Machine LearningIMAGINATIVENCT05809232
Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care
A study testing CARES-guided Group in people with Machine Learning, run by Singapore General Hospital at 1 site in Singapore.
Trial summary
Predicting surgical risks are important to patients and clinicians for shared decision making process and management plan. The study team aim to conduct a hybrid type 1 effectiveness implementation study design. Read more about this study
- Who is it for?
- Ages 21 to 100 years
- Conditions the study is for
- Machine Learning
- What is being tested?
- CARES-guided Group
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 9,200 participants
- Where
- 1 siteSingapore
- Study dates
- May 2023to Dec 2027
- Phase
- Interventional
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients >=21 Years old
- Patients going for elective surgery
- For semi-structured interview:
- 1. Any clinician or nurse that used CARES during the research trial
Who cannot join
- Patients with reduced mental capacity
- Patients who are unable to give consent
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Singapore1 site
Singapore General Hospital
Singapore
Hairil Rizal Abdullah, MMED, hairil.rizal.abdullah@singhealth.com.sg
Hairil Rizal Abdullah, MMED
About this study
Official title: Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care - A Randomized Control Trial (IMAGINATIVE Trial)
Predicting surgical risks are important to patients and clinicians for shared decision making process and management plan. The study team aim to conduct a hybrid type 1 effectiveness implementation study design. A Randomized Controlled Trial where participants undergoing surgery In Singapore General Hospital (SGH) will be allocated in 1:1 ratio to CARES-guided (unblinded to risk level) or to unguided (blinded to risk level) groups. All participants undergoing elective surgeries in SGH will be considered eligible for enrolment into the study. For elective surgeries, the participants will mainly be recruited from Pre-admission Centre. The outcome of this study will help patients and clinicians make better decisions together. Firstly, the deployment of the CARES model in a live clinical environment could potentially reduce postoperative complications and improve the quality of surgical care provision. The findings from this study would allow fine-tuning of CARES as well as further deployment of additional risk models for specific complications other than Mortality and ICU stay. This in turn would translate to better health for the surgical population and improved cost-effectiveness. This is significant as the surgical population is expected to continuously grow due to improved access to care, better technologies and the aging population. Secondly, IMAGINATIVE will be instrumental in improving our understanding of the deployment strategies for AI/ML predictive models in healthcare. Models such as CARES could be the standard of care in the future if proven to improve the health outcomes of patients. As model deployments are costly and can be disruptive to the EMR processes, this study would be the initial spark for future deployment and health services research focusing on improving the value of these model deployments.
- Conditions
- Machine Learning
- Being tested
- CARES-guided Group
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CARES-guided GroupOther
Participants randomised to the CARES-guided arm will have their CARES-score calculated and entered into the Pre-Anesthesia Assessment electronic form within the Electronic Medical Records (EMR). This score and its relevant advisories will be prominently displayed on this electronic form. (Participants on this arm will receive this intervention in addition to the routine practice).
| Study group | Role | What they receive |
|---|---|---|
| CARES-guided Group | Active comparator | The Intervention Receives: Other: CARES-guided Group |
| Non CARES-Guided Group | No intervention | The control - Participants randomized to the control arm will continue to have their routine Pre-Anesthesia Assessment on the electronic form, without the CARES calculator calculations, as per current practice |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in perioperative mortality rates
To assess the effectiveness of the Machine Learning Clinical Decision Support (ML-CDS). Hypothesis: The CARES-guided group will have a 30% relative reduction in one-year mortality rate due to the increased clinician awareness of the risks.
Measured: Five years
Secondary outcomes (1)
Change in potentially avoidable planned ICU admission after surgery
To assess the effectiveness of the ML-CDS algorithm in optimizing ICU bed utilization, which is an important and costly hospital resource Hypothesis: There will be a 25% relative reduction in the potentially avoidable planned ICU admission after surgery in the CARES-guided group
Measured: Five years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Apr 2023
- Study startMay 2023 (estimated)
- Primary completionJul 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Machine LearningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 9,200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Singapore General Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05809232
- Sponsor's protocol ID
- IMAGINATIVE Trial
- Acronym
- IMAGINATIVE
- Status last verified
- Mar 2023
- First submitted
- 14 Mar 2023
- Last updated
- 12 Apr 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Abdullah HR, Brenda TPY, Loh C, Ong M, Lamoureux E, Lim GH, Lum E. Protocol for the impact of machine learning-based clinician decision support algorithims in perioperative care (IMAGINATIVE) in Singapore general hospital : a large prospective randomised controlled trial. BMJ Open. 2024 Dec 20;14(12):e086769. doi: 10.1136/bmjopen-2024-086769.
PubMed 39806608