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Deep neural network-estimated electrocardiographic age as a mortality predictor

Lima, Emilly M. (author)
Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
Horta Ribeiro, Antônio (author)
Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens,Univ Fed Minas Gerais, Dept Ciencia Comp, Belo Horizonte, MG, Brazil
Paixao, Gabriela M. M. (author)
Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
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Horta Ribeiro, Manoel (author)
Ecole Polytech Fed Lausanne, Lausanne, Switzerland
Pinto-Filho, Marcelo M. (author)
Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
Gomes, Paulo R. (author)
Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
Oliveira, Derick M. (author)
Univ Fed Minas Gerais, Dept Ciencia Comp, Belo Horizonte, MG, Brazil
Sabino, Ester C. (author)
Univ Sao Paulo, Inst Med Trop, Fac Med, Sao Paulo, Brazil
Duncan, Bruce B. (author)
Univ Fed Rio Grande do Sul, Programa Posgrad Epidemiol, Porto Alegre, RS, Brazil.;Univ Fed Rio Grande do Sul, Hosp Clin Porto Alegre, Porto Alegre, RS, Brazil
Giatti, Luana (author)
Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
Barreto, Sandhi M. (author)
Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
Meira Jr, Wagner (author)
Univ Fed Minas Gerais, Dept Ciencia Comp, Belo Horizonte, MG, Brazil
Schön, Thomas B., Professor, 1977- (author)
Uppsala universitet,Avdelningen för systemteknik,Artificiell intelligens
Ribeiro, Antonio Luiz P. (author)
Univ Fed Minas Gerais, Hosp Clin, Telehlth Ctr, Belo Horizonte, MG, Brazil; Univ Fed Minas Gerais, Fac Med, Belo Horizonte, MG, Brazil
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 (creator_code:org_t)
2021-08-25
2021
English.
In: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
Related links:
https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
https://www.nature.c...
https://urn.kb.se/re...
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  • Journal article (peer-reviewed)
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  • 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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

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