Abstract
The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patient-specific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective model-based monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patient-ventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, real-time collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.
Highlights
Mechanical ventilation (MV) supports the breathing of respiratory failure patients to maintain adequate blood oxygenation and reduces the work of breathing to allow them to recover from the underlying disease or insult [1], [2]
It comprises of three network-connected subsystems: Subsystem 1: Data acquisition (DAQ); Subsystem 2: Data storage; and Subsystem 3: Server and data management platform
This web application is known as the data management platform (DMP), and it manages and processes both real-time data from the DAQ and retrospective data from storage
Summary
Mechanical ventilation (MV) supports the breathing of respiratory failure patients to maintain adequate blood oxygenation and reduces the work of breathing to allow them to recover from the underlying disease or insult [1], [2]. While many models can successfully capture lung dynamics [17], very few can accurately predict the pulmonary response over time [10], [18]–[20] These model-based works are performed retrospectively, and there is a need of a platform to enable real-time MV data monitoring, processing and to provide decision support information to the clinicians. This research presents a network system (CARENet) to remotely monitor MV patient condition and provide real-time respiratory mechanics analysis to personalize and guide care. It outlines the network system architecture, implementation, and functionality, and tests its application to provide real-time analysis of MV patient data.
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