171 lines
10 KiB
TeX
171 lines
10 KiB
TeX
\documentclass[10pt, a5paper]{article}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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%\usepackage[english, ngerman]{babel}
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\usepackage{csquotes}
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\usepackage[english]{babel}
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\usepackage{graphicx}
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\usepackage{parskip}
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\usepackage[left=1cm, right=1cm, top=1.5cm, bottom=1.5cm]{geometry}
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%\usepackage[table]{xcolor}
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\usepackage{color}
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\usepackage[colorlinks]{hyperref}
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\pagestyle{plain}
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% Citations
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%\usepackage{cite}
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\usepackage[backend=biber, style=vancouver]{biblatex}
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\addbibresource{../bibliography/bibliography.bib}
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% Colors
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\definecolor{PLRI_Rot}{RGB}{190,30,60}
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\definecolor{grau}{RGB}{120,110,100}
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\begin{document}
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{\fontfamily{phv}\selectfont}
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\input{cover.tex}
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\section{Background}
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Clinical deterioration is a critical concern in healthcare, particularly for vulnerable populations such as the elderly and chronically
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ill patients. It refers to a decline in a patient's health status and may lead to adverse outcomes, including hospitalization,
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longer stays in intensive care units, and increased healthcare costs.
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Early warning scores (EWS) have been widely adopted internationally for early detection of deteriorating patients\cite{downey_strengths_2017}.
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A large body of scientific evidence validates the effectiveness of EWS in assessing severity of illness, and in predicting adverse clinical events,
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such as severe deterioration, likelihood of ICU admission, and mortality, both on hospital wards\cite{subbe_validation_2001, buist_association_2004, paterson_prediction_2006, alam_exploring_2015, bilben_national_2016, brekke_value_2019}
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and in ambulatory care \cite{ehara_effectiveness_2019, burgos-esteban_effectiveness_2022, paganelli_conceptual_2022}.
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Two common implementations are the \textit{Modified Early Warning Score} (MEWS) and the
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\textit{National Early Warning Score 2} (NEWS2)\cite{burgos-esteban_effectiveness_2022}.
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Both are calculated by capturing various vital parameters from the patient at a specific point in time, followed by numerical aggregation of the
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captured data according to the specifically used score\cite{subbe_validation_2001, noauthor_national_2017}.
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For MEWS, each type of vitals parameter is assigned an individual score based on which range it is in.
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The individual scores are then added together to produce the final MEWS.
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The ranges for individual scores for each type of vital parameter is shown in table \ref{mews-table}.
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\begin{table}[!h]
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\noindent\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{l>{\columncolor{red!15}}c>{\columncolor{orange!15}}c>{\columncolor{yellow!15}}c>{\columncolor{green!15}}c>{\columncolor{yellow!15}}c>{\columncolor{orange!15}}c>{\columncolor{red!15}}c}[hvlines,colortbl-like]
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\hline
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& $\mathbf{+3}$ & $\mathbf{+2}$ & $\mathbf{+1}$ & $\mathbf{+0}$ & $\mathbf{+1}$ & $\mathbf{+2}$ & $\mathbf{+3}$ \\
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\hline
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Systolic Blood Pressure [mmHg] & $<70$ & $71-80$ & $81-100$ & $101-199$ & & $\geq 200$ & \\
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\hline
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Heart Rate [bpm] & & $<40$ & $41-50$ & $51-100$ & $101-110$ & $111-129$ & $\geq 130$ \\
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\hline
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Respiratory Rate [bpm] & & $<9$ & & $9-14$ & $15-20$ & $21-29$ & $\geq 30$ \\
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\hline
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Temperature [°C] & & $<35$ & & $35-38.4$ & & $\geq 38.5$ & \\
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\hline
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AVPU score & & & & alert & reacting to voice & reacting to pain & unresponsive \\
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\hline
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\end{NiceTabular}
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}
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\caption{\label{mews-table}MEWS calculation thresholds}
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\end{table}
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Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, inputting data into an EWS-calculator by hand.
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Low frequency of scoring, miscalculations and practical integration are known setbacks of NEWS2 and other scores\cite{eisenkraft_developing_2023}.
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Remote patient monitoring (RPM) can improve detection of deterioration\cite{shaik_remote_2023} by greatly reducing the
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amount of human interaction required to take measurements and perform EWS calculations.
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A number of studies have explored RPM combined with automated EWS calculation in hospitals\cite{filho_iot-based_2021, un_observational_2021, karvounis_hospital_2021, eisenkraft_developing_2023}.
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With hospitals facing critical patient demand during the SARS-CoV-2 pandemic, interest in exploring remote patient monitoring options surged,
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and NEWS2 emerged as an effective tool for predicting severe infection outcomes\cite{gidari_predictive_2020, otoom_iot-based_2020, filho_iot-based_2021, carr_evaluation_2021},
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while reducing person-to-person contact during patient monitoring.
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Since then, a variety of wearable medical sensors capable of continuously recording vital parameters have been developed and are
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commercially available\cite{noauthor_visi_nodate, noauthor_equivital_nodate, noauthor_vitls_nodate, noauthor_caretaker_nodate, noauthor_medtronic_nodate}.
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%Javanbakht et al. found that continuous vitals monitoring is more cost-effective than intermittent monitoring\cite{javanbakht_cost_2020}, however the findings of this study should be taken lightly due to potential bias reporting.
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\section{Motivation}
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% TODO EWS makes prediction value better than monitoring abnormalities in single vital signs
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Installing and operating traditional continuous monitoring systems, like the vital sign monitors used in medical facilities, demands
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specialized equipment and technical expertise.
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Furthermore, these systems are cumbersome for patients, as they involve connecting patient and sensor device with numerous electrodes
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and cables, restricting patient mobility to the bed area, and physically tying the monitoring equipment
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to a single location.
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In contrast, battery-powered, wireless vitals monitoring devices, such as wearable arm- or wristbands, can incorporate multiple biosensors in a single device in a
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much smaller form-factor, and allow for a much higher degree of patient mobility, rapid deployment and scalability\cite{un_observational_2021}.
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Therefore, utilizing such devices for RPM is a suitable approach.
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While the application of EWS in ambulant care facilities and hospitals has been thoroughly investigated, very little research has been done to
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assess their practicability for remote monitoring of at-risk patients at home.
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Some studies have examined monitoring vital signs of at-home-patients for abnormalities in an experimental setting,
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however in most of them, no automated EWS calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
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In 2015, Anzanpour et al. developed a monitoring system which collects vitals data and calculates EWS, however due to limited or nonexistent
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availability of wireless sensors for all vital signs relevant to EWS, the work was limited to using a laboratory prototype
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and required manual interaction in transferring vitals data\cite{anzanpour_internet_2015}.
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Sahu et al. documented their development of an EWS-supported digital early warning system using the PM6750\cite{sahu_internet--things-enabled_2022},
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an experimental vitals data monitoring device capable of taking continuous measurements in a laboratory setting\cite{noauthor_pm6750_nodate}.
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However, their methodology of real-time EWS calculation using data gathered in the laboratory is inconsistent and was not demonstrated.
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In summary, with the current availability of wearable, networked biosensors and the effectiveness of EWS in medical facilities, combining
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both aspects presents an important and interesting research opportunity which could help reduce mortality and improve clinical outcomes
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for patients at risk of deterioration, both in their homes and on the go.
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%Patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care\cite{downey_patient_2018}.
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%Taking continuous measurements is superior to measuring intermittently\cite{gronbaek_continuous_2023, shaik_remote_2023}.
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\section{Objectives}
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The objective of this research is to explore the practical feasibility of using an existing, clinically validated EWS to remotely monitor
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patients who are still at risk of deterioration after having been dismissed from medical care facilities,
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utilizing smart medical sensor devices.
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Taking measurements using the devices should be as easy and unintrusive as possible for the patient, enabling them to take
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vital sign readings easily from the comfort of their home or while out of the house.
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MEWS will be used as an EWS for deterioration monitoring.
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Furthermore, individual vital signs will be monitored for abnormalities.
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The following vital signs will be captured:
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\begin{itemize}
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\item Heart Rate (HR)
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\item Blood Pressure (BP)
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\item Respiratory Rate (RR)
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\item Body Temperature (TEMP)
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\item Blood Oxygen Saturation (SPO2)
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\item AVPU Score
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\end{itemize}
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The following smart medical devices will be used to take vital sign measurements:
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\begin{table}[!h]
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\noindent\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{lll}[hvlines,colortbl-like]
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\hline
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\textbf{Device Name} & \textbf{Device Type} & \textbf{Captured Vitals Parameter} \\
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\hline
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\href{https://www.withings.com/de/en/scanwatch}{Withings Scanwatch} & Wearable Smartwatch & HR, SPO2, RR (sleeping) \\
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\hline
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\href{https://www.withings.com/de/en/thermo}{Withings Thermo} & Handheld Smart Thermometer & TEMP \\
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\hline
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\href{https://www.withings.com/de/en/bpm-core}{Withings BPM Core} & Smart Blood Pressure Cuff & BP, HR \\
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\hline
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\end{NiceTabular}
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}
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\caption{\label{device-table}Smart Devices used for data capture}
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\end{table}
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This will be accomplished by designing and developing a web application that can capture and process vitals data from a wide range of
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smart medical sensors, and accurately calculates the MEWS based on the captured data.
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If the calculated value lies outside of the acceptable MEWS threshold, both the patient and medical staff can be alerted,
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allowing preemptive action to be taken.
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\section{Tasks}
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\newpage
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\printbibliography
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\end{document}
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