Evidence map›Paper›PMID 40774703›Full record

ArticleBMJ open2025

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.

Ramesh Nadarajah, Ali Wahab, Tobin Joseph, Catherine Reynolds, Sheena Bennett, Mohammad Haris, Adam B Smith, Chris Hayward, Jianhua Wu, Chris P Gale

2 registry-linked trialsAbstract readClinical Trial Protocol
In one paragraph

Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05898165 active not recruitingnot on this map

Future Innovations in Novel Detection for Atrial Fibrillation (FIND-AF): Pilot Study of a Risk Score for Early Detection of Atrial Fibrillation

Typeobservational_patient_registrySponsorUniversity of LeedsRan2023 to 2026Enrolled1,955ConditionsAtrial Fibrillation, Heart Diseases, Cardiovascular DiseasesArmsDevelopment of an algorithm
NCT06444711 naactive not recruitingnot on this map

A Randomised Clinical Trial for OPTIMISation of Cardio-renal-metabolic-pulmonary Disease Guideline Adherence in High Risk Community Dwelling Individuals and Evaluation of Outcomes

TypeinterventionalSponsorUniversity of LeedsRan2025 to 2045Enrolled138ConditionsCardiovascular Diseases, Renal Disease, Metabolic Disease, Pulmonary DiseaseArmsNICE guidance
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ramesh NadarajahUniversity of Leeds, Leeds, UK r.nadarajah@leeds.ac.uk.ORCID http://orcid.org/0000-0001-9895-9356
Ali WahabDepartment of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Tobin JosephLeeds Institute of Data Analytics, Leeds, UK.ORCID http://orcid.org/0000-0002-9283-9895
Catherine ReynoldsLeeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Sheena BennettLeeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Mohammad HarisLeeds Institute of Data Analytics, Leeds, UK.
Adam B SmithLeeds Institute of Data Analytics, Leeds, UK.
Chris HaywardLeeds Institute of Data Analytics, Leeds, UK.
Jianhua WuQueen Mary University of London, London, UK.
Chris P GaleLeeds Institute of Data Analytics, Leeds, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPeople 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.

Indexed as

Atrial FibrillationLung DiseasesMachine LearningAdultAgedAlgorithmsFemaleHumansIndependent LivingMaleMulticenter Studies as TopicPragmatic Clinical Trials as TopicProspective StudiesRandomized Controlled Trials as TopicRisk AssessmentRisk FactorsCardiovascular DiseaseClinical TrialElectronic Health RecordsMachine LearningRisk Factors

Identifiers

PMID40774703
PMCPMC12336468

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.