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Home monitoring of vital signs and generation of alerts in a cohort of people living with dementia
Author(s) -
David Michael,
Barnaghi Payam,
Nilforooshan Ramin,
Rostill Helen,
Soreq Eyal,
Sharp David J,
Scott Gregory
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.055151
Subject(s) - vital signs , dementia , medicine , pandemic , blood pressure , cohort , medical emergency , medical diagnosis , emergency medicine , adverse effect , disease , pediatrics , covid-19 , surgery , infectious disease (medical specialty) , pathology
Background People with dementia (PwD) are at increased risk of adverse medical events (e.g. infections and falls). These often cause clinical deterioration, and potentially preventable admissions. Remote home monitoring of vital signs using internet‐of‐things technology can identify risk factors for these events – something particular pertinent during the COVID‐19 pandemic. We present data from an on‐going UK Dementia Research Institute and Technology Integrated Health Management (DRI‐TIHM) project. We aim to define algorithms that generate automated alerts, like the hospital‐based NEWS system (Morgan, 1997, Clin Intensive Car ), and provide more proactive care for PwD. Method PwD recorded their systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), temperature (BTM), bodyweight (BW) and oxygen saturation (Sats) daily (figure 1) and data were collected centrally. A ‘monitoring team’ followed algorithms in response to alerts and advised medical attention if required. Events such as infections, were logged and correlated with the data. The dataset was then used to calculate the number of alerts that would have been raised if different thresholds were used. Result 52 PwD living at home were included. Patients had a range of dementia diagnoses, most commonly Alzheimer’s disease. In total, 89,894 measurements were collected over 556 days (figure 2). During the pandemic, a sub‐group were given Sats probes and these produced a significant number of false positive results (figure 4f). On sub‐group analysis, PwD with Parkinson’s disease dementia had significantly lower SBP and DBP (p=0.004 and p=0.01 respectively, Mann‐Whitney U) (figure 3a), one of whom suffered from repeated falls (figure 3b). Pilot data were used to set alert thresholds. Average values and number of alerts for each domain are shown (table 1). We then modelled the effect of different thresholds, based on NEWS, to the number of alerts generated (figure 4, table 2). This is informative in optimising sensitivity and specificity of alerts whilst considering the burden on the monitoring service. Conclusion Remote monitoring can be implemented for PwD. These data can be used to generate clinical alerts that may signify adverse medical outcomes. The thresholds need to be adjusted to optimise efficiency in the context of varying reliability of home monitoring devices.