Clinical Evaluation of an AI Risk Prediction System (AI-TRiPS)
Trial statusNot yet recruiting
Trial phaseEarly Phase 1
Trial typeInterventional
Biological sexAll
Age16+
SponsorQueen Mary University of London
About this trial
The goal of this clinical study is to evaluate a software device and its impact on clinician behaviour during the initial management of trauma patients in a real-world clinical setting. Known as the AI-TRiPS Device this software uses real-time prehospital data and machine learning-based risk predictions which are displayed digitally for hospital trauma teams prior patient arrival.
The investigators will use a Stepped Wedge Cluster Randomised Controlled study design with an integrated process evaluation.
The Device will be deployed across the London Major Trauma System where the Major Trauma Centres will be the clusters. Each cluster will transition from control (standard care) to intervention at a pre-specified time (time of transition is randomised).
Primary Outcome: Clinician behaviour, assessed via the accuracy of risk prediction and clinician confidence.
Secondary Outcome: Clinician acceptability, care process metrics, patient outcomes, and safety endpoints.
Primary study population: Hospital trauma clinicians, following initial resuscitation of each eligible trauma patient, who will complete electronic questionnaires.
Secondary study population: Adult trauma patients, data will be collected for the duration of their index admission to hospital, to assess outcomes and enable comparison with clinician risk predictions.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Senior clinical decision-maker involved in the initial trauma resuscitation (e.g. consultant or senior trainee in emergency medicine, anaesthesia, intensive care medicine, or surgery).
Based at one of the four participating Major Trauma Centres.
Able and willing to provide informed consent.
Completed the required study-specific training.
Disqualifiers
Aged under 16
Not treated by London's Air Ambulance.
Transported to a non-participating hospital.
Not managed by any participating clinicians.
Trial design
Design model
Sequential
Treatments tested in this trial
AI-TRiPS Device
DeviceThis is Software as a Medical Device designed to function as an aid to inform clinical situational awareness by presenting predictions of patient trajectory (probability of death, probability of trauma induced coagulopathy, probability of red cell transfusion, probability of acute kidney injury).
Treatment groups
1,200 Participants
are divided into 2 treatment groupsGroup A: AI TRIPS device interventionExperimental treatment 1 intervention
Group B: Usual Standard CareNo intervention 0 interventions
Trial outcomes
Primary outcomes
1
Clinician Risk Prediction - Mortality, Trauma Induced Coagulopathy, and Acute Kidney Injury
Clinician participants will make probability estimates (0-100%) on index admission in each of the 3 domains.
Time frame
Baseline2
Clinician Risk Prediction - Estimation of Blood Transfusion Volume
Clinicians will estimate the number(n) of packed red blood cell (pRBC) units required for transfusion in the first 24 hours. The estimation will take place immediately after initial resuscitation.
Time frame
Baseline3
Clinician Confidence
Clinician Participants will self-report their confidence in their predictions using the Post-Task Confidence Scale (PTCS), a Likert scale from 1-7, where the higher the score the higher the level confidence.
Time frame
Baseline to 24 Hours - Immediately following initial clinician predictions4
Clinician Cognitive Effort
Clinician participants self-report the mental effort required to make each prediction using the Paas Mental Effort Scale ( Likert Scale 1-9) where a lower score corresponds to low mental effort.
Time frame
Baseline to 24 Hours - immediately following risk predictionsSecondary outcomes
1
Clinician Decision-Making Behaviour - Decision Making
Measurement of whether a decision was made (Decision in this case refers to activation of the major haemorrhage protocol, or proceeding directly to definitive haemorrhage control). This data will be extracted from the National Major Trauma Registry and/or patient clinical records. Binary (yes/no) based on whether the outcome was performed.
Time frame
From discharge through to study completion, an average of 1 year.2
Clinician Decision-Making Behaviour - Appropriateness of Decision Making
Expert panel review of decision making with regards to activation of major haemorrhage protocol/proceeding directly to definitive haemorrhage control. Expert review of extracted patient data from National Major Trauma Registry and/or Patient clinical records. Binary (Appropriate/Inappropriate)
Time frame
From Discharge through to study completion, an average of 1 year.3
Clinician Decision-Making Behaviour - Clinician Confidence
Clinician Participants will self-report their confidence in their predictions using the Post-Task Confidence Scale (PTCS), a Likert scale from 1-7, where the higher the score the higher the level confidence.
Time frame
Baseline to 24 hours - Immediately following initial clinician decision making4
Clinician Decision-Making Behaviour - Clinician Cognitive Effort
Clinician participants self-report the mental effort required to make each prediction using the Paas Mental Effort Scale ( Likert Scale 1-9) where a lower score corresponds to low mental effort.
Time frame
Baseline to 24 Hours - immediately following risk predictionsLocations
Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Queen Mary University of London
Lead sponsor
Congressionally Directed Medical Research Programs
Collaborator
University of Aberdeen
Collaborator
Barts & The London NHS Trust
Collaborator
St George's University Hospitals NHS Foundation Trust
Collaborator
Imperial College Healthcare NHS Trust
Collaborator
King's College Hospital NHS Trust
Collaborator
London Ambulance Service NHS Trust
Collaborator
London's Air Ambulance Charity
Collaborator
Source ClinicalTrials.gov
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