Physiotherapy - Research Publications

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Type: Conference Paper × Start date: 2021 × Author: Berlowitz, David ×

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    Identification of Patient Ventilator Asynchrony in Physiological Data Through Integrating Machine-Learning
    Stell, A ; Caparo, E ; Wang, Z ; Wang, C ; Berlowitz, D ; Howard, M ; Sinnott, R ; Aickelin, U (SCITEPRESS - Science and Technology Publications, 2024)
    Patient Ventilator Asynchrony (PVA) occurs where a mechanical ventilator aiding a patient's breathing falls out of synchronisation with their breathing pattern. This de-synchronisation may cause patient distress and can lead to long-term negative clinical outcomes. Research into the causes and possible mitigations of PVA is currently conducted by clinical domain experts using manual methods, such as parsing entire sleep hypnograms visually, and identifying and tagging instances of PVA that they find. This process is very labour-intensive and can be error prone. This project aims to make this analysis more efficient, by using machine-learning approaches to automatically parse, classify, and suggest instances of PVA for ultimate confirmation by domain experts. The solution has been developed based on a retrospective dataset of intervention and control patients that were recruited to a non-invasive ventilation study. This achieves a specificity metric of over 90%. This paper describes the process of integrating the output of the machine learning into the bedside clinical monitoring system for production use in anticipation of a future clinical trial.
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    P132 Research in the time of COVID-19: Recruitment to a clinical trial comparing models of NIV implementation in people with MND
    Sheers, N ; Howard, M ; Hannan, L ; Retica, S ; Berlowitz, D (Oxford University Press (OUP), 2021-10-07)
    Abstract Introduction A pilot randomised controlled trial (RCT) examining the feasibility of a new model of non-invasive ventilation (NIV) implementation was due to commence in early 2020. Based on previous research, it was anticipated that 100% of people with motor neurone disease (MND) would be eligible, 60% would consent to participate and 20 people would be randomised in five months. The aim of this report is to describe the impact of COVID-19 pandemic contingencies on trial recruitment. Methods Report of project progress, participant screening and recruitment. Results First reports of COVID-19 coincided with study commencement and changed usual healthcare delivery. Lockdowns meant telehealth substituted for face-to-face assessment, respiratory function testing was limited and/or patients were reluctant to seek medical treatment. This modified pathway impacted evaluation of diagnosis, timing of need for NIV and procedural safety, with patients then referred specifically for a single-day hospital NIV implementation to enable face-to-face multidisciplinary assessment to aid decisions. Of 81 potential participants screened in an 8-month period, 64% were ineligible for the RCT. Despite this shift in eligibility rate, 16 people with MND have been recruited as of May 2021. Conclusion The current climate has amplified the significance of this research trial; people with MND have had reduced access to face-to-face services globally and clinicians have had to quickly adapt to a changing landscape of telemedicine and remote monitoring of patients. This trial’s screening data suggest that COVID-19 hasn’t stopped people with MND being implemented on NIV, but it has altered assessment pathways.