Evidence map›Paper›PMID 36057589›Full record

ReviewBMC psychiatry2022

The development and validation of a medicines optimisation tool to protect the physical health of people with severe mental illness (OPTIMISE).

Aoife Carolan, Dolores Keating, Stephen McWilliams, Caroline Hynes, Mary O'Neill, Fiona Boland, Sharon Holland, Judith Strawbridge, Cristín Ryan

Open access · goldAbstract readReview
In one paragraph

Review in BMC psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.5field-weighted citation impact, top 8% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Making meaning of multimorbidity and severe mental illness: A viewpoint.The Australian and New Zealand journal of psychiatry · 2024
    Article
  4. Article
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

9 authors at 6 institutions in 2 countries.

Aoife CarolanSaint John of God Hospital, Stillorgan, Co. Dublin, Ireland. aoife.carolan@sjog.ie.
Dolores KeatingSaint John of God Hospital, Stillorgan, Co. Dublin, Ireland.
Stephen McWilliamsSaint John of God Hospital, Stillorgan, Co. Dublin, Ireland.
Caroline HynesSaint John of God Hospital, Stillorgan, Co. Dublin, Ireland.
Mary O'NeillTallaght University Hospital, Dublin 24, Ireland.
Fiona BolandData Science Centre, Royal College of Surgeons in Ireland, 123 St Stephens Green, Dublin 2, Dublin, Ireland.
Sharon HollandNorthumberland, Tyne and Wear NHS Foundation Trust, Newcastle upon Tyne, UK.
Judith StrawbridgeSchool of Pharmacy and Biomolecular Science, Royal College of Surgeons Ireland, 123 St Stephen's Green, Dublin 2, Dublin, Ireland.
Cristín RyanSchool of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin 2, Dublin, Ireland.
Saint John of God HospitalRoyal College of Surgeons in Ireland · IECumbria Northumberland Tyne and Wear NHS Foundation Trust · GBTallaght University Hospital · IETrinity College Dublin · IEUniversity College Dublin · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe life expectancy of people with severe mental illness (SMI) is shorter than those without SMI, with multimorbidity and poorer physical health contributing to health inequality. Screening tools could potentially assist the optimisation of medicines to protect the physical health of people with SMI. The aim of our research was to design and validate a medicines optimisation tool (OPTIMISE) to help clinicians to optimise physical health in people with SMI.

methodsA review of existing published guidelines, PubMed and Medline was carried out. Literature was examined for medicines optimisation recommendations and also for reference to the management of physical illness in people with mental illness. Potential indicators were grouped according to physiological system. A multidisciplinary team with expertise in mental health and the development of screening tools agreed that 83 indicators should be included in the first draft of OPTIMISE. The Delphi consensus technique was used to develop and validate the contents. A 17-member multidisciplinary panel of experts from the UK and Ireland completed 2 rounds of Delphi consensus, rating their level of agreement to 83 prescribing indicators using a 5-point Likert scale. Indicators were accepted for inclusion in the OPTIMISE tool after achieving a median score of 1 or 2, where 1 indicated strongly agree and 2 indicated agree, and 75

resultsConsensus was achieved after 2 rounds of Delphi for 62 prescribing indicators where 53 indicators were accepted after round 1 and a further 9 indicators were accepted after round 2. Interrater reliability of OPTIMISE between physicians and pharmacists indicated a substantial level of agreement with a kappa statistic of 0.75.

conclusionsOPTIMISE is a 62 indicator medicines optimisation tool designed to assist decision making in those treating adults with SMI. It was developed using a Delphi consensus methodology and interrater reliability is substantial. OPTIMISE has the potential to improve medicines optimisation by ensuring preventative medicines are considered when clinically indicated. Further research involving the implementation of OPTIMISE is required to demonstrate its true benefit.

trial registrationThis article does not report the results of a health care intervention on human participants.

Indexed as

Health Status DisparitiesMental DisordersAdultConsensusDelphi TechniqueHumansReproducibility of ResultsMedicines OptimisationMental IllnessPhysical HealthScreening ToolSevere Mental Illness

Identifiers

PMID36057589
PMCPMC9441032
OpenAlexW4294531537

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.