Ignite Creation Date:
2025-12-25 @ 4:43 AM
Ignite Modification Date:
2025-12-26 @ 3:44 AM
Study NCT ID:
NCT04136418
Status:
UNKNOWN
Last Update Posted:
2022-11-01
First Post:
2019-10-21
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Predict&Prevent: Use of a Personalised Early Warning Decision Support System to Predict and Prevent Acute Exacerbations of COPD
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D029424', 'term': 'Pulmonary Disease, Chronic Obstructive'}], 'ancestors': [{'id': 'D008173', 'term': 'Lung Diseases, Obstructive'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}, {'id': 'D002908', 'term': 'Chronic Disease'}, {'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'A phase III, 2 arm, multi-centre, open label, parallel-group randomised designed clinical investigation'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 384}}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2020-10-07', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-10', 'completionDateStruct': {'date': '2023-03-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-10-31', 'studyFirstSubmitDate': '2019-10-21', 'studyFirstSubmitQcDate': '2019-10-21', 'lastUpdatePostDateStruct': {'date': '2022-11-01', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2019-10-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-03-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Blood C-Reactive Protein (CRP) levels', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'Variation in blood CRP levels during exacerbations'}, {'measure': 'Salivary C-Reactive Protein (CRP) levels', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'Variation in salivary CRP levels during exacerbations'}], 'primaryOutcomes': [{'measure': 'AECOPD-related hospital admissions', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'The number of AECOPD-related hospital admissions'}], 'secondaryOutcomes': [{'measure': 'Total inpatient days', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'Number of days a patient is in hospital'}, {'measure': 'Number of COPD exacerbations reported by the patient', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'Number of patient defined exacerbations'}, {'measure': 'Number of A&E visits', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'Number of times that a patient reports attending Accident \\& Emergency (A\\&E) due to COPD exacerbations'}, {'measure': 'Symptom control markers using Anthonisen criteria', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'Presence of symptom control markers (breathlessness, colour of sputum, amount of sputum produced)'}, {'measure': 'End-user experience of the App', 'timeFrame': 'For a period of 12 months post randomisation', 'description': 'technology acceptability usability/utility via bespoke qualitative questionnaires and interviews'}, {'measure': 'COPD specific health-related quality of life', 'timeFrame': '3, 6, 9 and 12 months post randomisation', 'description': 'Assessed by the COPD Assessment Test validated questionnaire'}, {'measure': 'Health-related quality of life', 'timeFrame': '3, 6, 9 and 12 months post randomisation', 'description': 'Assessed by the EQ-5D-5L validated questionnaire'}, {'measure': 'Lifestyle choices', 'timeFrame': '3, 6, 9 and 12 months post randomisation', 'description': 'assessed via either responses to bespoke questions on the App or bespoke questionnaires and interviews'}, {'measure': 'Functional expiratory volume (FEV1)', 'timeFrame': 'At 12 months post randomisation', 'description': 'Functional expiratory volume assessed by spirometry'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Chronic Obstructive Pulmonary Disease']}, 'referencesModule': {'references': [{'pmid': '40799048', 'type': 'DERIVED', 'citation': 'Gkini E, Mehta RL, Tearne S, Doos L, Jowett S, Gale N, Turner AM. Use of a Personalised Early Warning Decision Support System for Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Results of the "Predict & Prevent" Phase III Trial. COPD. 2025 Dec;22(1):2544719. doi: 10.1080/15412555.2025.2544719. Epub 2025 Aug 13.'}, {'pmid': '36914185', 'type': 'DERIVED', 'citation': "Kaur D, Mehta RL, Jarrett H, Jowett S, Gale NK, Turner AM, Spiteri M, Patel N. Phase III, two arm, multi-centre, open label, parallel-group randomised designed clinical investigation of the use of a personalised early warning decision support system to predict and prevent acute exacerbations of chronic obstructive pulmonary disease: 'Predict & Prevent AECOPD' - study protocol. BMJ Open. 2023 Mar 13;13(3):e061050. doi: 10.1136/bmjopen-2022-061050."}, {'pmid': '34495549', 'type': 'DERIVED', 'citation': 'Poot CC, Meijer E, Kruis AL, Smidt N, Chavannes NH, Honkoop PJ. Integrated disease management interventions for patients with chronic obstructive pulmonary disease. Cochrane Database Syst Rev. 2021 Sep 8;9(9):CD009437. doi: 10.1002/14651858.CD009437.pub3.'}]}, 'descriptionModule': {'briefSummary': "COPD is a common complex disease with debilitating breathlessness; mortality and reduced quality of life, accelerated by frequent lung attacks (exacerbations). Changes in breathlessness, cough and/or sputum production often change before exacerbations but patients cannot judge the importance of such changes so they remain unreported and untreated. Remote monitoring systems have been developed but none have yet convincingly shown the ability to identify these early changes of an exacerbation and how severe they can be.\n\nThis study asks if a smart digital health intervention (COPDPredict™) can be used by both COPD patients and clinicians to improve self-management, predict lung attacks early, intervene promptly, and avoid hospitalisation.\n\nCOPDPredict™ consists of a patient-facing App and clinician-facing smart early warning decision support system. It collects and processes information to determine a patient's health through a combination of wellbeing scores, lung function and biomarker measurements. This information is combined to generate personalised lung health profiles. As each patient is monitored over time, the system detects changes from an individual's 'usual health' and indicates the likelihood of imminent exacerbation of COPD. When this happens, alerts are sent to both the individual and the clinician, with instructions to the patient on what actions to take. Any advice from clinicians can be exchanged via the App's secure messaging facility. If patients have followed the action plan but fail to improve or if an episode triggers an 'at high risk alert', clinicians are further prompted to case manage and intervene with escalated treatment, including home visits, if necessary.\n\nThe COPDPredict™ intervention aims to assist patients and clinicians in preventing clinical deterioration from COPD exacerbations with prompt appropriate intervention.\n\nThis study will randomise 384 patients who have frequent exacerbations, from hospitals in the West Midlands, to either (1) standard self-management plan (SSMP) with rescue medication (RM), or (2) COPDPredict™ and RM.", 'detailedDescription': 'Changes in dyspnoea, coughing and/or sputum production often precede exacerbations but as symptoms vary within-same day and across days, patients cannot easily judge the significance of such changes with the result that exacerbations remain unreported and untreated. Furthermore due to heterogeneity amongst COPD patients, predictions must be personalised to be clinically meaningful. Remote monitoring and POC systems have evolved rapidly but none have yet convincingly demonstrated the capability to predict exacerbations and stratify episode severity.\n\nTo address the above problem, COPDPredictTM has been created and developed. This System automatically processes information that is regularly sent by patients using COPDPredictTM), which connects to peripheral monitors via Bluetooth and uses intelligent software to determine a patient\'s health through a combination of wellbeing scores, lung function and measurements of key biomarkers in blood and saliva. The clinical team has access to a secure web portal (dashboard) which allows them to monitor patient data, case manage and make informed decisions on clinical practice.\n\nDepending on the degree of change from a given patient\'s \'usual health\', timely alerts are sent to the individual, with sign-posting to an action plan. Alerts are also sent to clinicians who support and advise patients via App\'s secure messaging facility. If patients fail to improve with self-treat plan or if an episode triggers an \'at high risk alert\' from the start, clinicians are prompted to be involved and intervene with escalated treatment\n\nThe Clinician facing dashboard allows for "real-time" case management and the ability to remotely monitor the patients and facilitate interaction. Clinicians can choose to escalate treatments based on the results being transmitted by the patients.\n\nThis clinical investigation asks if COPDPredictTM can be used by patients with COPD at home and the clinicians managing the patients to improve self-management and help them identify exacerbations, intervene promptly and avoid hospitalisation. The clinical investigation will randomise 384 patients, from 4 hospitals in the West Midlands. United Kingdom, who have frequent AECOPD to use either the SSMP and RM (if needed according to the SSMP) or the COPDPredict App and RM (if needed according to the App self-management plan or clinician input).'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Clinically diagnosed chronic obstructive pulmonary disease (COPD), confirmed by post-bronchodilator spirometry and defined as a ratio of Forced Expiratory VolumeFEV1 to Forced Vital Capacity \\<0.7 and \\<lower limit of normal for age post bronchodilator use\n* ≥2 Acute Exacerbations of COPD (AECOPD) in the previous 12 months according to the patient and/or ≥1 hospital admission for AECOPD\n* Exacerbation free for at least 6 weeks\n* An age of at least 18 years\n* Willing and able to comply with the data collection process out to 12 months from randomisation\n* Ability to consent\n* Ability to use intervention as judged by the investigator at screening, upon demonstration of the system to the patient\n\nExclusion Criteria:\n\n* Life expectancy \\< 12 months\n* Patients with active infection, unstable co-morbidities at enrolment or very severe comorbidities such as grade IV heart failure, renal failure on haemodialysis or active neoplasia or significant cognitive impairment;'}, 'identificationModule': {'nctId': 'NCT04136418', 'briefTitle': 'Predict&Prevent: Use of a Personalised Early Warning Decision Support System to Predict and Prevent Acute Exacerbations of COPD', 'organization': {'class': 'OTHER', 'fullName': 'University of Birmingham'}, 'officialTitle': 'A Randomised Designed Clinical Investigation of the Use of a Personalised Early Warning Decision Support System With Novel Saliva Bio-profiling to Predict and Prevent Acute Exacerbations of Chronic Obstructive Pulmonary Disease', 'orgStudyIdInfo': {'id': 'Worktribe 833757'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'Usual care', 'description': 'Patients currently self-manage their condition using antibiotics and steroids when their disease symptoms match the criteria in information provided by a clinician', 'interventionNames': ['Other: Usual care']}, {'type': 'EXPERIMENTAL', 'label': 'Mobile App device', 'description': 'Patients enter their health status onto an App which is relayed to the healthcare team, who can then provide further information or clinical intervention should they so choose', 'interventionNames': ['Device: COPDPredict mobile App']}], 'interventions': [{'name': 'COPDPredict mobile App', 'type': 'DEVICE', 'description': "An App on a mobile device is used by the patient to track the status of their COPD and inform the patient's care team", 'armGroupLabels': ['Mobile App device']}, {'name': 'Usual care', 'type': 'OTHER', 'description': 'Patients self-manage their COPD using prescribed medication in accordance with basic guidance information', 'armGroupLabels': ['Usual care']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'CV2 2DX', 'city': 'Coventry', 'state': 'England', 'country': 'United Kingdom', 'facility': 'University Hospitals Coventry & Warwickshire Trust', 'geoPoint': {'lat': 52.40656, 'lon': -1.51217}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'The data will be commercially sensitive'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Birmingham', 'class': 'OTHER'}, 'collaborators': [{'name': 'University Hospitals of North Midlands NHS Trust', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}