Key result
A novel sensorless adaptive physiological control system using a deep CNN accurately estimated preload (RMSE 0.84 mmHg) and prevented ventricular suction and pulmonary congestion in 180 simulated scenarios.
Why the study?
LVAD physiological control systems require pressure feedback to adjust pump speed, but suitable long-term implantable sensors are currently unavailable.
Does a sensorless adaptive physiological control system using a deep CNN for preload estimation accurately estimate preload and prevent ventricular suction and pulmonary congestion in simulated LVAD patient models?
Does a sensorless adaptive physiological control system using a deep CNN for preload estimation accurately estimate preload and prevent ventricular suction and pulmonary congestion in simulated LVAD patient models?
Absolute Event Rate: 0% vs 0%
A novel deep learning-based sensorless control system for LVADs accurately estimates preload and prevents hazardous hemodynamic events in simulated patient models, potentially eliminating the need for implantable pressure sensors.
May enable robust LVAD physiological control with integrated sensors; leaves open long-term reliability and drift mitigation in clinical use.
Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44%, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.
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Fetanat et al. (2021) studied Heart failure (requiring left ventricular assist device) (n=130). Sensorless adaptive physiological control system (deep CNN and FFDL-MFAC) vs. Measured preload-based physiological control system was evaluated on Incidence of ventricular suction and pulmonary congestion. A novel sensorless adaptive physiological control system using a deep CNN accurately estimated preload (RMSE 0.84 mmHg) and prevented ventricular suction and pulmonary congestion in 180 simulated scenarios.
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