Clinical trial NCT04292158: The New Golden Standard: the Early Warning Score Algorithm
Official title: The New Golden Standard: the Early Warning Score Algorithm
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Early Warning Score
Keywords: Early warning score
Vital parameters
Predictive algorithm
Medical Devices
Lead sponsor: University of Liege
Sponsor class: OTHER
Collaborators: name: Academisch Ziekenhuis Maastricht
name: KU Leuven
name: Ziekenhuis Oost-Limburg
name: Hasselt University
name: Maastricht University
First posted: 2020-03-02
Enrollment: 80
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Hospitalized for at least 1 day at the participating hospitals excluding patients admitted at high intensity units: Intensive care units, Coronary care units, Emergency room.

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Exclusion Criteria:

* Suffering from infectious disease
* Participating to another study that could intervene with the study results (e.g. experimental medication that could affect the heart rate).

Primary outcomes:
measure: Precision of predictive early warning score algorithm
timeFrame: Up to 1 week
Secondary outcomes:
Other outcomes:
Locations:
city: Genk
country: Belgium
facility: Ziekenhuis Oost-Limburg
state: Limbourg
city: Liège
country: Belgium
facility: Centre Hospitalier Universitaire de Liège
city: Maastricht
country: Netherlands
facility: Maastricht University Medical Center+
Study officials:
affiliation: Liège University Hospital - CHU de Liège
name: Patrizio Lancellotti
role: PRINCIPAL_INVESTIGATOR
affiliation: Liège University Hospital - CHU de Liège
name: Pierre Delanaye
role: STUDY_DIRECTOR
affiliation: Liège University Hospital - CHU de Liège
name: Arnaud Ancion
role: STUDY_DIRECTOR
affiliation: Department of Cardiology and Department Future Health, Ziekenhuis Oost-Limburg
name: Pieter Vandervoort
role: STUDY_DIRECTOR
affiliation: Maastricht University Medical Centre, department of Anesthesiology
name: Dianne de Korte-de Boer
role: STUDY_CHAIR
References:
citation: Gerry S, Birks J, Bonnici T, Watkinson PJ, Kirtley S, Collins GS. Early warning scores for detecting deterioration in adult hospital patients: a systematic review protocol. BMJ Open. 2017 Dec 3;7(12):e019268. doi: 10.1136/bmjopen-2017-019268.
citation: Jarvis S, Kovacs C, Briggs J, Meredith P, Schmidt PE, Featherstone PI, Prytherch DR, Smith GB. Aggregate National Early Warning Score (NEWS) values are more important than high scores for a single vital signs parameter for discriminating the risk of adverse outcomes. Resuscitation. 2015 Feb;87:75-80. doi: 10.1016/j.resuscitation.2014.11.014. Epub 2014 Nov 26.
citation: Moon A, Cosgrove JF, Lea D, Fairs A, Cressey DM. An eight year audit before and after the introduction of modified early warning score (MEWS) charts, of patients admitted to a tertiary referral intensive care unit after CPR. Resuscitation. 2011 Feb;82(2):150-4. doi: 10.1016/j.resuscitation.2010.09.480. Epub 2010 Nov 5.
citation: Ajami S, Teimouri F. Features and application of wearable biosensors in medical care. J Res Med Sci. 2015 Dec;20(12):1208-15. doi: 10.4103/1735-1995.172991.
citation: Smith MEB, Chiovaro JC, O'Neil M, Kansagara D, Quinones A, Freeman M, Motu'apuaka M, Slatore CG. Early Warning System Scores: A Systematic Review [Internet]. Washington (DC): Department of Veterans Affairs (US); 2014 Jan. Available from http://www.ncbi.nlm.nih.gov/books/NBK259026/