On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG

Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an open question. Moreover, it remains unclear whether combining DL with FE may improve performance. Methods: We considered three tasks intending to address these research gaps: cardiac arrhythmia diagnosis (multiclass-multilabel classification), atrial fibrillation risk prediction (binary classification), and age estimation (regression). We used an overall dataset of 2.3M 12-lead ECG recordings to train the following models for each task: i) a random forest taking the FE as input was trained as a classical machine learning approach; ii) an end-to-end DL model; and iii) a merged model of FE+DL. Results: FE yielded comparable results to DL while necessitating significantly less data for the two classification tasks and it was outperformed by DL for the regression task. For all tasks, merging FE with DL did not improve performance over DL alone. Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task. We also found that combining FE with DL did not improve over DL alone which suggests that the FE were redundant with the features learned by DL. Significance: Our findings provides important recommendations on what machine learning strategy and data regime to chose with respect to the task at hand for the development of new machine learning models based on the 12-lead ECG

Local governments’ efficiency: study of its seterminants

Public administration efficiency is increasing on the agenda, considering the scarcity of public resources and the greater demand of citizens for their needs to be met. This research aims to study the determinants of the financial efficiency of the Portuguese municipalities. For this , the determinants were grouped into three categories: sociodemographic, political and budgetary. There seems to be evidence, considering the results, that political determinants do not influence the financial efficiency of municipalities. Regarding the sociodemographic determinants, it is observed that the financial efficiency of the municipalities is influenced by location, purchasing power index, tourism and the unemployment rate. Tax revenue and financial independence are budgetary determinants that positively influence the financial efficiency of municipalities. Staff expenditure have a negative effect on it.info:eu-repo/semantics/publishedVersio

Association between C-reactive protein with all-cause mortality in ELSA-Brasil cohort

Background: High-sensitive C-reactive protein (hsCRP) has been proposed as a marker of incident cardiovascular disease and vascular mortality, and it may also be a marker of non-vascular mortality. However, most evidence comes from either North American or European cohorts. The present proposal aims to investigate the association of high-sensitive C-reactive protein with the risk of all-cause mortality in a multi-ethnic Brazilian population Methods: Cohort data from baseline (2008–2010) of 14 792 subjects participating in the Brazilian Longitudinal Study of Adult Health were used. HsCRP was assayed with Immunochemistry. The association of baseline covariates with all-cause mortality was calculated by Cox regression for univariate model and adjusted for different confounders after mean follow-up of 8.0 ± 1.1 years. The final model was adjusted for age, sex, self-rated race/ethnicity, schooling, health behaviours and prevalent chronic disease. Results: The risk of death increased steadily by quartiles of hsCRP from 1.45 (95% Confidence Interval: 1.05, 2.01) in Quartile 2 to 1.95 (1.42, 2.69) in Quartile 4 compared to Quartile 1. Furthermore, the persistence of a significant graded association after the exclusion of deaths in the first year of follow-up suggests that these results are unlikely to be due to reverse causality. Finally, the hazard ratios were unaffected by the exclusion of participants that had self-reported past medical history for diabetes, cancer and chronic obstructive pulmonary disease. Conclusions: Our study shows that hsCRP levels is associated with mortality in a highly admixed population, independently of a large set of lifestyle and clinical variables

Cost-Effectiveness of Routine Screening for Cardiac Toxicity in Patients Treated with Imatinib in Brazil

AbstractWe performed a cost-effectiveness study of different strategies of screening for cardiotoxicity in patients receiving imatinib, the first strategy based on yearly echocardiograms in all patients and the second strategy based on yearly B-type natriuretic peptide level measurement, reserving echocardiograms for patients with an abnormal test result. Results are presented in terms of additional cost per diagnosis as compared with not performing any screening. From the Brazilian private sector’s perspective, strategies 1 and 2 resulted in additional costs of US $30,951.53 and US$19,925.64 per diagnosis of cardiotoxicity, respectively. From the perspective of the Brazilian public health system, the same strategies generated additional costs of US $7,668.00 and US$20,232.87 per diagnosis, respectively. In our study, systematic screening for cardiotoxicity in patients using imatinib has a high cost per diagnosis. If screening is to be adopted, a strategy based on B-type natriuretic peptide level measurement, reserving echocardiography for patients with abnormal results, results in lower costs per diagnosis in the private sector. From the public health system’s perspective, costs per diagnosis will greatly depend on the reimbursement values adopted for B-type natriuretic peptide level measurement

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification

Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available

Síndrome purpúrico-papular em "luvas e meias" por parvovírus B19: relato de caso

We present a case of papular-purpuric "gloves and socks" syndrome (PPGSS) in an adult male with acute parvovirus B19 infection. The patient displayed the classical features of fever, oral lesions, and purpura on hands and feet, but the purpuric lesions on the feet evolved to superficial skin necrosis, a feature not previously described in this syndrome. We believe this is the first reported case of PPGSS occurring in Brazil.Um caso de síndrome purpúrico-papular em "luvas e meias" devido à infecção aguda por parvovírus B19 é descrito em um homem adulto que, além das manifestações clássicas de febre, lesões orais e púrpura em mãos e pés, evoluiu com icterícia e necrose cutânea superficial dos pés, características até então não descritas nesta síndrome. Acreditamos tratar-se do primeiro caso descrito no Brasil

Longitudinal study of patients with chronic Chagas cardiomyopathy in Brazil (SaMi-Trop project): a cohort profile.

PurposeWe have established a prospective cohort of 1959 patients with chronic Chagas cardiomyopathy to evaluate if a clinical prediction rule based on ECG, brain natriuretic peptide (BNP) levels, and other biomarkers can be useful in clinical practice. This paper outlines the study and baseline characteristics of the participants.ParticipantsThe study is being conducted in 21 municipalities of the northern part of Minas Gerais State in Brazil, and includes a follow-up of 2 years. The baseline evaluation included collection of sociodemographic information, social determinants of health, health-related behaviours, comorbidities, medicines in use, history of previous treatment for Chagas disease, functional class, quality of life, blood sample collection, and ECG. Patients were mostly female, aged 50-74 years, with low family income and educational level, with known Chagas disease for >10 years; 46% presented with functional class >II. Previous use of benznidazole was reported by 25.2% and permanent use of pacemaker by 6.2%. Almost half of the patients presented with high blood cholesterol and hypertension, and one-third of them had diabetes mellitus. N-terminal of the prohormone BNP (NT-ProBNP) level was >300 pg/mL in 30% of the sample.Findings to dateClinical and laboratory markers predictive of severe and progressive Chagas disease were identified as high NT-ProBNP levels, as well as symptoms of advanced heart failure. These results confirm the important residual morbidity of Chagas disease in the remote areas, thus supporting political decisions that should prioritise in addition to epidemiological surveillance the medical treatment of chronic Chagas cardiomyopathy in the coming years. The São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) represents a major challenge for focused research in neglected diseases, with knowledge that can be applied in primary healthcare.Future plansWe will continue following this patients' cohort to provide relevant information about the development and progression of Chagas disease in remotes areas, with social and economic inequalities.Trial registration numberNCT02646943; Pre-results

COMPATIBILIDADE VISUAL DAS REDES DE DRENAGENS DA BACIA HIDROGRÁFICA DO RIO JORDÃO (MG) EXTRAÍDAS DOS MDEs SRTM E ASTER USANDO OPERADORES DE SIMPLIFICAÇÃO E SUAVIZAÇÃO

A generalização cartográfica visa adaptar as feições cartográficas e as suas relações geográficas de acordo com a escala de representação do produto cartográfico. Com o advento dos computadores, os operadores permitem observar essa adaptação geométrica e semântica desse conjunto de feições segundo a função e a finalidade estabelecidas para esse produto. Com o propósito de analisar visualmente a generalização cartográfica de feições lineares extraídas do Modelo Digital de Elevação (MDE), a partir da aplicação dos operadores simplificação e suavização, tornou-se o foco deste trabalho. No caso, foram usados os algoritmos POINT-REMOVE, para a simplificação e o PAEK, para a suavização das redes de drenagem extraídas a partir do MDE ASTER e do MDE SRTM, respectivamente, com 30 e 90 metros de resolução espacial.  Esses operadores estão disponíveis no software ARCGis 10.1 e a área de estudo foi a  bacia hidrográfica do Rio Jordão (MG). Houve controle da qualidade posicional da carta topográfica gerada em meio digital e dos MDEs e a comparação visual ocorreu com a sobreposição desses produtos. Para essa análise visual se valeu dos princípios dafotointerpretação e do modelo de comunicação cartográfica e os resultados apontam maior similitude entre os trechos lineares com a carta topográfica dessa bacia, independentemente do MDE. Por outro lado, a resolução espacial e a topografia interferem na extração dessa rede de drenage

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