Schlesinger Daphne E, Diamant Nathaniel, Raghu Aniruddh, Reinertsen Erik, Young Katherine, Batra Puneet, Pomerantsev Eugene, Stultz Collin M
Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts, USA.
Institute for Medical Engineering and Science, MIT, Cambridge, Massachusetts, USA.
JACC Adv. 2022 Mar 18;1(1):100003. doi: 10.1016/j.jacadv.2022.100003. eCollection 2022 Mar.
Central hemodynamic parameters are typically measured via pulmonary artery catherization-an invasive procedure that involves some risk to the patient and is not routinely available in all settings.
This study sought to develop a noninvasive method to identify elevated mean pulmonary capillary wedge pressure (mPCWP).
We leveraged data from 248,955 clinical records at the Massachusetts General Hospital to develop a deep learning model that can infer when the mPCWP >15 mmHg using the 12-lead electrocardiogram (ECG). Of these data, 242,216 records were used to pre-train a model that generates useful ECG representations. The remaining 6,739 records contain encounters with direct measurements of the mPCWP. Eighty percent of these data were used for model development and testing (5,390), and the remaining records comprise a holdout set (1,349) that was used to evaluate the model. We developed an associated unreliability score that identifies when model predictions are likely to be untrustworthy.
The model achieves an area under the receiver operating characteristic curve (AUC) of 0.80 ± 0.02 (test set) and 0.79 ± 0.01 (holdout set). Model performance varies as a function of the unreliability, where patients with high unreliability scores correspond to a subgroup where model performance is poor: for example, patients in the holdout set with unreliability scores in the highest decile have a reduced AUC of 0.70 ± 0.06.
The mPCWP can be inferred from the ECG, and the reliability of this inference can be measured. When invasive monitoring cannot be expeditiously performed, deep learning models may provide information that can inform clinical care.
中心血流动力学参数通常通过肺动脉导管插入术进行测量——这是一种侵入性操作，对患者存在一定风险，且并非在所有情况下都能常规进行。
本研究旨在开发一种非侵入性方法来识别平均肺毛细血管楔压（mPCWP）升高的情况。
我们利用麻省总医院248955份临床记录的数据，开发了一种深度学习模型，该模型可以使用12导联心电图（ECG）推断mPCWP>15 mmHg的情况。在这些数据中，242216份记录用于预训练一个能生成有用ECG表征的模型。其余6739份记录包含mPCWP直接测量值的病例。其中80%的数据用于模型开发和测试（5390份），其余记录构成一个保留集（1349份），用于评估模型。我们开发了一个相关的不可靠性评分，用于识别模型预测何时可能不可信。
该模型在受试者工作特征曲线（AUC）下的面积为0.80±0.02（测试集）和0.79±0.01（保留集）。模型性能随不可靠性而变化，不可靠性评分高的患者对应模型性能较差的亚组：例如，保留集中不可靠性评分处于最高十分位数的患者，其AUC降低至0.70±0.06。
mPCWP可从ECG推断得出，且这种推断的可靠性可以测量。当无法迅速进行侵入性监测时，深度学习模型可能提供有助于临床护理的信息。
