Early Warning ScoreEAGLENCT04292158
The New Golden Standard: the Early Warning Score Algorithm
An observational study of Early Warning Score, run by University of Liege at 3 sites in 2 countries.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Early Warning Score
- What is being tested?
- SomnoTouch and MOX
- Type of treatment
- Medical device
- How many people
- 80 participants
- Where
- 3 sitesin 2 countries
- Study dates
- 1 Apr 2019to 30 Apr 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
None listed.
Who cannot join
- 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).
Trial locations and contacts
3 sites in 2 countries.
3 sites in 2 countries
Belgium2 sites
Ziekenhuis Oost-Limburg
Genk, Limbourg, 3600
Centre Hospitalier Universitaire de Liège
Liège, 4000
About this study
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.
Read the 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.
- Conditions
- Early Warning Score
- Being tested
- SomnoTouch, MOX
- Keywords
- Early warning score, Vital parameters, Predictive algorithm, Medical Devices
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SomnoTouchDevice
The patient will be equipped with the SomnoTouch device. This device is able to record and estimate the following data:
ECG data PPG data Heart rate Respiration rate Blood pressure (mmHg) Oxygen saturation (%).
All patient will be stored for further analysis.
MOXDevice
The patient will be equipped with the MOX device. This device is able to record and estimate the following data:
Accelerometers data Activity Body posture
All patient will be stored for further analysis.
| Study group | Role | What they receive |
|---|---|---|
| Main Group | Hospitalized for at least 1 day clinic stay at the participating hospitals Receives: Device: SomnoTouch; Device: MOX |
What is being measured
The primary outcome is the main question the study is designed to answer.
Precision of predictive early warning score algorithm
Precision defined of truepositives divided by the sum of truepositives and truenegatives. This measure indicates how often the predictive EWS was right in identifying adverse events.
Measured: Up to 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Mar 2020
- Study start1 Apr 2019
- Primary completion4 Nov 2019
- Study completion30 Apr 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Early Warning ScoreStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 80 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Liege
- Sponsor type
- University, hospital or other
- Collaborators
- Academisch Ziekenhuis Maastricht, KU Leuven, Ziekenhuis Oost-Limburg, Hasselt University, Maastricht University
- Responsible party
- Principal investigator, Patrizio Lancellotti, University of Liege
- Investigators
- Patrizio Lancellotti, Principal investigator, Liège University Hospital - CHU de LiègePierre Delanaye, Study director, Liège University Hospital - CHU de LiègeArnaud Ancion, Study director, Liège University Hospital - CHU de LiègePieter Vandervoort, Study director, Department of Cardiology and Department Future Health, Ziekenhuis Oost-LimburgDianne de Korte-de Boer, Study chair, Maastricht University Medical Centre, department of Anesthesiology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04292158
- Sponsor's protocol ID
- 2018/184
- Acronym
- EAGLE
- Status last verified
- May 2020
- First submitted
- 1 Oct 2019
- Last updated
- 15 May 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 29203508Jarvis 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.
PubMed 25433295Moon 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.
PubMed 21056524Ajami 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.
PubMed 26958058Smith 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/
PubMed 25506953
Study at a glance
From the registry record, last updated by the sponsor on 15 May 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The New Golden Standard: the Early Warning Score Algorithm
- ClinicalTrials.gov ID
- NCT04292158
- Acronym / sponsor ID
- EAGLE / 2018/184
- Conditions
- Early Warning Score
- Keywords
- Early warning score, Vital parameters, Predictive algorithm, Medical Devices
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 80 participants (actual)
- Lead sponsor
- University of LiegeUniversity, hospital or other
- Collaborators
- Academisch Ziekenhuis Maastricht, KU Leuven, Ziekenhuis Oost-Limburg, Hasselt University, Maastricht University
- Study start
- 1 Apr 2019 (actual)
- Primary completion
- 4 Nov 2019 (actual)
- Study completion
- 30 Apr 2020 (actual)
- First posted
- 2 Mar 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Main Group | – | Device: SomnoTouch, Device: MOXHospitalized for at least 1 day clinic stay at the participating hospitals |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (0 listed)
Key exclusion criteria (2 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SomnoTouchThe patient will be equipped with the SomnoTouch device. This device is able to record and estimate the following data: ECG data PPG data Heart rate Respiration rate Blood pressure (mmHg) Oxygen saturation (%). All patient will be stored for further analysis. | Device | Main Group |
| MOXThe patient will be equipped with the MOX device. This device is able to record and estimate the following data: Accelerometers data Activity Body posture All patient will be stored for further analysis. | Device | Main Group |
Primary outcome
Precision of predictive early warning score algorithm
Time frame: Up to 1 week
Precision defined of truepositives divided by the sum of truepositives and truenegatives. This measure indicates how often the predictive EWS was right in identifying adverse events.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 May 2020.
- Background: PubMed 29203508Gerry 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.
- Background: PubMed 25433295Jarvis 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.
- Background: PubMed 21056524Moon 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.
- Background: PubMed 26958058Ajami 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.
- Background: PubMed 25506953Smith 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/
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Patrizio Lancellotti principal investigator
Liège University Hospital - CHU de Liège
Pierre Delanaye study director
Liège University Hospital - CHU de Liège
Arnaud Ancion study director
Liège University Hospital - CHU de Liège
3 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Early Warning Score, run by University of Liege at 3 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 May 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in Belgium, Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Precision of predictive early warning score algorithm (time frame: Up to 1 week). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04292158, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 May 2020.