Ignite Creation Date:
2025-12-25 @ 12:14 AM
Ignite Modification Date:
2025-12-25 @ 10:15 PM
Study NCT ID:
NCT04292158
Status:
COMPLETED
Last Update Posted:
2020-05-15
First Post:
2019-10-01
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
The New Golden Standard: the Early Warning Score Algorithm
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_ONLY'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 80}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2019-04-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2020-05', 'completionDateStruct': {'date': '2020-04-30', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2020-05-14', 'studyFirstSubmitDate': '2019-10-01', 'studyFirstSubmitQcDate': '2020-02-28', 'lastUpdatePostDateStruct': {'date': '2020-05-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2020-03-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2019-11-04', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Precision of predictive early warning score algorithm', 'timeFrame': 'Up to 1 week', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Early warning score', 'Vital parameters', 'Predictive algorithm', 'Medical Devices'], 'conditions': ['Early Warning Score']}, 'referencesModule': {'references': [{'pmid': '29203508', 'type': 'BACKGROUND', '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.'}, {'pmid': '25433295', 'type': 'BACKGROUND', '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.'}, {'pmid': '21056524', 'type': 'BACKGROUND', '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.'}, {'pmid': '26958058', 'type': 'BACKGROUND', '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.'}, {'pmid': '25506953', 'type': 'BACKGROUND', '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/"}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': "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.\n\nThe 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.\n\nThe 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.\n\nThe 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.\n\nThe 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Exclusion Criteria:\n\n* Suffering from infectious disease\n* Participating to another study that could intervene with the study results (e.g. experimental medication that could affect the heart rate).'}, 'identificationModule': {'nctId': 'NCT04292158', 'acronym': 'EAGLE', 'briefTitle': 'The New Golden Standard: the Early Warning Score Algorithm', 'organization': {'class': 'OTHER', 'fullName': 'University of Liege'}, 'officialTitle': 'The New Golden Standard: the Early Warning Score Algorithm', 'orgStudyIdInfo': {'id': '2018/184'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Main Group', 'description': 'Hospitalized for at least 1 day clinic stay at the participating hospitals', 'interventionNames': ['Device: SomnoTouch', 'Device: MOX']}], 'interventions': [{'name': 'SomnoTouch', 'type': 'DEVICE', 'description': 'The patient will be equipped with the SomnoTouch device. This device is able to record and estimate the following data:\n\nECG data PPG data Heart rate Respiration rate Blood pressure (mmHg) Oxygen saturation (%).\n\nAll patient will be stored for further analysis.', 'armGroupLabels': ['Main Group']}, {'name': 'MOX', 'type': 'DEVICE', 'description': 'The patient will be equipped with the MOX device. This device is able to record and estimate the following data:\n\nAccelerometers data Activity Body posture\n\nAll patient will be stored for further analysis.', 'armGroupLabels': ['Main Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '3600', 'city': 'Genk', 'state': 'Limbourg', 'country': 'Belgium', 'facility': 'Ziekenhuis Oost-Limburg', 'geoPoint': {'lat': 50.965, 'lon': 5.50082}}, {'zip': '4000', 'city': 'Liège', 'country': 'Belgium', 'facility': 'Centre Hospitalier Universitaire de Liège', 'geoPoint': {'lat': 50.63373, 'lon': 5.56749}}, {'city': 'Maastricht', 'country': 'Netherlands', 'facility': 'Maastricht University Medical Center+', 'geoPoint': {'lat': 50.84833, 'lon': 5.68889}}], 'overallOfficials': [{'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': 'Liège University Hospital - CHU de Liège'}, {'name': 'Pieter Vandervoort', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Department of Cardiology and Department Future Health, Ziekenhuis Oost-Limburg'}, {'name': 'Dianne de Korte-de Boer', 'role': 'STUDY_CHAIR', 'affiliation': 'Maastricht University Medical Centre, department of Anesthesiology'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Liege', 'class': 'OTHER'}, 'collaborators': [{'name': 'Academisch Ziekenhuis Maastricht', 'class': 'OTHER'}, {'name': 'KU Leuven', 'class': 'OTHER'}, {'name': 'Ziekenhuis Oost-Limburg', 'class': 'OTHER'}, {'name': 'Hasselt University', 'class': 'OTHER'}, {'name': 'Maastricht University', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Patrizio Lancellotti', 'investigatorAffiliation': 'University of Liege'}}}}