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
- Are there results?
- Not yet: the study is under way
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
Study at a glance
From the registry record, last updated by the sponsor on 12 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact of Machine Learning-based Clinician Decision Support Algorithms in Perioperative Care - A Randomized Control Trial (IMAGINATIVE Trial)
- ClinicalTrials.gov ID
- NCT05809232
- Acronym / sponsor ID
- IMAGINATIVE / IMAGINATIVE Trial
- Conditions
- Machine Learning
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 9,200 participants (estimated)
- Lead sponsor
- Singapore General HospitalUniversity, hospital or other
- Study start
- May 2023 (estimated)
- Primary completion
- Jul 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 12 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| CARES-guided Group | Active comparator | Other: CARES-guided GroupThe Intervention |
| 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 |
Who may qualify
- Ages
- 21 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Patients >=21 Years old
- Patients going for elective surgery
- 1. Any clinician or nurse that used CARES during the research trial
Key exclusion criteria (2 listed)
- Patients with reduced mental capacity
- Patients who are unable to give consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CARES-guided GroupParticipants 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). | Other | CARES-guided Group |
Primary outcome
Change in perioperative mortality rates
Time frame: Five years
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.
1 secondary outcome
Change in potentially avoidable planned ICU admission after surgery
Time frame: Five years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Apr 2023.
- Derived from this study: PubMed 39806608Abdullah 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CARES-guided Group in people with Machine Learning, run by Singapore General Hospital at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 12 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Hairil Rizal Abdullah, MBBS (63265428, hairil.rizal.abdullah@singhealth.com.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in perioperative mortality rates (time frame: Five years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05809232, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Apr 2023.