Protocol for the OPTIMSE-1 randomised clinical trial to test specialist-led identification and management of cardio-renal-metabolic-pulmonary disease in machine learning algorithm-detected high-risk community-dwelling individuals

Abstract

INTRODUCTION: People identified as higher risk by a machine learning algorithm (Future Innovations in Novel Detection of Atrial Fibrillation [FIND-AF]) are at increased risk of cardio-renal-metabolic-pulmonary disease and cardiovascular death. The OPTIMISE-1 randomised controlled trial aims to test the effect of community-based specialist-led identification and management of cardio-renal-metabolic-pulmonary (CRMP) disease and risk factors compared with usual care on the use of therapeutic interventions over a follow-up of 6 months among high FIND-AF risk community-dwelling individuals. METHODS AND ANALYSIS: OPTIMISE-1 is a multicentre, pragmatic, prospective, randomised, open-label, blinded-endpoint strategy trial that will recruit 138 participants aged 30 years or older, with a high FIND-AF risk score and previously enrolled in the FIND-AF pilot study (NCT05898165), to be randomised 1:1 to a specialist-led care intervention or usual care. The primary endpoint is a composite of initiation or increase of guideline-directed CRMP therapies. The secondary endpoints are the components of the primary endpoint, time to primary endpoint, diagnosis of new CRMP diseases or risk factors, time to diagnosis of new CRMP diseases or risk factors, initiation or increase of guideline-directed CRMP therapies for participants with recorded CRMP disease, initiation or increase of guideline-directed CRMP therapies for participants with newly diagnosed CRMP disease and change in participant-reported quality of life. ETHICS AND DISSEMINATION: The study has ethical approval (the North East & North Tyneside 2 Research Ethics Committee reference 24/NE/0188). Findings will be announced at relevant conferences and published in peer-reviewed journals in line with the Funder's open access policy. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov NCT06444711.

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