Vomiting Prevention in Children With Cancer

Sponsor
The Hospital for Sick Children
Study ID
NCT06886451
Status
Recruiting
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Conditions

  • Chemotherapy Induced Nausea and Vomiting
  • Pediatric Cancer
  • Quality of Life (QOL)

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Not accepted

Interventions

  • ML-based intervention — OTHER
    For each patient, a ML model will predict the risk of vomiting within the next 96 hours. Patients will then receive care pathway-consistent interventions based on the ML model predictions.

Study Details

The goal of this single arm trial is to learn if a machine learning (ML) model predicting the risk of vomiting within the next 96 hours will impact vomiting outcomes in inpatient cancer pediatric patients. The main questions it aims to answer are whether an ML model predicting the risk of vomiting within the next 96 hours will: Primary 1\. Reduce the proportion with any vomiting within the 96-hour window Secondary 1. Reduce the number of vomiting episodes 2. Increase the proportion receiving care pathway-consistent care 3. Impact on number of administrations and costs of antiemetic medications Newly admitted participants will have a ML model predict the risk of vomiting within the next 96 hours according to their medical admission information. The prediction will be made at 8:30 AM following admission. Pharmacists will be charged with bringing information about patients' vomiting risk to the attention of the medical team and implementing interventions.

Key Dates

First listed
Mar 20, 2025
Start date
Mar 18, 2025
Status verified
May 2025
Primary completion
Mar 18, 2027
Completion
Mar 18, 2027

Study Design

Enrollment
1,332 participants (estimated)
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE

Arms

  • Experimental: ML model
    ML model to predict the risk of vomiting within the next 96 hours.

Primary Outcome Measure

Vomiting post prediction time [ Time Frame: 0-96 hours post prediction time ]

Central Contacts

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