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- Lima, Emilly M., et al.
(författare)
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Deep neural network-estimated electrocardiographic age as a mortality predictor
- 2021
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- The electrocardiogram (ECG) is the most commonly used exam for the screening and evaluation of cardiovascular diseases. Here, the authors propose that the age predicted by artificial intelligence from the raw ECG tracing can be a measure of cardiovascular health and provide prognostic information. 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.
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| 3. |
- Paixão, Gabriela M. M., et al.
(författare)
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Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients
- 2021
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Ingår i: Hearts. - : MDPI AG. - 2673-3846. ; 2:4, s. 449-458
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Tidskriftsartikel (refereegranskat)abstract
- Computerized electrocardiography (ECG) has been widely used and allows linkage to electronic medical records. The present study describes the development and clinical applications of an electronic cohort derived from a digital ECG database obtained by the Telehealth Network of Minas Gerais, Brazil, for the period 2010–2017, linked to the mortality data from the national information system, the Clinical Outcomes in Digital Electrocardiography (CODE) dataset. From 2,470,424 ECGs, 1,773,689 patients were identified. A total of 1,666,778 (94%) underwent a valid ECG recording for the period 2010 to 2017, with 1,558,421 patients over 16 years old; 40.2% were men, with a mean age of 51.7 [SD 17.6] years. During a mean follow-up of 3.7 years, the mortality rate was 3.3%. ECG abnormalities assessed were: atrial fibrillation (AF), right bundle branch block (RBBB), left bundle branch block (LBBB), atrioventricular block (AVB), and ventricular pre-excitation. Most ECG abnormalities (AF: Hazard ratio [HR] 2.10; 95% CI 2.03–2.17; RBBB: HR 1.32; 95%CI 1.27–1.36; LBBB: HR 1.69; 95% CI 1.62–1.76; first degree AVB: Relative survival [RS]: 0.76; 95% CI0.71–0.81; 2:1 AVB: RS 0.21 95% CI0.09–0.52; and RS 0.36; third degree AVB: 95% CI 0.26–0.49) were predictors of overall mortality, except for ventricular pre-excitation (HR 1.41; 95% CI 0.56–3.57) and Mobitz I AVB (RS 0.65; 95% CI 0.34–1.24). In conclusion, a large ECG database established by a telehealth network can be a useful tool for facilitating new advances in the fields of digital electrocardiography, clinical cardiology and cardiovascular epidemiology.
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| 5. |
- Dima, B., et al.
(författare)
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Fungal Systematics and Evolution: FUSE 7
- 2021
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Ingår i: Sydowia. - 0082-0598. ; 73, s. 271-340
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Tidskriftsartikel (refereegranskat)abstract
- In this 7th contribution to the Fungal Systematics and Evolution series published by Sydowia, the authors formally describe 14 species: Cantharomyces paschalis, Cryptandromyces pinguis, C. tricornis, Laboulbenia amblystomi (Laboulbeniales); Cortinarius squamosus, Entoloma brunneicoeruleum, E. callipygmaeum, E. minutigranulosum, E. perasprellum, E. pulchripes, E. tigrinum, E. timidum, E. violaceoserrulatum (Agaricales); and Suillus quercinus (Boletales). The following new country records are reported: Crepidotus malachioides from Italy, Leucoagaricus mucrocystis from French Guiana, Pluteus multiformis from Turkey (Agaricales); Herpomyces periplanetae from Benin, the D.R. Congo, and Togo (Herpomycetales); Melanustilospora ari from Pakistan (Urocystidales); Neopestalotiopsis clavispora causing fruit rot on Zizyphus mauritiana from India (Amphisphaeriales); and Phytopythium chamaehyphon and Pp. litorale from Brazil (Peronosporales). Finally, a new combination is proposed based on morphology, ecology, and phylogenetic analysis: Rhodocollybia asema (Agaricales).
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| 6. |
- Ribeiro, Antônio H., et al.
(författare)
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Automatic diagnosis of the 12-lead ECG using a deep neural network
- 2020
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Ingår i: Nature Communications. - : NATURE PUBLISHING GROUP. - 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- 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. The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. In that context, the authors present a Deep Neural Network (DNN) that recognizes different abnormalities in ECG recordings which matches or outperform cardiology and emergency resident medical doctors.
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