Tamborini Alessio, Aghilinejad Arian, Matthews Ray V, Gharib Morteza
Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA.
Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA.
JACC Adv. 2025 Aug 22;4(9):102104. doi: 10.1016/j.jacadv.2025.102104.
Left ventricular (LV) pressure measurement is the clinical gold standard for assessing cardiac function; however, its reliance on invasive catheterization limits accessibility and widespread use.
This study aimed to develop a cuff-based machine learning (cuff-ML) approach for reconstructing LV pressure from noninvasive brachial waveforms as a bedside assessment of cardiac function.
Subjects referred for nonemergent left heart catheterization were recruited for LV pressure and brachial cuff waveform measurement. The cuff-ML method was trained using brachial waveforms to predict LV pressure and was evaluated for morphology and parameters accuracy against invasive catheter measurements. Cardiac function was assessed based on the reduced LV peak pressure derivative ([+]dP/dt <1,200 mm Hg/s).
A total of 104 subjects, comprising 3,572 simultaneous LV and cuff-based brachial waveform pairs, were analyzed using a 70:30 train-test split (test cohort: 32 subjects, 1,023 cardiac cycles). The cuff-ML approach demonstrated high accuracy in reconstructing LV waveform shape compared to catheter measurements (median normalized root mean squared error = 8.2%). Pressure-based parameters, including maximum pressure (r = 0.92, P < 0.001), mean blood pressure (r = 0.94, P < 0.001), and developed pressure (r = 0.85, P < 0.001), showed strong correlations with invasive measurements. Cuff-ML-reconstructed waveforms identified abnormal systolic contractility (72% sensitivity, 73% specificity) on a beat-to-beat basis.
Cuff-ML accurately reconstructs LV pressure from brachial cuff measurements. This noninvasive approach may be helpful for assessment of cardiac function and requires further study.
左心室（LV）压力测量是评估心脏功能的临床金标准；然而，其对侵入性导管插入术的依赖限制了其可及性和广泛应用。
本研究旨在开发一种基于袖带的机器学习（袖带 - ML）方法，用于从无创肱动脉波形重建左心室压力，作为心脏功能的床旁评估方法。
招募因非紧急左心导管插入术而就诊的受试者，进行左心室压力和肱动脉袖带波形测量。袖带 - ML方法使用肱动脉波形进行训练以预测左心室压力，并针对侵入性导管测量评估其形态和参数准确性。基于降低的左心室峰值压力导数（[+]dP/dt <1,200 mmHg/s）评估心脏功能。
共分析了104名受试者，包括3,572对同时记录的左心室和基于袖带的肱动脉波形，采用70:30的训练 - 测试划分（测试队列：32名受试者，1,023个心动周期）。与导管测量相比，袖带 - ML方法在重建左心室波形形状方面显示出高精度（中位数归一化均方根误差 = 8.2%）。基于压力的参数，包括最大压力（r = 0.92，P < 0.001）、平均血压（r = 0.94，P < 0.001）和压力上升速率（r = 0.85，P < 0.001），与侵入性测量显示出强相关性。袖带 - ML重建的波形逐搏识别异常收缩性（敏感性72%，特异性73%）。
袖带 - ML可从肱动脉袖带测量中准确重建左心室压力。这种非侵入性方法可能有助于心脏功能评估，需要进一步研究。
