Leung Robert, Wang Biqi, Gottbrecht Matthew, Doerr Adam, Marya Neil, Soni Apurv, McManus David D, Lin Honghuang
Program in Digital Medicine, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States.
Division of Health Systems Science, Department of Medicine, UMass Chan Medical School, Worcester, MA, United States.
Front Cardiovasc Med. 2024 Jun 28;11:1368094. doi: 10.3389/fcvm.2024.1368094. eCollection 2024.
Stroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified. Recently, deep neural networks (DNNs) have been used to determine age from ECGs, otherwise known as the electrocardiographic-age (ECG-age), which predicts clinical outcomes. However, the relationship between ECG-age and stroke has not been well studied. We hypothesized that ECG-age is associated with incident stroke.
In this study, UK Biobank participants with available ECGs (from 2014 or later). ECG-age was estimated using a deep neural network (DNN) applied to raw ECG waveforms. We calculated the Δage (ECG-age minus chronological age) and classified individuals as having normal, accelerated, or decelerated aging if Δage was within, higher, or lower than the mean absolute error of the model, respectively. Multivariable Cox proportional hazards regression models adjusted for age, sex, and clinical factors were used to assess the association between Δage and incident stroke.
The study population included 67,757 UK Biobank participants (mean age 65 ± 8 years; 48.3% male). Every 10-year increase in Δage was associated with a 22% increase in incident stroke [HR, 1.22 (95% CI, 1.00-1.49)] in the multivariable-adjusted model. Accelerated aging was associated with a 42% increase in incident stroke [HR, 1.42 (95% CI, 1.12-1.80)] compared to normal aging. In addition, Δage was associated with prevalent stroke [OR, 1.28 (95% CI, 1.11-1.49)].
DNN-estimated ECG-age was associated with incident and prevalent stroke in the UK Biobank. Further investigation is required to determine if ECG-age can be used as a reliable biomarker of stroke risk.
尽管在预防和治疗方面有所改善，但中风仍然是全球死亡和残疾的主要原因。传统的中风风险计算器存在偏差且不够精确。需要识别新的中风预测指标。最近，深度神经网络（DNN）已被用于从心电图（ECG）中确定年龄，即心电图年龄（ECG-age），它可以预测临床结果。然而，ECG-age与中风之间的关系尚未得到充分研究。我们假设ECG-age与中风事件有关。
在本研究中，选取了英国生物银行中可获取心电图（来自2014年或更晚）的参与者。使用应用于原始心电图波形的深度神经网络（DNN）估计ECG-age。我们计算了年龄差（ECG-age减去实际年龄），并根据年龄差是否在模型平均绝对误差范围内、高于或低于模型平均绝对误差，将个体分别分类为具有正常、加速或减速衰老。使用调整了年龄、性别和临床因素的多变量Cox比例风险回归模型来评估年龄差与中风事件之间的关联。
研究人群包括67757名英国生物银行参与者（平均年龄65±8岁；48.3%为男性）。在多变量调整模型中，年龄差每增加10岁，中风事件增加22%[风险比（HR），1.22（95%置信区间，1.00 - 1.49）]。与正常衰老相比，加速衰老与中风事件增加42%[HR，1.42（95%置信区间，1.12 - 1.80）]相关。此外，年龄差与中风患病率相关[比值比（OR），1.28（95%置信区间，1.11 - 1.49）]。
在英国生物银行中，DNN估计的ECG-age与中风事件和中风患病率相关。需要进一步研究以确定ECG-age是否可作为中风风险的可靠生物标志物。
