Machine LearningNCT06886529
PACT Involvement in Cardiology Patients
A study testing ML-based intervention in people with Machine Learning and Cardiovascular Outcome, run by The Hospital for Sick Children at 1 site in Canada.
Trial summary
The goal of this trial is to determine the effectiveness of a machine-learning (ML) model predicting a serious cardiac event within the next three months, when compared pre- versus post-deployment, in pediatric cardiac inpatients. The main questions it aims to answer are whether deployment of the ML model: 1. Read more about this study
- Who is it for?
- Ages Up to 18 years
- Conditions the study is for
- Machine Learning, Cardiovascular Outcome, Pediatric Palliative Care
- What is being tested?
- ML-based intervention
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,000 participants
- Where
- 1 siteCanada
- Study dates
- 16 Oct 2025to 16 Oct 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 Up to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Who may be able to join
- Pediatric inpatients admitted to cardiology
Who cannot join
- Expected to be discharged prior to midnight on the day of admission
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Early PACT Involvement in Cardiology Patients Using Machine Learning
The goal of this trial is to determine the effectiveness of a machine-learning (ML) model predicting a serious cardiac event within the next three months, when compared pre- versus post-deployment, in pediatric cardiac inpatients. The main questions it aims to answer are whether deployment of the ML model:
- Increases PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days
- Increases PACT consultation or visit within the next three months among those who experience a serious cardiac event during this period
- Decreases time to PACT consultation or visit among those seen by PACT during this period
- Decreases the incidence of death in the intensive care unit (ICU)
- Increases documentation of goals of care
High-risk cardiology patients will be identified by an ML model each morning. If the patient has been seen by the PACT team within the past year, the update will go to the PACT team members. If the patient hasn't been seen by the PACT team, the email will be sent to the cardiology physician in charge of the patient. This physician will decide whether a PACT consultation is necessary based on their clinical judgment. If so, a referral will be made using the usual process. Outcomes of the identified patients will be compared pre- and post-deployment.
Read the detailed description
At The Hospital for Sick Children (SickKids), the collaboration between cardiology and palliative care is much stronger than other centers, with routine involvement in patients being considered for heart transplant. Despite this, earlier involvement of palliative care would be advantageous. Our cardiology co-investigators identified patients who would benefit from earlier palliative care team involvement as those receiving advanced heart therapies (defined as ventricular assist device (VAD) and being wait listed for heart transplant) and those who die. The study team created a clinical deployment environment named SickKids Enterprise-wide Data in Azure Repository (SEDAR). [1] SEDAR is a modular and robust approach to deliver foundational data that is re-usable across multiple ML projects. It offers validated EHR data in a standardized and curated schema. ML is a promising approach to identify cardiac patients at the highest risk of these serious cardiac outcomes who may benefit from earlier palliative care team involvement. To assess the effectiveness of this approach, patient outcomes will be compared pre- and post-deployment of the ML model. The pre-period will include patients admitted for a 12-month period before deployment (starting 15 months prior to deployment). The post-period will include patients admitted for a 12-month period following deployment starting 3 months post-deployment start.
- Being tested
- ML-based intervention
- Keywords
- quality of life, cardiovascular outcomes, machine learning, prediction models, pediatric
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ML-based interventionOther
ML model predicting a serious cardiac event in cardiac patients, defined as VAD procedure, being wait listed for heart transplant or death within the next three months.
| Study group | Role | What they receive |
|---|---|---|
| ML model | Experimental | Cardiac patients identified by an ML model for having the highest risk of serious cardiac outcomes. Receives: Other: ML-based intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of admissions with PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days
The primary outcome will be the proportion of admissions with PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days. This variable will be measured using SEDAR.
Measured: Time of enrolment to 3 months
Secondary outcomes (4)
PACT consultation or visit within the next three months among those with a positive model prediction
PACT consultation or visit within the next three months among those with a positive model prediction will be measured using SEDAR.
Measured: Time of enrolment to 3 months
Time to PACT consultation or visit among those seen by PACT
Time to PACT consultation or visit among those seen by PACT will be measured using SEDAR.
Measured: Time of enrolment to 3 months
Death in the ICU
Death in the ICU will be measured using SEDAR.
Measured: Time of enrolment to 3 months
Documentation of goals of care
Goals of care will be abstracted via chart review.
Measured: Time of enrolment to 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2025
- Study start16 Oct 2025
- Primary completion1 Sep 2026
- Study completion16 Oct 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
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Hospital for Sick Children
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lillian Sung, The Hospital for Sick Children
- Investigators
- Lillian Sung, MD, PhD, Principal investigator, The Hospital for Sick Children
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06886529
- Sponsor's protocol ID
- 3433
- Status last verified
- Sep 2026
- First submitted
- 13 Mar 2025
- Last updated
- 4 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Patel P, Robinson PD, Phillips R, Baggott C, Devine K, Gibson P, Guilcher GMT, Holdsworth MT, Neumann E, Orsey AD, Spinelli D, Thackray J, van de Wetering M, Cabral S, Sung L, Dupuis LL. Treatment of breakthrough and prevention of refractory chemotherapy-induced nausea and vomiting in pediatric cancer patients: Clinical practice guideline update. Pediatr Blood Cancer. 2023 Aug;70(8):e30395. doi: 10.1002/pbc.30395. Epub 2023 May 13.
PubMed 37178438