and combined outcome rates decreased gradually for ICD patients during the periods 2011-2014 and 2015-2018 compared to the initial period (2007-2010). ChD was associated with higher VT/VF events in ICD patients, only in the first two periods.

Adherence to the Dietary Approaches to Stop Hypertension (DASH) and Serum Urate Concentrations: A Longitudinal Analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

BACKGROUND: Increased serum urate (SU) and hyperuricemia (HU) are associated with chronic noncommunicable diseases and mortality. SU concentrations are affected by several factors, including diet, and are expected to rise with age. We investigated whether the Dietary Approaches to Stop Hypertension (DASH) diet alter this trend. OBJECTIVE: The objective was to assess whether adherence to the DASH diet predicts a longitudinal change in SU concentrations and risk of HU in 8 y of follow-up. METHODS: Longitudinal analyses using baseline (2008-2010, aged 35-74 y), second (2012-2014), and third (2016-2018) visits data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The inclusion criteria were having complete food frequency questionnaire (FFQ) and urinary sodium measurement, in addition to having SU measurement at the 1st visit and at least 1 of the 2 follow-up visits. For the HU incidence analyses, participants had also to be free from HU at baseline. The final samples included 12575 individuals for the SU change analyses and 10549 for the HU incidence analyses. Adherence to DASH diet was assessed as continuous value. HU was defined as SU>6.8 mg/dL and/or urate-lowering therapy use. Mixed-effect linear and Poisson regressions (incidence rate ratio [IRR] and 95% confidence interval [CI]) were used in the analyses, adjusted for confounders. RESULTS: The mean age was 51.4 (8.7) y, and 55.4% were females. SU means (standard deviation) were 5.4 (1.4) at 1st visit, 5.2 (1.4) at 2nd visit, and 5.1(1.3) mg/dL at 3rd visit. The HU incidence rate was 8.87 per 1000 person-y. Each additional point in adherence to the DASH diet accelerated SU decline (P< 0.01) and lowered the incidence of HU by 4.3% (IRR: 0.957; 95% CI: 0.938,0.977) in adjusted model. CONCLUSION: The present study findings reinforce the importance of encouraging the DASH diet as a healthy dietary pattern to control and reduce the SU concentrations and risk of HU.

ISE/ISHNE expert consensus statement on the ECG diagnosis of left ventricular hypertrophy: The change of the paradigm.

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria. The classical paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces, reflected in the augmented QRS amplitude. However, the low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm. The theoretical background for voltage measured at the body surface is defined by the solid angle theorem, which relates the measured voltage to spatial and non-spatial determinants. The spatial determinants are represented by the extent of the activation front and the distance of the recording electrodes. The non-spatial determinants comprise electrical characteristics of the myocardium, which are comparatively neglected in the interpretation of the QRS patterns. Various clinical conditions are associated with LVH. These conditions produce considerable diversity of electrical properties alterations thereby modifying the resultant QRS patterns. The spectrum of QRS patterns observed in LVH patients is quite broad, including also left axis deviation, left anterior fascicular block, incomplete and complete left bundle branch blocks, Q waves, and fragmented QRS. Importantly, the QRS complex can be within normal limits. The new paradigm stresses the electrophysiological background in interpreting QRS changes, i.e., the effect of the non-spatial determinants. This postulates that the role of ECG is not to estimate LV size in LVH, but to understand and decode the underlying electrical processes, which are crucial in relation to cardiovascular risk assessment.

Enzyme function and evolution through the lens of bioinformatics.

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.

Effect of a Structured Multilevel Telehealth Service on Hospital Admissions and Mortality During COVID-19 in a Resource-Limited Region in Brazil: Retrospective Cohort Study.

BACKGROUND: The COVID-19 pandemic represented a great stimulus for the adoption of telehealth and many initiatives in this field have emerged worldwide. However, despite this massive growth, data addressing the effectiveness of telehealth with respect to clinical outcomes remain scarce. OBJECTIVE: The aim of this study was to evaluate the impact of the adoption of a structured multilevel telehealth service on hospital admissions during the acute illness course and the mortality of adult patients with flu syndrome in the context of the COVID-19 pandemic. METHODS: A retrospective cohort study was performed in two Brazilian cities where a public COVID-19 telehealth service (TeleCOVID-MG) was deployed. TeleCOVID-MG was a structured multilevel telehealth service, including (1) first response and risk stratification through a chatbot software or phone call center, (2) teleconsultations with nurses and medical doctors, and (3) a telemonitoring system. For this analysis, we included data of adult patients registered in the Flu Syndrome notification databases who were diagnosed with flu syndrome between June 1, 2020, and May 31, 2021. The exposed group comprised patients with flu syndrome who used TeleCOVID-MG at least once during the illness course and the control group comprised patients who did not use this telehealth service during the respiratory illness course. Sociodemographic characteristics, comorbidities, and clinical outcomes data were extracted from the Brazilian official databases for flu syndrome, Severe Acute Respiratory Syndrome (due to any respiratory virus), and mortality. Models for the clinical outcomes were estimated by logistic regression. RESULTS: The final study population comprised 82,182 adult patients with a valid registry in the Flu Syndrome notification system. When compared to patients who did not use the service (n=67,689, 82.4%), patients supported by TeleCOVID-MG (n=14,493, 17.6%) had a lower chance of hospitalization during the acute respiratory illness course, even after adjusting for sociodemographic characteristics and underlying medical conditions (odds ratio [OR] 0.82, 95% CI 0.71-0.94; P=.005). No difference in mortality was observed between groups (OR 0.99, 95% CI 0.86-1.12; P=.83). CONCLUSIONS: A telehealth service applied on a large scale in a limited-resource region to tackle COVID-19 was related to reduced hospitalizations without increasing the mortality rate. Quality health care using inexpensive and readily available telehealth and digital health tools may be delivered in areas with limited resources and should be considered as a potential and valuable health care strategy. The success of a telehealth initiative relies on a partnership between the involved stakeholders to define the roles and responsibilities; set an alignment between the different modalities and levels of health care; and address the usual drawbacks related to the implementation process, such as infrastructure and accessibility issues.

Vagal dysautonomia in patients with Chagas disease and mortality: 14-year results of a population cohort of the elderly.

INTRODUCTION: Great part of Chagas disease (ChD) mortality occurs due to ventricular arrhythmias, and autonomic function (AF) may predict unfavorable outcomes. We aimed to evaluate the predictive value of AF indexes in ChD patients. METHODS: The Bambuí Study of Aging is a prospective cohort of residents ≥60 years at study onset (1997), in the southeastern Brazilian city of Bambuí (15,000 inhabitants). Consented participants underwent annual follow-up visits, and death certificates were tracked. AF was assessed by the maximum expiration on minimum inspiration (E:I) ratio during ECG acquisition and by heart rate variability indices: SDRR (standard deviation of adjacent RR intervals) and RMSSD (square root of the mean of the sum of squares of the differences between adjacent RR intervals)), calculated using a computer algorithm. Cox proportional hazards regression was performed to access the prognostic value of AF indexes, expressed as terciles, for all-cause mortality, after adjustment for demographic, clinical and ECG variables. RESULTS: From 1742 qualifying residents, 1000 had valid AF tests, being 321 with ChD. Among these, median age was 68 (64-74) years, and 32.5% were men. In Cox survival analyses, only SDRR was associated with all-cause mortality in non-adjusted models: SDRR (hazard ratio (HR): 1.26 (95% CI 1.08-1.47), p < 0.001), E:I ratio (HR: 1.13 (95% CI 0,98-1.31), p = 0.10) and RMSSD (HR: 0.99 (0.86-1.16), p = 0.95). After adjustment for sex and age, none of the indexes remained as independent predictors. CONCLUSION: Among elderly patients with ChD, AF indexes available in this cohort were not independent predictors of 14-year mortality.

Scale up of implementation of a multidimensional intervention to enhance hypertension and diabetes care at the primary care setting: A protocol for a cluster-randomized study in Brazil.

BACKGROUND: Hypertension and diabetes mellitus (DM) are highly prevalent in low and middle-income countries (LMICs), and the proportion of patients with uncontrolled diseases is higher than in high-income countries. Innovative strategies are required to surpass barriers of low sources, distance and quality of health care. Our aim is to assess the uptake and effectiveness of the implementation of an integrated multidimensional strategy in the primary care setting, for the management of people with hypertension and diabetes mellitus in Brazil. METHODS: This scale up implementation study called Control of Hypertension and diAbetes in MINas Gerais (CHArMING) Project has mixed-methods, and comprehends 4 steps: (1) needs assessment, including a standardized structured questionnaire and focus groups with health care practitioners; (2) baseline period, 3 months before the implementation of the intervention; (3) cluster randomized controlled trial (RCT) with a 12-months follow-up period; and (4) a qualitative study after the end of follow-up. The cluster RCT will randomize 35 centers to intervention (n = 18) or usual care (n = 17). Patients ≥18 years old, with diagnosis of hypertension and/or DM, of 5 Brazilian cities in a resource-constrained area will be enrolled. The intervention consists of a multifaceted strategy, with a multidisciplinary approach, including telehealth tools (decision support systems, short message service, telediagnosis), continued education with an approach to issues related to the care of people with hypertension and diabetes in primary care, including pharmacological and non-pharmacological treatment and behavioral change. The project has actions focused on professionals and patients. CONCLUSIONS: This study consists of a multidimensional strategy with multidisciplinary approach using digital health to improve the control of hypertension and/or DM in the primary health care setting. We expect to provide the basis for implementing an innovative management program for hypertension and DM in Brazil, aiming to reduce the present and future burden of these diseases in Brazil and other LMICs. CLINICAL TRIAL IDENTIFIER: This study was registered in ClinicalTrials.gov. (NCT05660928).

Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Percentiles of predicted 10-year cardiovascular disease risk by sex and age in Brazil and their association with estimated risk of long-term atherosclerotic events.

OBJECTIVE: Expressing the cardiovascular disease (CVD) risk in relation to peers may complement the estimation of absolute CVD risk. We aimed to determine 10-year CVD risk percentiles by sex and age in the Brazilian population and evaluate their association with estimated long-term atherosclerotic CVD (ASCVD) risk. METHODS: A cross-sectional analysis of baseline data from the ELSA-Brasil study was conducted in individuals aged 40-74 years without prior ASCVD. Ten-year CVD risk and long-term ASCVD risk were estimated by the WHO risk score and the Multinational Cardiovascular Risk Consortium tool, respectively. Ten-year risk percentiles were determined by ranking the calculated risks within each sex and age group. RESULTS: Ten-year CVD risk versus percentile plots were constructed for each sex and age group using data from 13,364 participants (55% females; median age, 52 [IQR, 46-59] years). Long-term ASCVD risk was calculated in 12,973 (97.1%) participants. Compared to individuals at the <25th risk percentile, those at the ≥75th percentile had a greater risk of being in the highest quartile of long-term risk (ORs [95% CIs] 6.57 [5.18-8.30] in females and 11.59 [8.42-15.96] in males) in regression models adjusted for age, race, education, and 10-year CVD risk. In both sexes, the association between risk percentile and long-term risk weakened after age 50. A tool for calculating 10-year CVD risk and the corresponding percentile is available at https://bit.ly/3CzPUi6. CONCLUSIONS: We established percentiles of predicted 10-year CVD risk by sex and age in the Brazilian population, which independently reflect the estimated long-term ASCVD risk in younger individuals.

Use of Antihypertensive Drugs and Arterial Stiffness in the Longitudinal Study of Adult Health (ELSA-Brasil).

OBJECTIVE: Evaluate the longitudinal association between BP control and the use of antihypertensive classes with arterial stiffness (AS) in Brazilian adults. METHODS: This study included 1830 participants with arterial hypertension (1092 participants with controlled BP and 738 participants with uncontrolled BP) from the Longitudinal Study of Adult Health (ELSA-Brasil). AS was assessed by pulse wave velocity (PWV) and pulse pressure (PP) at baseline and repeated after approximately 9 years. Associations between AS and BP control and the use of antihypertensives, diuretics, angiotensin-converting enzyme inhibitors (ACEI), AT1 receptor blockers (ARB), calcium channel blockers (CCB), and beta blockers (in the population with controlled BP), at baseline were investigated using linear mixed-effects models. RESULTS: Uncontrolled BP was associated with worse PWV and PP trajectory, respectively (ß = 0.026 [0.008 to 0.036] / ß = 0.273 [0.216 to 0.330]). Among the participants with controlled BP, using CCB (ß = 0.031 [0.011 to 0.051]) was associated with a worse PWV trajectory, compared to not using this class and this combination, respectively. CONCLUSION: BP control, regardless of the class of antihypertensive used is associated with a better AS trajectory, as assessed by PWV and PP. Among participants with controlled BP, the use of BCC, compared to not using this class, seems to be worse for the trajectory of PWV in individuals with arterial hypertension without cardiovascular disease. Further studies are needed to assess whether this effect results in a better prognosis for patients with arterial hypertension.

Ultra-processed foods: Cross-sectional and longitudinal association with uric acid and hyperuricemia in ELSA-Brasil.

BACKGROUND AND AIMS: Food intake influences uric acid (UA) levels and hyperuricemia (HU), but evidence on the role of ultra-processed foods (UPFs) are scarce. The association between UPFs consumption and (1) HU prevalence and UA levels; (2) HU cumulative incidence; and (3) UA level change over a 4-year period was investigated. METHODS AND RESULTS: Cross-sectional and longitudinal analyses were performed using baseline (2008-2010, aged 35-74 years) and second visit (2012-2014) data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants with glomerular filtration rate <60 mL/min/1.73 m2, bariatric surgery, implausible caloric intake, and using urate-lowering therapy (ULT) at baseline were excluded (all analyses). Participants with HU at baseline were excluded from longitudinal analyses. UPFs consumption was assessed using a food frequency questionnaire (FFQ) and categorized by the NOVA classification system (100 g/day). HU was defined as UA≥6.8 mg/dL. Linear, logistic, and mixed-effect linear regressions investigated the associations between UPFs consumption and UA/HU, adjusted for covariates. The final samples included 13,923 (cross-sectional) and 10,517 (longitudinal) individuals. The prevalence of HU was 18.7%, and the cumulative incidence was 4.9%. Greater UPFs consumption was associated with a greater prevalence of HU (OR:1.025 95%CI: 1.006; 1.044) and higher UA levels (ß:0.024 95%CI: 0.016; 0.032). Every additional consumption of 100 g/day of UPFs raised the 4-year cumulative incidence of HU by 5.6% (95%CI: 1.021; 1.092). However, UPFs were not associated with the pace of UA level changes during the study period. CONCLUSION: The present study shows that greater UPFs consumption is associated with another deleterious health consequence: higher UA levels and the risk of having HU.

Chitosan/Albumin Coating Factorial Optimization of Alginate/Dextran Sulfate Cores for Oral Delivery of Insulin.

The design of nanoparticle formulations composed of biopolymers, that govern the physicochemical properties of orally delivered insulin, relies on improving insulin stability and absorption through the intestinal mucosa while protecting it from harsh conditions in the gastrointestinal (GI) tract. Chitosan/polyethylene glycol (PEG) and albumin coating of alginate/dextran sulfate hydrogel cores are presented as a multilayer complex protecting insulin within the nanoparticle. This study aims to optimize a nanoparticle formulation by assessing the relationship between design parameters and experimental data using response surface methodology through a 3-factor 3-level optimization Box-Behnken design. While the selected independent variables were the concentrations of PEG, chitosan and albumin, the dependent variables were particle size, polydispersity index (PDI), zeta potential, and insulin release. Experimental results showed a nanoparticle size ranging from 313 to 585 nm, with PDI from 0.17 to 0.39 and zeta potential ranging from -29 to -44 mV. Insulin bioactivity was maintained in simulated GI media with over 45% cumulative release after 180 min in a simulated intestinal medium. Based on the experimental responses and according to the criteria of desirability on the experimental region's constraints, solutions of 0.03% PEG, 0.047% chitosan and 1.20% albumin provide an optimum nanoparticle formulation for insulin oral delivery.

Usability of Telehealth Systems for Noncommunicable Diseases in Primary Care From the COVID-19 Pandemic Onward: Systematic Review.

BACKGROUND: During the COVID-19 pandemic, telehealth was expanded without the opportunity to extensively evaluate the adopted technology's usability. OBJECTIVE: We aimed to synthesize evidence on health professionals' perceptions regarding the usability of telehealth systems in the primary care of individuals with noncommunicable diseases (NCDs; hypertension and diabetes) from the COVID-19 pandemic onward. METHODS: A systematic review was performed of clinical trials, prospective cohort studies, retrospective observational studies, and studies that used qualitative data collection and analysis methods published in English, Spanish, and Portuguese from March 2020 onward. The databases queried were MEDLINE, Embase, BIREME, IEEE Xplore, BVS, Google Scholar, and grey literature. Studies involving health professionals who used telehealth systems in primary care and managed patients with NCDs from the COVID-19 pandemic onward were considered eligible. Titles, abstracts, and full texts were reviewed. Data were extracted to provide a narrative qualitative evidence synthesis of the included articles. The risk of bias and methodological quality of the included studies were analyzed. The primary outcome was the usability of telehealth systems, while the secondary outcomes were satisfaction and the contexts in which the telehealth system was used. RESULTS: We included 11 of 417 retrieved studies, which had data from 248 health care professionals. These health care professionals were mostly doctors and nurses with prior experience in telehealth in high- and middle-income countries. Overall, 9 studies (82%) were qualitative studies and 2 (18%) were quasiexperimental or multisite trial studies. Moreover, 7 studies (64%) addressed diabetes, 1 (9%) addressed diabetes and hypertension, and 3 (27%) addressed chronic diseases. Most studies used a survey to assess usability. With a moderate confidence level, we concluded that health professionals considered the usability of telehealth systems to be good and felt comfortable and satisfied. Patients felt satisfied using telehealth. The most important predictor for using digital health technologies was ease of use. The main barriers were technological challenges, connectivity issues, low computer literacy, inability to perform complete physical examination, and lack of training. Although the usability of telehealth systems was considered good, there is a need for research that investigates factors that may influence the perceptions of telehealth usability, such as differences between private and public services; differences in the level of experience of professionals, including professional experience and experience with digital tools; and differences in gender, age groups, occupations, and settings. CONCLUSIONS: The COVID-19 pandemic has generated incredible demand for virtual care. Professionals' favorable perceptions of the usability of telehealth indicate that it can facilitate access to quality care. Although there are still challenges to telehealth, more than infrastructure challenges, the most reported challenges were related to empowering people for digital health. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021296887; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=296887. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.21801/ppcrj.2022.82.6.

Telemedicine-Based Management of Oral Anticoagulation Therapy: Systematic Review and Meta-analysis.

BACKGROUND: Oral anticoagulation is the cornerstone treatment of several diseases. Its management is often challenging, and different telemedicine strategies have been implemented to support it. OBJECTIVE: The aim of the study is to systematically review the evidence on the impact of telemedicine-based oral anticoagulation management compared to usual care on thromboembolic and bleeding events. METHODS: Randomized controlled trials were searched in 5 databases from inception to September 2021. Two independent reviewers performed study selection and data extraction. Total thromboembolic events, major bleeding, mortality, and time in therapeutic range were assessed. Results were pooled using random effect models. RESULTS: In total, 25 randomized controlled trials were included (n=25,746 patients) and classified as moderate to high risk of bias by the Cochrane tool. Telemedicine resulted in lower rates of thromboembolic events, though not statistically significant (n=13 studies, relative risk [RR] 0.75, 95% CI 0.53-1.07; I2=42%), comparable rates of major bleeding (n=11 studies, RR 0.94, 95% CI 0.82-1.07; I2=0%) and mortality (n=12 studies, RR 0.96, 95% CI 0.78-1.20; I2=11%), and an improved time in therapeutic range (n=16 studies, mean difference 3.38, 95% CI 1.12-5.65; I2=90%). In the subgroup of the multitasking intervention, telemedicine resulted in an important reduction of thromboembolic events (RR 0.20, 95% CI 0.08-0.48). CONCLUSIONS: Telemedicine-based oral anticoagulation management resulted in similar rates of major bleeding and mortality, a trend for fewer thromboembolic events, and better anticoagulation quality compared to standard care. Given the potential benefits of telemedicine-based care, such as greater access to remote populations or people with ambulatory restrictions, these findings may encourage further implementation of eHealth strategies for anticoagulation management, particularly as part of multifaceted interventions for integrated care of chronic diseases. Meanwhile, researchers should develop higher-quality evidence focusing on hard clinical outcomes, cost-effectiveness, and quality of life. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020159208; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=159208.

End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas- cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. METHODS: We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition. RESULTS: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years. CONCLUSION: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes.

ISE/ISHNE Expert Consensus Statement on ECG Diagnosis of Left Ventricular Hypertrophy: The Change of the Paradigm. The joint paper of the International Society of Electrocardiology and the International Society for Holter Monitoring and Noninvasive Electrocardiology.

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria, i.e. the increased QRS complex amplitude in defined leads. The classical ECG diagnostic paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces. These increased forces are reflected in the augmented QRS amplitude in the corresponding leads. However, the clinical observations document increased QRS amplitude only in the minority of patients with LVH. The low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm.