Evidence map›Paper›PMID 39289023›Full record

ArticleBMJ open2024

Digital wound monitoring with artificial intelligence to prioritise surgical wounds in cardiac surgery patients for priority or standard review: protocol for a randomised feasibility trial (WISDOM).

Judith Tanner, Melissa Rochon, Roy Harris, Jacqueline Beckhelling, James Jurkiewicz, Lara Mason, Janet Bouttell, Sarah Bolton, Jon Dummer, Keith Wilson and 2 more

Registry-linked trialAbstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06475703 (Wound Imaging Software and Digital platfOrM to Detect and Prioritise Non-healing Surgical Wounds), which is not on this map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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.

NCT06475703 nacompletednot on this map

Wound Imaging Software and Digital platfOrM to Detect and Prioritise Non-healing Surgical Wounds (WISDOM)

TypeinterventionalSponsorGuy's and St Thomas' NHS Foundation TrustRan2024 to 2025Enrolled120ConditionsSurgical Wound, Wound Healing Delayed, Heart, Surgery, Heart, Functional Disturbance as ResultArmsIsla wound prioritisation module, Standard follow-up
3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. 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

12 authors.

Judith TannerSchool of Health Sciences, University of Nottingham, Nottingham, UK Judith.Tanner@nottingham.ac.uk.ORCID 0000-0003-0023-3149
Melissa RochonInfection Prevention and Control, Guy's and St Thomas' NHS Foundation Trust, London, UK.ORCID 0000-0002-1101-2256
Roy HarrisNIHR Research Support Service, Nottingham, UK.
Jacqueline BeckhellingUniversity Hospitals of Derby and Burton NHS Foundation Trust, Derby, UK.ORCID 0000-0002-5898-8678
James JurkiewiczIsla Health, London, UK.
Lara MasonIsla Health, London, UK.
Janet BouttellCentre for Healthcare Equipment and Technology Adoption, Nottingham, UK.
Sarah BoltonCentre for Healthcare Equipment and Technology Adoption, Nottingham, UK.
Jon DummerHealth Innovation East Midlands, Nottingham, UK.
Keith WilsonLiverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK.
Luxmi DhoonmoonCentral and North West London NHS Foundation Trust, London, UK.
Karen CariagaInfection Prevention and Control, Guy's and St Thomas' NHS Foundation Trust, London, UK.ORCID 0009-0006-5525-2362

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDigital surgical wound monitoring for patients at home is becoming an increasingly common method of wound follow-up. This regular monitoring improves patient outcomes by detecting wound complications early and enabling treatment to start before complications worsen. However, reviewing the digital data creates a new and additional workload for staff. The aim of this study is to assess a surgical wound monitoring platform that uses artificial intelligence to assist clinicians to review patients' wound images by prioritising concerning images for urgent review. This will manage staff time more effectively. METHODS AND ANALYSIS: This is a feasibility study for a new artificial intelligence module with 120 cardiac surgery patients at two centres serving a range of patient ethnicities and urban, rural and coastal locations. Each patient will be randomly allocated using a 1:1 ratio with mixed block sizes to receive the platform with the new detection and prioritising module (for up to 30 days after surgery) plus standard postoperative wound care or standard postoperative wound care only. Assessment is through surveys, interviews, phone calls and platform review at 30 days and through medical notes review and patient phone calls at 60 days. Outcomes will assess safety, acceptability, feasibility and health economic endpoints. The decision to proceed to a definitive trial will be based on prespecified progression criteria. ETHICS AND DISSEMINATION: Permission to conduct the study was granted by the North of Scotland Research Ethics Committee 1 (24/NS0005) and the MHRA (CI/2024/0004/GB). The results of this Wound Imaging Software Digital platfOrM (WISDOM) study will be reported in peer-reviewed open-access journals and shared with participants and stakeholders. TRIAL REGISTRATION NUMBERS: ISRCTN16900119 and NCT06475703.

Indexed as

Artificial IntelligenceCardiac Surgical ProceduresFeasibility StudiesHumansMonitoring, PhysiologicRandomized Controlled Trials as TopicSurgical WoundSurgical Wound InfectionArtificial IntelligenceClinical TrialeHealthInfection controlTelemedicineWOUND MANAGEMENT

Identifiers

PMID39289023
PMCPMC11409336

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.