Nadarajah Ramesh, Wahab Ali, Joseph Tobin, Reynolds Catherine, Bennett Sheena, Haris Mohammad, Smith Adam B, Hayward Chris, Wu Jianhua, Gale Chris P
University of Leeds, Leeds, UK
Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
BMJ Open. 2025 Aug 6;15(8):e101088. doi: 10.1136/bmjopen-2025-101088.
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.
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.
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.
Clinicaltrials.gov NCT06444711.
通过机器学习算法（心房颤动新型检测的未来创新 [FIND-AF]）识别出的高风险人群患心肾代谢性肺病和心血管死亡的风险增加。OPTIMISE-1随机对照试验旨在测试与常规护理相比，由社区专科医生主导的心肾代谢性肺病（CRMP）及其危险因素的识别和管理，对FIND-AF高风险社区居住个体在6个月随访期间使用治疗干预措施的影响。
OPTIMISE-1是一项多中心、务实、前瞻性、随机、开放标签、盲终点策略试验，将招募138名30岁及以上、FIND-AF风险评分高且先前参加过FIND-AF试点研究（NCT05898165）的参与者，按1:1随机分配到专科医生主导的护理干预组或常规护理组。主要终点是指南指导的CRMP治疗的启动或增加的综合指标。次要终点包括主要终点的组成部分、达到主要终点的时间、新CRMP疾病或危险因素的诊断、新CRMP疾病或危险因素的诊断时间、有记录的CRMP疾病参与者的指南指导的CRMP治疗的启动或增加、新诊断的CRMP疾病参与者的指南指导的CRMP治疗的启动或增加以及参与者报告的生活质量变化。
该研究已获得伦理批准（东北和北泰恩赛德2研究伦理委员会参考号24/NE/0188）。研究结果将在相关会议上公布，并根据资助者的开放获取政策发表在同行评审期刊上。
Clinicaltrials.gov NCT06444711。
