PACT Involvement in Cardiology Patients

Recruiting now · Not applicable

Conditions studied: Machine Learning, Cardiovascular Outcome, Pediatric Palliative Care, Pediatric Cardiology

In brief

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.

Key facts

Study ID
NCT06886529
Run by
The Hospital for Sick Children
People needed
1000
Starts
2025-10-16
Expected to finish
2027-10-16
Last updated by the study team
2026-04-23

Who can join

Age: any, up to 18. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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