Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Ng, Qing Arn; Chiew, Yeong Shiong; Wang, Xin; Tan, Chee Pin; Nor, Mohd Basri Mat; Damanhuri, Nor Salwa; Chase, J. Geoffrey

doi:10.1109/access.2021.3092194

Cited by 22 publications

(9 citation statements)

References 90 publications

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“…Such inter- and intra-patient variability is a major concern in any form of MV [ 25 , 26 ]. To overcome the inconsistencies of respiratory system mechanics, the proposed method can be implemented in a closed-loop system capable of monitoring the health conditions of the Co-MV patients in real-time [ 22 , 27 – 29 ]. An in-series that enables more control and personalised MV, which would also benefit from patient matching, could be used where each patient has a unique inspiratory circuit and control [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

“…The major concern of Co-MV in PC mode is the shared tidal volume (VT) delivered to each patient. The distribution of VT depends solely on the patient-specific respiratory mechanics, which vary over time and with patient care [ 21 , 22 ]. Patients with different respiratory mechanics will receive different VT in proportion to these values, and in particular, in proportion to their relative respiratory elastance.…”

Section: Introductionmentioning

confidence: 99%

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Model-based patient matching for in-parallel pressure-controlled ventilation

et al. 2022

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Background Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. Methods The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. Results The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. Conclusion This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-based patient matching for in-parallel pressure-controlled ventilation

et al. 2022

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“…a tablet or desktop and is then integrated with a data acquisition system (e.g. [52,53]) to monitor and collect pressure-flow data. Initial settings at the beginning of ventilation would first be selected by the clinician.…”

Section: Discussionmentioning

confidence: 99%

Stochastic integrated model-based protocol for volume-controlled ventilation setting

et al. 2022

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Background and objective Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration. Methods A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each. Results From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol. Conclusions Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.

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“…An acquisition strategy's identification and handling of possible threats to the company's long-term existence is one of the most critical components of the process. It is necessary to integrate a large number of systems in order to support 6 Complexity business processes that span numerous firms or organisations. New risks, such as a large and diverse user base, as well as increasing unpredictability, are incorporated into this operational framework by [32], making the development and implementation of an acquisition plan more difficult to accomplish.…”

Section: Literature Reviewmentioning

confidence: 99%

“…rough the use of collected and previously used data, predictive data analytics may foresee and forecast future consequences [4]. In Babylonian times, data was stored on tablets to anticipate harvest, but a dramatic leap in the ability to reason over enormous volumes of data arose in the 1940s with the advent of computer development [5,6], storage, and machine learning techniques [7]. In the 1940s and 1950s, data analytics models were utilised to anticipate the consequences of nuclear chain reactions in the Manhattan Project and weather forecasts using the ENIAC computer [8].…”

Section: Introductionmentioning

confidence: 99%

Design of System‐of‐System Acquisition Analysis Using Machine Learning

Alshammari

2022

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A system of system’s ability to function is derived from the integration of systems from different sources. An SOS’s systems serve two purposes: first, to accomplish their own specific aims, and second, to provide resources to the SOS as a whole. In the last few decades, machine learning and data analytics have been widely used in system design and acquisitions. Every organisation that acquires a sophisticated system employs some type of data analytics to evaluate the system’s independent objectives, which is universally accepted. Data analytics and decision-making regarding the independent system is rarely shared across SOS stakeholders, even though the systems contribute to and benefit from the larger SOS. The goal of this research is to determine how the exchange of data sets and the corresponding analytics by SOS stakeholders can improve SOS capacity. Predicting SOS capabilities by exchanging relevant data sets and prescribing information connections between systems, we propose to use machine learning techniques. This article serves as an intermediate analysis of the above research work and aims to estimate the benefit of information sharing among the SOS stakeholders. In this research, we have applied different machine learning models to the IBM HR analytics data set to determine the corresponding analytics by SOS stakeholders that can improve SOS capacity. We propose using machine learning techniques to forecast SOS capabilities through the sharing of relevant data sets, and we prescribe the information linkages across systems to make this possible. This paper provides an update on the progress being made toward the aforementioned research project, and its primary focus is on developing a method to put a dollar amount on the benefits of information sharing among the many parties involved in the SOS.

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Network Data Acquisition and Monitoring System for Intensive Care Mechanical Ventilation Treatment

Cited by 22 publications

References 90 publications

Model-based patient matching for in-parallel pressure-controlled ventilation

Model-based patient matching for in-parallel pressure-controlled ventilation

Stochastic integrated model-based protocol for volume-controlled ventilation setting

Design of System‐of‐System Acquisition Analysis Using Machine Learning

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