Early Warning Score Clinical Trial
Official title:
The New Golden Standard: the Early Warning Score Algorithm
The objective is this study is the development and implementation of a smart algorithm to compute an early warning indicator able to predict early patient deterioration.
Data will be collected at the three sites using the SomnoTouchTM and MOXTM devices,
commercially available and CE approved. Every month, the data will be sent to the KUL and UM
to develop the algorithm. Study centers will also send some pre-defined patient
characteristics extracted from the patient's EMR to better contextualize the data.
The EWS formula has a free interpretation of the vital parameters weighting and the vital
parameters to be taken into account in the scoring system. Therefore, many variants of the
EWS arose the past decade (i.e. MEWS, NEWS). The algorithm developed in this study should
define an objective approach for the EWS formula, diminishing the discordances regarding the
weight per parameter. Using a patient-personalized approach, the definite algorithm should be
based on the patient's vital parameter measured during his/her whole hospitalization,
generating a patient-personalized weight per parameter and an overall reliable EWS scoring
system.
The EWS score is often only measured twice per day per patient, creating a large window for
disease worsening. The algorithm developed in this study could be deployed along the wearable
device developed in the WearIT4Health project. The device would continuously feed the
algorithm with data acquired from its sensors. Thus, the EWS would be computed every 10
seconds.
The EWS scoring system has already been proven to be an effective approach in reducing
clinical deterioration, reducing the admission to intensive care units and thus overall
reducing mortality. However, as mentioned above the EWS is measured in a rather low
frequency. Therefore, estimation of the EWS score via continuous monitored parameters should
further increase patient survival.
The primary objective of the EAGLE study is to collect continuously monitored vital and
activity parameter data and use it to develop an algorithm that can early identify clinical
deterioration to optimize the application of the EWS system.
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