Schlesinger Daphne E, Alam Ridwan, Ringel Roey, Pomerantsev Eugene, Devireddy Srikanth, Shah Pinak, Garasic Joseph, Stultz Collin M
Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA.
Research Laboratory of Electronics, MIT, Cambridge, MA, USA.
Commun Med (Lond). 2025 Jan 7;5(1):4. doi: 10.1038/s43856-024-00730-5.
The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.
We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated. The model was developed and internally evaluated using a cohort of 6739 samples from the Massachusetts General Hospital (MGH) and externally validated on a cohort of 4620 samples from a second institution. We then evaluated model on patch-monitor electrocardiographic data on a small prospective cohort.
The model achieves an area under the receiver operating characteristic curve of 0.80 for detecting elevated left atrial pressures on an internal holdout dataset from MGH and 0.76 on an external validation set from a second institution. A further prospective dataset was obtained using single-lead electrocardiogram data with a patch-monitor from patients who underwent right heart catheterization at MGH. Evaluation of the model on this dataset yielded an area under the receiver operating characteristic curve of 0.875 for identifying elevated left atrial pressures for electrocardiogram signals acquired close to the time of the right heart catheterization procedure.
These results demonstrate the utility and the potential of ambulatory cardiac hemodynamic monitoring with electrocardiogram patch-monitors.
非侵入性测量左心房压力的能力将有助于识别有肺充血风险的患者，并指导积极的心力衰竭治疗。可穿戴心脏监测器可记录单导联心电图数据，提供可用于推断左心房压力的信息。
我们开发了一种利用单导联心电图数据的深度神经网络来确定左心房压力何时升高。该模型使用来自麻省总医院（MGH）的6739个样本队列进行开发和内部评估，并在来自另一家机构的4620个样本队列上进行外部验证。然后，我们在一个小型前瞻性队列的贴片监测心电图数据上评估该模型。
该模型在MGH的内部保留数据集上检测左心房压力升高时受试者操作特征曲线下面积为0.80，在另一家机构的外部验证集上为0.76。使用来自MGH接受右心导管检查患者的单导联心电图数据和贴片监测器获得了一个进一步的前瞻性数据集。在该数据集上对模型进行评估，对于在右心导管检查程序时间附近采集的心电图信号，识别左心房压力升高时受试者操作特征曲线下面积为0.875。
这些结果证明了使用心电图贴片监测器进行动态心脏血流动力学监测的实用性和潜力。
