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NCT Number: NCT06886529

PACT Involvement in Cardiology Patients

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. Increases PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days 2. Increases PACT consultation or visit within the next three months among those who experience a serious cardiac event during this period 3. Decreases time to PACT consultation or visit among those seen by PACT during this period 4. Decreases the incidence of death in the intensive care unit (ICU) 5. 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.

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Key information

Conditions

Machine Learning Cardiovascular Outcome Pediatric Cardiology Pediatric Palliative Care

Age range

Up to 18 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

The Hospital for Sick Children

Toronto, M5G1X8, Canada

Location status: Recruiting

Location contact

Lillian Sung, MD, PhD

CONTACT

[email protected]

416-813-5287

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Pediatric inpatients admitted to cardiology

Exclusion criteria

  • Expected to be discharged prior to midnight on the day of admission

Treatment and study plan

ML-based intervention

Other

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.

Primary outcomes

  1. Proportion of admissions with PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days

    Time frame: Time of enrolment to 3 months

    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.

Secondary outcomes

  1. PACT consultation or visit within the next three months among those with a positive model prediction

    Time frame: Time of enrolment to 3 months

    PACT consultation or visit within the next three months among those with a positive model prediction will be measured using SEDAR.

  2. Time to PACT consultation or visit among those seen by PACT

    Time frame: Time of enrolment to 3 months

    Time to PACT consultation or visit among those seen by PACT will be measured using SEDAR.

  3. Death in the ICU

    Time frame: Time of enrolment to 3 months

    Death in the ICU will be measured using SEDAR.

  4. Documentation of goals of care

    Time frame: Time of enrolment to 3 months

    Goals of care will be abstracted via chart review.

Study contacts

Contact information is provided by the study sponsor or research team.

Agata Wolochacz, BMSc

CONTACT

[email protected]

4168137654 ext. 309976

Lillian Sung, MD, PhD

CONTACT

[email protected]

4168135287

Sponsors and collaborators

Lead sponsor

The Hospital for Sick Children

Other

Registry information

Official study title

Early PACT Involvement in Cardiology Patients Using Machine Learning

Important dates

Study start
2025
Primary completion
2027
Study completion
2027
First posted
Mar 20, 2025
Registry last updated
Apr 23, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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