Pub Date : 2021-11-01DOI: 10.1136/thorax-2021-btsabstracts.134
S. Lua, D. Lowe, A. Taylor, M. Sim, B. Henderson, C. Trueman, O. Meredith, S. Burns, P. McGuinness, C. Carlin
P24 Figure 1CARP trial wearable respiratory rate, respiratory support and outcome data from 3 patients with severe COVID-19[Figure omitted. See PDF]Results156 patients were screened, with 77 recruited to the CARP trial. 32 patients required non-invasive respiratory support, of which 14 were escalated to mechanical intubation. 17 patients died within trial.Bland-Altman analyses of paired RR data confirmed that wearable sensor data shows good agreement with critical care RR monitoring (Phillips Intellivue MX700), and that ward-based intermittent clinician RR measurements were imprecise.From the initial utility review of CARP physiology data visualisations, rising hourly average RR >25/min is associated with subsequent patient deterioration. Improving and stable hourly average RR of <25/min associates with stable respiratory failure and improvement to hospital discharge (figure 1).ConclusionContinuous wearable respiratory rate remote monitoring in COVID-19 inpatients is feasible. Planned machine learning and time-series analyses of the detailed physiology and clinical endpoint data will determine appropriate cut-offs and feature importance for deteriorating patient risk predictions. The CARP clinical dashboard provides an infrastructure for future implementation and evaluation of these AI insights.
{"title":"P24 COVID-19 advanced respiratory physiology (CARP) wearable respiratory monitoring: early insights","authors":"S. Lua, D. Lowe, A. Taylor, M. Sim, B. Henderson, C. Trueman, O. Meredith, S. Burns, P. McGuinness, C. Carlin","doi":"10.1136/thorax-2021-btsabstracts.134","DOIUrl":"https://doi.org/10.1136/thorax-2021-btsabstracts.134","url":null,"abstract":"P24 Figure 1CARP trial wearable respiratory rate, respiratory support and outcome data from 3 patients with severe COVID-19[Figure omitted. See PDF]Results156 patients were screened, with 77 recruited to the CARP trial. 32 patients required non-invasive respiratory support, of which 14 were escalated to mechanical intubation. 17 patients died within trial.Bland-Altman analyses of paired RR data confirmed that wearable sensor data shows good agreement with critical care RR monitoring (Phillips Intellivue MX700), and that ward-based intermittent clinician RR measurements were imprecise.From the initial utility review of CARP physiology data visualisations, rising hourly average RR >25/min is associated with subsequent patient deterioration. Improving and stable hourly average RR of <25/min associates with stable respiratory failure and improvement to hospital discharge (figure 1).ConclusionContinuous wearable respiratory rate remote monitoring in COVID-19 inpatients is feasible. Planned machine learning and time-series analyses of the detailed physiology and clinical endpoint data will determine appropriate cut-offs and feature importance for deteriorating patient risk predictions. The CARP clinical dashboard provides an infrastructure for future implementation and evaluation of these AI insights.","PeriodicalId":319670,"journal":{"name":"Virtual monitoring in COVID-19","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124151058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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