Abstract:Background: Systemic hemodynamics and specific ventilator settings have been shown to predict survival during venoarterial extracorporeal membrane oxygenation (VA ECMO). While these factors are intertwined with right ventricular (RV) function, the independent relationship between RV function and survival during VA ECMO is unknown. Objectives: To identify the relationship between RV function with mortality and duration of ECMO support. Methods: Cardiac ECMO runs in adults from the Extracorporeal Life Support Or… Show more
“…According to data from the Extracorporeal Life Support Organization (ELSO), Patients undergoing VV-ECMO have a survival rate of 58%, whereas those undergoing VA-ECMO have a comparatively lower survival rate of 43% [4]. The existing ECMOs, which maintain a certain high blood pressure (BP) in the aorta, can lead to fatal side effects, such as left ventricular dilatation, increased left atrium pressure (LAP), and pulmonary edema when the patient's left ventricle (LV) contractility is weak and cannot generate a BP above the arterial pressure, thereby causing the blood in the LV to stagnate; consequently, this necessitates improvements to resolve blood stagnation in the LV [5][6][7][8].…”
Implementing counter-pulsation (CP) control in pulsatile extracorporeal membrane oxygenator (p-ECMO) systems offers a refined approach to mitigate risks commonly associated with conventional ECMOs. To attain CP between the p-ECMO and heart, accurate detection of heartbeats within blood pressure (BP) waveform data becomes imperative, especially in situations where measuring electrocardiograms (ECGs) are difficult or impractical. In this study, a cumulative algorithm incorporating filter-type neural networks was developed to distinguish heartbeats from other pulse signals generated by the p-ECMO, reflections, or motion artifacts in the BP data. A control system was implemented using the cumulative algorithm that detects the heart rate (HR) and maintains a proper interval between the p-ECMO's pulses and heart beats, thereby achieving CP. To ensure precise circulatory support control, the p-ECMO setup was connected to a mock circulation system, with the human BP waveforms being replicated using a heart model. The algorithm could maintain CP perfectly when the HR remained constant; however, owing to a 0.48-s delay from the HR detection to CP control, the success rate of the CP control decreases when a sudden increase in the HR occurred. In fact, when the HR varied by ± 5 bpm every minute, the CP success rate dropped to 78.62%, however this was still higher compared to the 25.75% success rate achieved when no control was applied.
“…According to data from the Extracorporeal Life Support Organization (ELSO), Patients undergoing VV-ECMO have a survival rate of 58%, whereas those undergoing VA-ECMO have a comparatively lower survival rate of 43% [4]. The existing ECMOs, which maintain a certain high blood pressure (BP) in the aorta, can lead to fatal side effects, such as left ventricular dilatation, increased left atrium pressure (LAP), and pulmonary edema when the patient's left ventricle (LV) contractility is weak and cannot generate a BP above the arterial pressure, thereby causing the blood in the LV to stagnate; consequently, this necessitates improvements to resolve blood stagnation in the LV [5][6][7][8].…”
Implementing counter-pulsation (CP) control in pulsatile extracorporeal membrane oxygenator (p-ECMO) systems offers a refined approach to mitigate risks commonly associated with conventional ECMOs. To attain CP between the p-ECMO and heart, accurate detection of heartbeats within blood pressure (BP) waveform data becomes imperative, especially in situations where measuring electrocardiograms (ECGs) are difficult or impractical. In this study, a cumulative algorithm incorporating filter-type neural networks was developed to distinguish heartbeats from other pulse signals generated by the p-ECMO, reflections, or motion artifacts in the BP data. A control system was implemented using the cumulative algorithm that detects the heart rate (HR) and maintains a proper interval between the p-ECMO's pulses and heart beats, thereby achieving CP. To ensure precise circulatory support control, the p-ECMO setup was connected to a mock circulation system, with the human BP waveforms being replicated using a heart model. The algorithm could maintain CP perfectly when the HR remained constant; however, owing to a 0.48-s delay from the HR detection to CP control, the success rate of the CP control decreases when a sudden increase in the HR occurred. In fact, when the HR varied by ± 5 bpm every minute, the CP success rate dropped to 78.62%, however this was still higher compared to the 25.75% success rate achieved when no control was applied.
“…According to data from the Extracorporeal Life Support Organization (ELSO), Patients undergoing VV-ECMO have been observed to have a survival rate of 58&, whereas those undergoing VA-ECMO have a comparatively lower survival rate of 43% [4]. The existing ECMO, which maintains a certain high blood pressure(BP) in the aorta, can lead to fatal side effects such as left ventricular dilatation, increased left atrium pressure (LAP), and pulmonary edema when the patient's left ventricle (LV) contractility is weak and cannot generate BP above arterial pressure, causing the blood in the LV to stagnate, thus necessitating improvements to resolve this issue of blood stagnation in the LV [5][6][7][8].…”
The counter-pulsation (CP) control of Pulsatile Extracorporeal Membrane Oxygenator(p-ECMO) contributes to reducing the risks associated with conventional ECMO, such as Left Ventricular dilatation and pulmonary edema. To achieve CP between p-ECMO and the heart, it is crucial to detect heartbeats and p-ECMO pulses in blood pressure (BP) waveform data, especially in cases where ECG measurement is challenging. This study aims to develop an algorithm utilizing deep neural network (DNN) to differentiate heartbeats from other pulses caused by p-ECMO, reflections, or motion artifacts in BP data, ensuring accurate CP control. A mock circulation system, replicating human BP waveforms with a heart model was connected to p-ECMO. Two trained DNNs were employed to measure the heart model's heart rate (HR) and evaluate whether p-ECMO operated in CP mode. In asynchronous mode experiments, the frequency of unintentionally occurring CP was only 25.75%. However, when utilizing the proposed algorithm, stable CP was observed, even when the initial pulse rate of p-ECMO differed from that of the heart model. Notably, even when the heart model changed its HR by 5 bpm every minute for 8 minutes within the range of 55 to 75 bpm, the CP success rate remained above 78%.
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