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<p>Convolutional neural networks (CNNs) can estimate electrocardiogram (ECG)-based heart age. We compared three published CNNs in the Tromsø Study cohort (7,108 participants) for accuracy, agreement, and prognostic value. Mean absolute error versus chronological age was 6.8, 7.8, and 6.4 years. Correlations with age were ~0.71–0.73 and agreement across CNNs was high (overall ICC 0.86). Using Cox models, we estimated hazard ratios per SD of δ-age (ECG age minus chronological age) for myocardial infarction, stroke, cardiovascular mortality, and all-cause mortality; discrimination was quantified by cross-validated C-index. δ-age predicted higher risk across outcomes; associations were strongest for δ-age<sub>1</sub> with myocardial infarction and all-cause mortality (HR 1.36 (1.11, 1.67) and 1.27 (1.08, 1.50)) and for δ-age<sub>2</sub> with stroke and cardiovascular mortality (HR 1.45 (1.17, 1.80) and 1.48 (1.07, 2.05)). C-indices were similar across models. Despite architectural and training-set differences, CNNs yielded consistent ECG ages and comparable risk prediction in an external population.</p>

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Electrocardiogram derived heart age models agreement, accuracy and predictive ability in the Tromsø study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月20日

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  • Arya Panthalanickal Vijayakumar,
  • Tom Wilsgaard,
  • Henrik Schirmer,
  • Ernest Diez Benavente,
  • René van Es,
  • Rutger R. van de Leur,
  • Haakon Lindekleiv,
  • Zachi I. Attia,
  • Francisco Lopez-Jimenez,
  • David A. Leon,
  • Olena Iakunchykova

摘要

Convolutional neural networks (CNNs) can estimate electrocardiogram (ECG)-based heart age. We compared three published CNNs in the Tromsø Study cohort (7,108 participants) for accuracy, agreement, and prognostic value. Mean absolute error versus chronological age was 6.8, 7.8, and 6.4 years. Correlations with age were ~0.71–0.73 and agreement across CNNs was high (overall ICC 0.86). Using Cox models, we estimated hazard ratios per SD of δ-age (ECG age minus chronological age) for myocardial infarction, stroke, cardiovascular mortality, and all-cause mortality; discrimination was quantified by cross-validated C-index. δ-age predicted higher risk across outcomes; associations were strongest for δ-age1 with myocardial infarction and all-cause mortality (HR 1.36 (1.11, 1.67) and 1.27 (1.08, 1.50)) and for δ-age2 with stroke and cardiovascular mortality (HR 1.45 (1.17, 1.80) and 1.48 (1.07, 2.05)). C-indices were similar across models. Despite architectural and training-set differences, CNNs yielded consistent ECG ages and comparable risk prediction in an external population.