Machine Learning Model for Pediatric Cardiology
Trial Summary
What is the purpose of this trial?
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.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the ML-based intervention treatment for pediatric cardiology?
The research shows that machine learning models can accurately predict risk factors and complications in pediatric heart surgeries, which can help doctors make better treatment decisions. For example, one study achieved 85% accuracy in predicting risk factors for a specific heart surgery, and another model effectively predicted complications after surgery, helping improve patient care.12345
Is the machine learning model for pediatric cardiology generally safe for humans?
Research shows that computational models, like those used in machine learning, are being evaluated for cardiac safety by analyzing health records to predict risks like arrhythmia (irregular heartbeat). These models are being used to improve drug safety assessments, suggesting they are considered safe enough for use in evaluating treatment risks.678910
How does the machine learning model treatment for pediatric cardiology differ from other treatments?
This treatment is unique because it uses machine learning to analyze patient data and predict outcomes, offering personalized treatment plans based on genetic profiles and risk factors. Unlike traditional methods, it provides tailored recommendations for drug therapy, surgical plans, and imaging schedules, enhancing decision-making in pediatric cardiology.1351112
Research Team
Lillian Sung, MD, PhD
Principal Investigator
The Hospital for Sick Children
Eligibility Criteria
This trial is for pediatric inpatients admitted to cardiology. It's designed to help doctors predict serious heart events and improve care with a machine-learning tool. The study excludes patients who don't meet the specific age and condition requirements.Inclusion Criteria
Timeline
Screening
Participants are screened for eligibility to participate in the trial
Pre-deployment Observation
Patient outcomes are observed for a 12-month period before the deployment of the ML model
Post-deployment Observation
Patient outcomes are observed for a 12-month period following the deployment of the ML model
Follow-up
Participants are monitored for safety and effectiveness after treatment
Treatment Details
Interventions
- ML-based intervention
Find a Clinic Near You
Who Is Running the Clinical Trial?
The Hospital for Sick Children
Lead Sponsor