Predict&Prevent: Use of a Personalised Early Warning Decision Support System to Predict and Prevent Acute Exacerbations of COPD

Chronic Obstructive Pulmonary Disease Clinical Trial

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

Status Active, not recruiting

Clinical Trial 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.

Clinical Trial 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). ;

Related Conditions & MeSH terms

• Chronic Obstructive Pulmonary Disease
• Lung Diseases
• Lung Diseases, Obstructive
• Pulmonary Disease, Chronic Obstructive

• NCT number: NCT04136418
• Study type: Interventional
• Source: University of Birmingham
• Status: Active, not recruiting
• Phase: N/A
• Start date: October 7, 2020
• Completion date: March 31, 2023