NCT06886529

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

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
1,000

participants targeted

Target at P75+ for not_applicable

Timeline
17mo left

Started Oct 2025

Typical duration for not_applicable

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress28%
Oct 2025Oct 2027

First Submitted

Initial submission to the registry

March 13, 2025

Completed
7 days until next milestone

First Posted

Study publicly available on registry

March 20, 2025

Completed
7 months until next milestone

Study Start

First participant enrolled

October 16, 2025

Completed
1.7 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

June 28, 2027

Expected
4 months until next milestone

Study Completion

Last participant's last visit for all outcomes

October 16, 2027

Last Updated

April 23, 2026

Status Verified

April 1, 2026

Enrollment Period

1.7 years

First QC Date

March 13, 2025

Last Update Submit

April 22, 2026

Conditions

Machine LearningCardiovascular OutcomePediatric Palliative CarePediatric Cardiology

Keywords

quality of lifecardiovascular outcomesmachine learningprediction modelspediatric

Outcome Measures

Primary Outcomes (1)

  • 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.

    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

    Time of enrolment to 3 months

  • Time to PACT consultation or visit among those seen by PACT

    Time of enrolment to 3 months

  • Death in the ICU

    Time of enrolment to 3 months

  • Documentation of goals of care

    Time of enrolment to 3 months

Study Arms (1)

ML model

EXPERIMENTAL

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

Other: ML-based intervention

Interventions

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.

ML model

Eligibility Criteria

AgeUp to 18 Years
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64)

You may qualify if:

  • Pediatric inpatients admitted to cardiology

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

The Hospital for Sick Children

Toronto, M5G1X8, Canada

RECRUITING

Related Publications (1)

  • 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.

    PMID: 37178438BACKGROUND

Study Officials

  • Lillian Sung, MD, PhD

    The Hospital for Sick Children

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Lillian Sung, MD, PhD

CONTACT

4168135287lillian.sung@sickkids.ca

Agata Wolochacz, BMSc

CONTACT

4168137654agata.wolochacz@sickkids.ca

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NA
Masking
NONE
Purpose
SUPPORTIVE CARE
Intervention Model
SINGLE GROUP
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Chief Clinical Data Scientist, Paediatric Oncologist

Study Record Dates

First Submitted

March 13, 2025

First Posted

March 20, 2025

Study Start

October 16, 2025

Primary Completion (Estimated)

June 28, 2027

Study Completion (Estimated)

October 16, 2027

Last Updated

April 23, 2026

Record last verified: 2026-04

Data Sharing

IPD Sharing
Will not share

Locations

CA(1)