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☐ ☆ ✇ BMJ Open

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

Por: Nadarajah · R. · Wahab · A. · Joseph · T. · Reynolds · C. · Bennett · S. · Haris · M. · Smith · A. B. · Hayward · C. · Wu · J. · Gale · C. P. — Agosto 7th 2025 at 08:17
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.

☐ ☆ ✇ PLOS ONE Medicine&Health

Effect of traffic volumes on polycyclic aromatic hydrocarbons of particulate matter: A comparative study from urban and rural areas in Malaysia

by Samer Al-Battawi, Mohd Talib Latif, Vivien How, Karuppiah Thilakavathy, Haris Hafizal Abd Hamid, Chung Keat Tan, Yu Bin Ho

Motor vehicles emit most Malaysian PAHs in particulate matter of 2.5 μm (PM2.5-bound PAHs). Although traffic-related air pollution harms healthy people, there is a knowledge gap regarding PAHs’ effects on Malaysians. This study examines PM2.5-bound PAH concentrations, distribution, sources, and health risks in Malaysia’s high and low-traffic zones. Kuala Lumpur (KL) and Hulu Langat (HL) exhibit Malaysia’s high- and low-traffic areas. The high-volume air sampler collected 40 ambient PM2.5 samples at both locations. Solid-phase extraction and gas chromatography-mass spectrometry (GC-MS) assessed PAHs. The mean PM2.5-bound PAH concentrations in KL (5.85 ng m-3) were significantly higher than in HL (0.55 ng m-3) (p-3) and eleven times more high-molecular-weight PAHs (HMW-PAHs) (3.22 vs. 0.28 ng m-3) than HL. Over 51% of PM2.5 air samples at both sites included HMW-PAHs. Source apportionment tools (Diagnostic ratio, positive matrix factorization, and principal component analysis) showed that fossil fuel combustions (petrol and diesel) produced the greatest PAHs in both locations. Moreover, PAH exposure impinged higher carcinogenic health risks in KL than in HL. In conclusion, traffic and automobile pollution account for the short- and long-term health risks posed by PAHs in both regions.
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