Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, United States of America.
Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America.
Physiol Meas. 2024 Aug 14;45(8). doi: 10.1088/1361-6579/ad69fd.
Instantaneous, non-invasive evaluation of left ventricular end-diastolic pressure (LVEDP) would have significant value in the diagnosis and treatment of heart failure. A new approach called cardiac triangle mapping (CTM) has been recently proposed, which can provide a non-invasive estimate of LVEDP. We hypothesized that a hybrid machine-learning (ML) method based on CTM can instantaneously identify an elevated LVEDP using simultaneously measured femoral pressure waveform and electrocardiogram (ECG).We studied 46 patients (Age: 39-90 (66.4 ± 9.9), BMI: 20.2-36.8 (27.6 ± 4.1), 12 females) scheduled for clinical left heart catheterizations or coronary angiograms at University of Southern California Keck Medical Center. Exclusion criteria included severe mitral/aortic valve disease; severe carotid stenosis; aortic abnormalities; ventricular paced rhythm; left bundle branch and anterior fascicular blocks; interventricular conduction delay; and atrial fibrillation. Invasive LVEDP and pressure waveforms at the iliac bifurcation were measured using transducer-tipped Millar catheters with simultaneous ECG. LVEDP range was 9.3-40.5 mmHg. LVEDP = 18 mmHg was used as cutoff. Random forest (RF) classifiers were trained using data from 36 patients and blindly tested on 10 patients.Our proposed ML classifier models accurately predict true LVEDP classes using appropriate physics-based features, where the most accurate demonstrates 100.0% (elevated) and 80.0% (normal) success in predicting true LVEDP classes on blind data.We demonstrated that physics-based ML models can instantaneously classify LVEDP using information from femoral waveforms and ECGs. Although an invasive validation, the required ML inputs can be potentially obtained non-invasively.
即时、无创地评估左心室舒张末期压（LVEDP）在心力衰竭的诊断和治疗中具有重要价值。最近提出了一种新的方法，称为心脏三角图（CTM），它可以提供 LVEDP 的无创估计。我们假设一种基于 CTM 的混合机器学习（ML）方法可以使用同时测量的股动脉压力波形和心电图（ECG）即时识别升高的 LVEDP。
我们研究了 46 名在南加州大学凯克医疗中心接受临床左心导管检查或冠状动脉造影的患者（年龄：39-90 岁（66.4 ± 9.9），BMI：20.2-36.8（27.6 ± 4.1），女性 12 名）。排除标准包括严重二尖瓣/主动脉瓣疾病；严重颈动脉狭窄；主动脉异常；心室起搏节律；左束支和前纤维束阻滞；室间传导延迟；和心房颤动。使用带有心电图同步的 Millar 导管尖端传感器测量髂分叉处的 LVEDP 和压力波形。LVEDP 范围为 9.3-40.5mmHg。将 LVEDP = 18mmHg 作为截止值。使用来自 36 名患者的数据训练随机森林（RF）分类器，并在 10 名患者上进行盲测。
我们提出的 ML 分类器模型使用适当的基于物理的特征准确预测真实的 LVEDP 类别，其中最准确的模型在盲数据上预测真实的 LVEDP 类别时成功率分别为 100.0%（升高）和 80.0%（正常）。我们证明了基于物理的 ML 模型可以使用股动脉波形和 ECG 信息即时分类 LVEDP。虽然这是一种有创验证，但所需的 ML 输入可以潜在地通过无创方式获得。
