Abstract 16334: An Artificial Intelligence Derived Method for Instantaneous Detection of Elevated Left Ventricular End Diastolic Pressure

Introduction: Instantaneous, non-invasive detection of an elevated left ventricular end-diastolic pressure (LVEDP) offers a significant benefit in diagnosis and treatment of heart failure. We recently proposed a systems approach, called cardiac triangle mapping (CTM), that uses intrinsic frequencies (IFs) of the arterial waveform and pre-ejection period (PEP) to map the global ventricular function (Pahlevan et al. Fluids 4.1 (2019): 16). Here, we tested the hypothesis that an elevated LVEDP can be detected using ECG and arterial pressure waveform by applying an artificial neural network (ANN) combined with CTM approach. Methods: This study included 46 patients (12 females, age 39-90 (66.4±9.9), BMI 20.2-36.8 (27.6±4.1)) who were scheduled for a clinical left heart catheterization or coronary angiogram at the Keck Medical Center of USC. Exclusion criteria were valvular heart disease, atrial fibrillation, or left bundle branch block. Invasive LVEDP and aortic pressure waveforms were measured using a 3F Millar transducer tipped catheter with simultaneous 3 channel ECG. The IFs were computed from pressure waveforms. PEPs were calculated as the time difference between the beginning of QRS and the uprising of the pressure waveform. A 3-layer network consisted of 6 input, 6 hidden and one output nodes was developed. LVEDP=18 mmHg was used as the cut-off for a binary outcome. Data from 34 patients were used to design the ANN (27 for training, 7 for validation). The model was tested on 12 additional patients. Results: Our results showed a specificity of 87% and a sensitivity of 96% in detecting an elevated LVEDP (Fig.1). Conclusions: Here, we demonstrated the proof-of-concept that an AI model based on reduced-order parameters (extracted from arterial waveform and ECG) can instantaneously detect an elevated LVEDP. Although our hemodynamic measurements were done invasively, all variables that are required for this AI-LVEDP calculation can be collected noninvasively.