Deep neural network-estimated electrocardiographic age as a mortality predictor

Lima, Emilly M.; Ribeiro, Antônio H.; Paixão, Gabriela M. M.; Ribeiro, Manoel Horta; Pinto-Filho, Marcelo Martins; Gomes, Paulo R.; Oliveira, Derick M.; Sabino, Éster Cerdeira; Duncan, Bruce Bartholow; Giatti, Luana; Barreto, Sandhi Maria; Meira, Wagner; Schön, Thomas B.; Ribeiro, Antônio Luiz Pinho

doi:10.1038/s41467-021-25351-7

Cited by 86 publications

(62 citation statements)

References 39 publications

(48 reference statements)

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“…Such results at least superficially correspond to the findings in our study that the Heart Age Gap increased with increasing burden of cardiovascular risk. Another AI method similar to that of Attia et al also more recently reported similarly encouraging results 10 .…”

Section: Comparison With Other Heart (Or Vascular) Agesmentioning

confidence: 72%

“…Heart rate, min -1 65 [11] 63 [10] 70 [11] 67 [ Age. Notably, P-wave duration is markedly different between these two patients, and heart rate is higher for the male than the female, helping drive the Heart Age higher in the male.…”

Section: Discussionmentioning

confidence: 99%

“…Or the extent to which unanticipated results might merely relate to excess dependency on the particular characteristics of a given DNN AI model’s training set 47 . In addition, a major flaw in all DNN-based AI ECG age models of which we are aware is that their age predictions were made using training datasets that also included individuals with both cardiovascular risk factors and established disease 8,10 . For ECG Heart Age to be used as a marker of potentially reversible cardiovascular disease and risk, it is imperative that ECG Heart Age agrees with chronological age in healthy populations, since it is the deviations from the line of identity in this relationship that form the basis for disease-related assessment and risk.…”

Section: Discussionmentioning

confidence: 99%

“…Different means of expressing a heart or vascular age have recently been published 13 , including the use of information in the 10-second 12-lead ECG to estimate ECG Heart Age based on artificial intelligence (AI) 7,8,10 . For example, Attia, et al, showed that by using a deep neural network (DNN) AI technique, a patient's chronological age could be predicted, and that if the difference between the predicted and actual age was small, prognosis was good 8 .…”

Section: Comparison With Other Heart (Or Vascular) Agesmentioning

confidence: 99%

“…a "Heart Age Gap". A Heart Age can either be determined by translating risk factor scores to what age a resultant score would represent in an individual with no risk factors, or it can be based on electrocardiographic changes [7][8][9][10][11][12] . Describing the risk to the patient using Heart Age has been reported to reduce metabolic risk factors and may have the advantage of being easily understood by the patient 11,13 .…”

Section: Introductionmentioning

confidence: 99%

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Heart age estimated using explainable advanced electrocardiography

Lindow

Palencia-Lamela

Schlegel

2021

Preprint

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Background: Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesized that explainable measures from the 10-second 12-lead ECG could successfully predict Bayesian ECG Heart Age. Methods: Advanced analysis was performed on ECGs from healthy subjects and patients with cardiovascular risk or proven heart disease. Regression models were used to predict a Bayesian 5-minute ECG Heart Age from the standard resting 10-second 12-lead ECG. The difference between 10-second ECG Heart Age and chronological age was compared. Results: In total, 2,771 subjects were included (n=1682 healthy volunteers, n=305 with cardiovascular risk factors, n=784 with cardiovascular disease). Overall, 10-second Heart Age showed strong agreement with the 5-minute Heart Age (R2=0.94, p<0.001, mean±SD bias 0.0±5.1 years). The difference between 10-second ECG Heart Age and chronological age was 0.0±5.7 years in healthy individuals, 7.4±7.3 years in subjects with cardiovascular risk factors (p<0.001), and 14.3±9.2 years for patients with cardiovascular disease (p<0.001). Conclusions: ECG Heart Age can be accurately estimated from a 10-second 12-lead ECG in a transparent and explainable fashion based on known ECG measures, without artificial intelligence techniques. The difference between ECG Heart Age and chronological age increases markedly with cardiovascular risk and disease.

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Section: Comparison With Other Heart (Or Vascular) Agesmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Comparison With Other Heart (Or Vascular) Agesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Heart age estimated using explainable advanced electrocardiography

Lindow

Palencia-Lamela

Schlegel

2021

Preprint

View full text Add to dashboard Cite

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Mass Spectrometry for Lipidomics: Methods and Applications – Aging and A lzheimer's Disease

Huynh

Beyene

Wang

et al. 2023

Mass Spectrometry for Lipidomics

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A Core–Shell Nanoreinforced Ion‐Conductive Implantable Hydrogel Bioelectronic Patch with High Sensitivity and Bioactivity for Real‐Time Synchronous Heart Monitoring and Repairing

Shen

Zhang

Han

et al. 2023

Adv Healthcare Materials

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To achieve synchronous repair and real‐time monitoring the infarcted myocardium based on an integrated ion‐conductive hydrogel patch is challenging yet intriguing. Herein, a novel synthetic strategy is reported based on core–shell‐structured curcumin‐nanocomposite‐reinforced ion‐conductive hydrogel for synchronous heart electrophysiological signal monitoring and infarcted heart repair. The nanoreinforcement and multisite cross‐linking of bioactive curcumin nanoparticles enable well elasticity with negligible hysteresis, implantability, ultrahigh mechanoelectrical sensitivity (37 ms), and reliable sensing capacity (over 3000 cycles) for the nanoreinforced hydrogel. Results of in vitro and in vivo experiments demonstrate that such solely physical microenvironment of electrophysiological and biomechanical characteristics combining with the role of bioactive curcumin exert the synchronous benefit of regulating inflammatory microenvironment, promoting angiogenesis, and reducing myocardial fibrosis for effective myocardial infarction (MI) repair. Especially, the hydrogel sensors offer the access for achieving accurate acquisition of cardiac signals, thus monitoring the whole MI healing process. This novel bioactive and electrophysiological‐sensing ion‐conductive hydrogel cardiac patch highlights a versatile strategy promising for synchronous integration of in vivo real‐time monitoring the MI status and excellent MI repair performance.

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Deep neural network-estimated electrocardiographic age as a mortality predictor

Cited by 86 publications

References 39 publications

Heart age estimated using explainable advanced electrocardiography

Heart age estimated using explainable advanced electrocardiography

Mass Spectrometry for Lipidomics: Methods and Applications – Aging and A lzheimer's Disease

A Core–Shell Nanoreinforced Ion‐Conductive Implantable Hydrogel Bioelectronic Patch with High Sensitivity and Bioactivity for Real‐Time Synchronous Heart Monitoring and Repairing

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