Clinical trial NCT04136418: Predict&Prevent: Use of a Personalised Early Warning Decision Support System to Predict and Prevent Acute Exacerbations of COPD
Official title: 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
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Chronic Obstructive Pulmonary Disease
Lead sponsor: University of Birmingham
Sponsor class: OTHER
Collaborators: name: University Hospitals of North Midlands NHS Trust
First posted: 2019-10-23
Enrollment: 384
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: A phase III, 2 arm, multi-centre, open label, parallel-group randomised designed clinical investigation
Primary purpose: PREVENTION

Brief summary: 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.

This 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.

COPDPredict™ 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.

The COPDPredict™ intervention aims to assist patients and clinicians in preventing clinical deterioration from COPD exacerbations with prompt appropriate intervention.

This 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.

Detailed description: 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.

To 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.

Depending 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

The 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.

This 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).

Eligibility criteria: Inclusion Criteria:

* 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
* ≥2 Acute Exacerbations of COPD (AECOPD) in the previous 12 months according to the patient and/or ≥1 hospital admission for AECOPD
* Exacerbation free for at least 6 weeks
* An age of at least 18 years
* Willing and able to comply with the data collection process out to 12 months from randomisation
* Ability to consent
* Ability to use intervention as judged by the investigator at screening, upon demonstration of the system to the patient

Exclusion Criteria:

* Life expectancy \< 12 months
* 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;

Primary outcomes:
measure: AECOPD-related hospital admissions
timeFrame: For a period of 12 months post randomisation
Secondary outcomes:
measure: Total inpatient days
timeFrame: For a period of 12 months post randomisation
measure: Number of COPD exacerbations reported by the patient
timeFrame: For a period of 12 months post randomisation
measure: Number of A&E visits
timeFrame: For a period of 12 months post randomisation
measure: Symptom control markers using Anthonisen criteria
timeFrame: For a period of 12 months post randomisation
measure: End-user experience of the App
timeFrame: For a period of 12 months post randomisation
measure: COPD specific health-related quality of life
timeFrame: 3, 6, 9 and 12 months post randomisation
measure: Health-related quality of life
timeFrame: 3, 6, 9 and 12 months post randomisation
measure: Lifestyle choices
timeFrame: 3, 6, 9 and 12 months post randomisation
measure: Functional expiratory volume (FEV1)
timeFrame: At 12 months post randomisation
Other outcomes:
measure: Blood C-Reactive Protein (CRP) levels
timeFrame: For a period of 12 months post randomisation
measure: Salivary C-Reactive Protein (CRP) levels
timeFrame: For a period of 12 months post randomisation
Locations:
city: Coventry
country: United Kingdom
facility: University Hospitals Coventry & Warwickshire Trust
state: England
Study officials:
References:
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.