Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Building 36-796, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St., Cambridge, MA, 02139, USA.
Sci Rep. 2023 Mar 9;13(1):3923. doi: 10.1038/s41598-023-30900-9.
Quantifying hemodynamic severity in patients with heart failure (HF) is an integral part of clinical care. A key indicator of hemodynamic severity is the mean Pulmonary Capillary Wedge Pressure (mPCWP), which is ideally measured invasively. Accurate non-invasive estimates of the mPCWP in patients with heart failure would help identify individuals at the greatest risk of a HF exacerbation. We developed a deep learning model, HFNet, that uses the 12-lead electrocardiogram (ECG) together with age and sex to identify when the mPCWP > 18 mmHg in patients who have a prior diagnosis of HF. The model was developed using retrospective data from the Massachusetts General Hospital and evaluated on both an internal test set and an independent external validation set, from another institution. We developed an uncertainty score that identifies when model performance is likely to be poor, thereby helping clinicians gauge when to trust a given model prediction. HFNet AUROC for the task of estimating mPCWP > 18 mmHg was 0.8 [Formula: see text] 0.01 and 0.[Formula: see text] 0.01 on the internal and external datasets, respectively. The AUROC on predictions with the highest uncertainty are 0.50 [Formula: see text] 0.02 (internal) and 0.[Formula: see text] 0.04 (external), while the AUROC on predictions with the lowest uncertainty were 0.86 ± 0.01 (internal) and 0.82 ± 0.01 (external). Using estimates of the prevalence of mPCWP > 18 mmHg in patients with reduced ventricular function, and a decision threshold corresponding to an 80% sensitivity, the calculated positive predictive value (PPV) is 0.[Formula: see text] 0.01when the corresponding chest x-ray (CXR) is consistent with interstitial edema HF. When the CXR is not consistent with interstitial edema, the estimated PPV is 0.[Formula: see text] 0.02, again at an 80% sensitivity threshold. HFNet can accurately predict elevated mPCWP in patients with HF using the 12-lead ECG and age/sex. The method also identifies cohorts in which the model is more/less likely to produce accurate outputs.
量化心力衰竭（HF）患者的血液动力学严重程度是临床护理的一个重要组成部分。血液动力学严重程度的一个关键指标是平均肺毛细血管楔压（mPCWP），理想情况下是通过侵入性测量得到的。在心力衰竭患者中，准确的非侵入性 mPCWP 估计值有助于确定那些 HF 恶化风险最大的个体。我们开发了一种深度学习模型 HFNet，该模型使用 12 导联心电图（ECG）结合年龄和性别来识别先前诊断为 HF 的患者 mPCWP>18mmHg 的情况。该模型是使用马萨诸塞州总医院的回顾性数据开发的，并在内部测试集和来自另一家机构的独立外部验证集上进行了评估。我们开发了一个不确定性评分，用于识别模型性能可能较差的情况，从而帮助临床医生评估何时信任给定的模型预测。HFNet 对估计 mPCWP>18mmHg 的任务的 AUROC 分别为 0.8[公式：见文本]0.01 和 0.7[公式：见文本]0.01，分别为内部和外部数据集。具有最高不确定性的预测的 AUROC 为 0.50[公式：见文本]0.02（内部）和 0.7[公式：见文本]0.04（外部），而具有最低不确定性的预测的 AUROC 为 0.86±0.01（内部）和 0.82±0.01（外部）。使用心室功能降低的患者中 mPCWP>18mmHg 的患病率估计值，以及与 80%灵敏度相对应的决策阈值，当相应的胸部 X 射线（CXR）与间质性水肿 HF 一致时，计算的阳性预测值（PPV）为 0.7[公式：见文本]0.01。当 CXR 与间质性水肿不一致时，估计的 PPV 为 0.7[公式：见文本]0.02，同样在 80%灵敏度阈值下。HFNet 可以使用 12 导联 ECG 和年龄/性别准确预测心力衰竭患者的 mPCWP 升高。该方法还可以识别模型更/不太可能产生准确输出的队列。
