Clinical and electrocardiographic outcomes evaluated by telemedicine of outpatients with clinical suspicion of COVID-19 treated with chloroquine compounds in Brazil†.

Aims: To evaluate clinical and electrocardiographic outcomes of patients with COVID-19, comparing those using chloroquine compounds (chloroquine) to individuals without specific treatment. Methods: Outpatients with suspected COVID-19 in Brazil who had at least one tele-electrocardiography (ECG) recorded in a telehealth system were enrolled in two arms (Group 1: chloroquine and Group 2: without specific treatment) and one registry (Group 3: other treatments). Outcomes were assessed through follow-up calls (phone contact, days 3 and 14) and linkage to national mortality and hospitalization databases. The primary outcome was composed of: hospitalization, intensive care admission, mechanical ventilation, and all-cause death, and the ECG outcome was the occurrence of major abnormalities by the Minnesota code. Significant variables in univariable logistic regression were included in 4 models: 1-unadjusted; 2-adjusted for age and sex; 3-model 2 + cardiovascular risk factors and 4-model 3 + COVID-19 symptoms. Results: In 303 days, 712 (10.2%) patients were allocated in group 1, 3,623 (52.1%) in group 2 and 2,622 (37.7%) in group 3; 1,969 had successful phone follow-up (G1: 260, G2: 871, and G3: 838). A late follow-up ECG was obtained for 917 (27.2%) patients [group 1: 81 (11.4%), group 2: 512 (14.1%), group 3: 334 (12.7%)]. In adjusted models, chloroquine was independently associated with greater chance of the composite clinical outcome: phone contact (model 4): OR = 3.24 (95% CI 2.31-4.54), p < 0.001. Chloroquine was also independently associated with higher mortality, assessed by phone + administrative data (model 3): OR = 1.67 (95% CI 1.20-2.28). However, chloroquine did not associate with the occurrence of major ECG abnormalities [model 3; OR = 0.80 (95% CI 0.63-1.02, p = 0.07)]. Abstracts with partial results of this work was accepted in the American Heart Association Scientific Sessions, November 2022, in Chicago, IL, USA. Conclusion: Chloroquine was associated with a higher risk of poor outcomes in patients suspected to have COVID-19 when compared to those who received standard care. Follow-up ECGs were obtained in only 13.2% of patients and did not show any significant differences in major abnormalities amongst the three groups. In the absence of early ECG changes, other side effects, late arrhythmias or deferral of care may be hypothesized to explain the worse outcomes.

A novel algorithm to assess the quality of 12-lead ECG recordings: validation in a real telecardiology application.

Objective. Automatic detection of Electrocardiograms (ECG) quality is fundamental to minimize costs and risks related to delayed diagnosis due to low ECG quality. Most algorithms to assess ECG quality include non-intuitive parameters. Also, they were developed using data non-representative of a real-world scenario, in terms of pathological ECGs and overrepresentation of low-quality ECG. Therefore, we introduce an algorithm to assess 12-lead ECG quality, Noise Automatic Classification Algorithm (NACA) developed in Telehealth Network of Minas Gerais (TNMG).Approach. NACA estimates a signal-to-noise ratio (SNR) for each ECG lead, where 'signal' is an estimated heartbeat template, and 'noise' is the discrepancy between the template and the ECG heartbeat. Then, clinically-inspired rules based on SNR are used to classify the ECG as acceptable or unacceptable. NACA was compared with Quality Measurement Algorithm (QMA), the winner of Computing in Cardiology Challenge 2011 (ChallengeCinC) by using five metrics: sensitivity (Se), specificity (Sp), positive predictive value (PPV),F2, and cost reduction resulting from adoption of the algorithm. Two datasets were used for validation: TestTNMG, consisting of 34 310 ECGs received by TNMG (1% unacceptable and 50% pathological); ChallengeCinC, consisting of 1000 ECGs (23% unacceptable, higher than real-world scenario).Main results. Both algorithms reached a similar performance on ChallengeCinC, although NACA performed considerably better than QMA in TestTNMG (Se = 0.89 versus 0.21; Sp = 0.99 versus 0.98; PPV = 0.59 versus 0.08;F2= 0.76 versus 0.16 and cost reduction 2.3 ± 1.8% versus 0.3 ± 0.3%, respectively).Significance. Implementing of NACA in a telecardiology service results in evident health and financial benefits for the patients and the healthcare system.

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.

Ventricular pre-excitation in primary care patients: Evaluation of the risk of mortality.

BACKGROUND: Ventricular pre-excitation is characterized by the presence of atrioventricular accessory pathways, predisposing to arrhythmias. Although it is well established that risk stratification in symptomatic patients should be invasive, there is a lack of evidence of the benefit in asymptomatic. OBJECTIVE: Evaluate ventricular pre-excitation in the electrocardiogram (ECG) as a risk factor for overall mortality in patients of Telehealth Network of Minas Gerais (TNMG), Brazil. METHODS: This observational study was developed with the database of digital ECGs (2010-2017) from TNMG. The electronic cohort was obtained by linking data from ECG exams and those from the national mortality information system. Only the first ECG was considered. Clinical data were self-reported, and ECGs were interpreted manually by cardiologists and automatically by the Glasgow University Interpreter software. Hazard ratio (HR) for mortality was estimated using weighted Cox regression. RESULTS: Nearly 1 665 667 patients were included (median age: 50 [Q1: 34; Q3: 63] years; 41.4% were male). In a mean follow-up of 3.7 years, the overall mortality rate was 3.1%. The prevalence of ventricular pre-excitation was 0.07%. In multivariate analysis, adjusting for sex and age, ventricular pre-excitation was not associated with an increased risk of mortality (HR: 1.41; 95% confidence interval [CI]: 0.56-3.57; p = .47) when compared to the whole sample or to patients with normal ECG (HR: 1.41; 95% CI: 0.53-4.36; p = .43). In a subanalysis on accessory pathway location, there was no evidence of a higher risk of death related to any location. CONCLUSION: Ventricular pre-excitation was not associated with an increased risk of mortality in a primary care cohort.

Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study.

Aims: Atrial fibrillation (AF) is a public health problem and its prevalence is increasing worldwide. Electronic cohorts, with large electrocardiogram (ECG) databases linked to mortality data, can be useful in determining prognostic value of ECG abnormalities. Our aim is to evaluate the risk of mortality in patients with AF from Brazil. Methods: This observational retrospective study of primary care patients was developed with the digital ECG database from the Telehealth Network of Minas Gerais, Brazil. ECGs performed from 2010 to 2017 were interpreted by cardiologists and the University of Glasgow automated analysis software. An electronic cohort was obtained linking data from ECG exams and those from a national mortality information system, using standard probabilistic linkage methods. We considered only the first ECG of each patient. Patients under 16 years were excluded. Hazard ratios (HR) for mortality were adjusted for demographic and self-reported clinical factors and estimated with Cox regression. Results: From a dataset of 1,773,689 patients, 1,558,421 were included, mean age 51.6 years; 40.2% male. There were 3.34% deaths from all causes in 3.68 years of median follow up. The prevalence of AF was 1.33%. AF was an independent risk factor for all-cause mortality (HR 2.10, 95%CI 2.03-2.17) and cardiovascular mortality (HR 2.06, 95%CI 1.86-2.29). Females with AF had a higher risk of overall and cardiovascular mortality compared with males (p < 0.001). Conclusions: AF was a strong predictor of cardiovascular and all-cause mortality in a primary care population, with increased risk in women. Condensed abstract: To assess risk of mortality in AF patients, an electronic cohort was obtained linking data from ECG exams of Brazilian primary care patients and a national mortality information system. From 1,558,421 patients, AF (prevalence 1.33%) carried a higher risk of overall and cardiovascular mortality, with increased risk in women. What's New: This is the first study with a large Brazilian electronic cohort to evaluate the risk of mortality linked to AF in primary care patients.AF patients from a Brazilian primary care population had a higher risk of death for all causes (HR 2.10, 95%CI 2.03-2.17) and cardiovascular mortality (HR 2.06, 95%CI 1.86-2.29).Female patients with AF had an increased risk of overall and cardiovascular mortality compared with male patients (p < 0.001).

Author Correction: Automatic diagnosis of the 12-lead ECG using a deep neural network.

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

Automatic diagnosis of the 12-lead ECG using a deep neural network.

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

Evaluation of mortality in bundle branch block patients from an electronic cohort: Clinical Outcomes in Digital Electrocardiography (CODE) study.

BACKGROUND: Left bundle branch block is recognized as a marker of higher risk of death, but the prognostic value of the right bundle branch block in the general population is still controversial. Our aim is to evaluate the risk of overall and cardiovascular mortality in patients with right (RBBB) and left bundle branch block (LBBB) in a large electronic cohort of Brazilian patients. METHODS: This observational retrospective study was developed with the database of digital ECGs from Telehealth Network of Minas Gerais, Brazil (TNMG). All ECGs performed from 2010 to 2017 in primary care patients over 16 years old were assessed. The electronic cohort was obtained by linking data from ECG exams (name, sex, date of birth, city of residence) and those from national mortality information system, using standard probabilistic linkage methods (FRIL: Fine-grained record linkage software, v.2.1.5, Atlanta, GA). Only the first ECG of each patient was considered. Clinical data were self-reported, and ECGs were interpreted manually by cardiologists and automatically by the Glasgow University Interpreter software. Hazard ratio (HR) for mortality was estimated using Cox regression. RESULTS: From a dataset of 1,773,689 patients, 1,558,421 primary care patients over 16 years old underwent a valid ECG recording during 2010 to 2017. We excluded 17,359 patients that didn't have a valid QRS measure from the Glasgow program and 11,091 patients from the control group that had QRS equal or above 120 ms and were not RBBB or LBBB. Therefore, 1,529,971 were included (median age 52 [Q1:38; Q3:65] years; 40.2% were male). In a mean follow-up of 3.7 years, the overall mortality rate was 3.34%. RBBB was more frequent (2.42%) than LBBB (1.32%). In multivariate analysis, adjusting for sex, age and comorbidities, both patients with RBBB (HR 1.32; CI 95% 1.27-1.37) and LBBB (HR 1.69; CI 95% 1.62-1.76) had higher risk of overall mortality. Women with RBBB had an increased risk of all-cause death compared to men (p < 0.001). Cardiovascular mortality was higher in patients with LBBB (HR 1.77; CI 95% 1.55-2.01), but not for RBBB. CONCLUSIONS: Patients with RBBB and LBBB had higher risk of overall mortality. Women with RBBB had more risk of all-cause death than men. LBBB was associated with higher risk of cardiovascular mortality.

Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study.

Digital electrocardiographs are now widely available and a large number of digital electrocardiograms (ECGs) have been recorded and stored. The present study describes the development and clinical applications of a large database of such digital ECGs, namely the CODE (Clinical Outcomes in Digital Electrocardiology) study. ECGs obtained by the Telehealth Network of Minas Gerais, Brazil, from 2010 to 17, were organized in a structured database. A hierarchical free-text machine learning algorithm recognized specific ECG diagnoses from cardiologist reports. The Glasgow ECG Analysis Program provided Minnesota Codes and automatic diagnostic statements. The presence of a specific ECG abnormality was considered when both automatic and medical diagnosis were concordant; cases of discordance were decided using heuristisc rules and manual review. The ECG database was linked to the national mortality information system using probabilistic linkage methods. From 2,470,424 ECGs, 1,773,689 patients were identified. After excluding the ECGs with technical problems and patients <16 years-old, 1,558,415 patients were studied. High performance measures were obtained using an end-to-end deep neural network trained to detect 6 types of ECG abnormalities, with F1 scores >80% and specificity >99% in an independent test dataset. We also evaluated the risk of mortality associated with the presence of atrial fibrillation (AF), which showed that AF was a strong predictor of cardiovascular mortality and mortality for all causes, with increased risk in women. In conclusion, a large database that comprises all ECGs performed by a large telehealth network can be useful for further developments in the field of digital electrocardiography, clinical cardiology and cardiovascular epidemiology.

Cardiac Involvement by Yellow Fever(from the PROVAR+ Study).

Incidence of Yellow Fever (YF) has increased in Brazil, and cardiac findings such as bradyarrhythmias and conduction abnormalities have been described. We aimed to perform a comprehensive cardiac evaluation of patients with YF, and to assess the association between cardiac involvement and disease severity. Patients hospitalized with YF from February to March 2018 underwent clinical and laboratory evaluation, focused bedside echocardiography (GE Vivid IQ), electrocardiogram and, in case of alterations, 24-hours Holter. Patients were divided into 2 groups according to YF severity. Five patients underwent magnetic resonance imaging and 3 had necropsy. Seventy patients had confirmed YF, 69% with severe form. Mean age was 48 ± 14 years, 63 (90%) were males and 5 (7%) died. Significant electrocardiogram abnormalities were present in 52% of patients with mild/moderate form of YF (G1) and 77% of those with severe form (G2), p = 0.046. Sinus bradycardia was observed in 24% (N = 17): G1 23% versus G2 25%, p = 0.67. Among 32 patients who underwent Holter, 14 (44%) had mean HR <60 beats per minute, being 8 from G2. Echocardiogram revealed left ventricular dysfunction in 4 (6%) patients, from G2. Left ventricular wall thickening with a hyper-refringent myocardial texture suggesting infiltration was observed in 17 patients (G1 18% vs G2 27%, p = 0.55). One magnetic resonance (G2) was suggestive of myocarditis, and one necropsy revealed areas of myocardial necrosis and acute myocarditis. In conclusion, cardiac involvement was observed in patients with YF, most commonly bradycardia and myocardial hyper-refringent texture suggestive of infiltration.