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Introduction: Intrinsic Frequency (IF) method is a recently developed systems-based method that extracts dynamics information about left ventricle function (LV), arterial dynamics, and the LV-arterial coupling from arterial waveforms. We have recently shown (Alavi et al. Circulation, 140 (2019), A12573-A12573) that IF can detect occurrence of an acute myocardial infarction (MI) using a single carotid pressure waveform. Here, we propose that the myocardial infarct size (area of necrosis over total LV area) can be approximated using a hybrid IF-artificial neural network (ANN) method. Methods: The standard MI model was used in anesthetized Sprague Dawley rats (n=27). The proximal left coronary artery was occluded for 30 minutes to ensure necrosis followed by 3 hours of reperfusion. The left ventricle slices were incubated in triphenyl tetrazolium chloride (TTC) to distinguish the necrotic (white) and the non-necrotic (dark red) areas (Fig.1a), thereby obtaining the size of MI through histopathology. IF parameters were computed from random carotid pressure waveforms 2 hours after the reperfusion. A 3-layer ANN model (4 input, 5 hidden, and 1 output node) was applied on IFs from 22 rats to design the ANN (18 for training, 4 for validation). The model was then tested on 5 different rats with the same MI procedure described above. Results: The results showed a significant correlation (R=0.64, P<0.0005) between our IF-artificial intelligence (IF-AI) model and the infarct size. The correlation was especially strong (R=0.84, P<0.0001) without the two outliers shown in Fig.1b. Conclusions: Our results suggest that a hybrid IF-AI method can predict the anatomic infarct size from an arterial waveform without advanced imaging. This technique is clinically significant since infarct sizes are link to the survival and development of heart failure in MI patients, and IF parameters can be obtained noninvasively from carotid waveforms using arterial tonometry devices or an iPhone.

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In-vitro models of the systemic circulation have gained a lot of interest for fundamental understanding of cardiovascular dynamics and for applied hemodynamic research. In this study, we introduce a physiologically accurate in-vitro hydraulic setup that models the hemodynamics of the coupled atrioventricular-aortic system. This unique experimental simulator has three major components: 1) an arterial system consisting of a human-scale artificial aorta along with the main branches, 2) an artificial left ventricle (LV) sac connected to a programmable piston-in-cylinder pump for simulating cardiac contraction and relaxation, and 3) an artificial left atrium (LA). The setup is designed in such a way that the basal LV is directly connected to the aortic root via an aortic valve, and to the LA via an artificial mitral valve. As a result, two-way hemodynamic couplings can be achieved for studying the effects that the LV, aorta, and LA have on each other. The collected pressure and flow measurements from this setup demonstrate a remarkable correspondence to clinical hemodynamics. We also investigate the physiological relevancies of isolated effects on cardiovascular hemodynamics of various major global parameters found in the circulatory system, including LV contractility, LV preload, heart rate, aortic compliance, and peripheral resistance. Subsequent control over such parameters ultimately captures physiological hemodynamic effects of LV systolic dysfunction, preload (cardiac) diseases, and afterload (arterial) diseases. The detailed design and fabrication of the proposed setup is also provided.

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.