PACT Involvement in Cardiology Patients

NCT ID: NCT06886529

Last Updated: 2025-04-02

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-06-28
• Study Completion Date: 2027-06-28

Brief 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. 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.

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.

Conditions

Machine Learning; Cardiovascular Outcome; Pediatric Palliative Care; Pediatric Cardiology

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: NONE

Study Groups

ML model

Cardiac patients identified by an ML model for having the highest risk of serious cardiac outcomes.

Group Type: EXPERIMENTAL

ML-based intervention

Intervention Type: 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.

Interventions

ML-based intervention

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Pediatric inpatients admitted to cardiology

Exclusion Criteria

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

• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

The Hospital for Sick Children

OTHER

Sponsor Role: lead

Responsible Party

Lillian Sung

Chief Clinical Data Scientist, Paediatric Oncologist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Lillian Sung, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Locations

The Hospital for Sick Children

Toronto, , Canada

Countries

Canada

Central Contacts

Lillian Sung, MD, PhD

Role: CONTACT

lillian.sung@sickkids.ca

4168135287

Agata Wolochacz, BMSc

Role: CONTACT

agata.wolochacz@sickkids.ca

4166187599

Facility Contacts

Lillian Sung, MD, PhD

Role: primary

lillian.sung@sickkids.ca

416-813-5287

References

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.

Reference Type: BACKGROUND

PMID: 37178438 (View on PubMed)

Other Identifiers

3433

Identifier Type: -

Identifier Source: org_study_id