Vomiting Prevention in Children With Cancer

September 3, 2026 updated by: Lillian Sung, The Hospital for Sick Children

Prevention of Vomiting in Pediatric Oncology Inpatients Using Machine Learning

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

Detailed Description

Vomiting is one of the most common complications of cancer therapies in pediatric patients, with substantial negative impacts on quality of life. Vomiting can also reduce oral intake, worsen nutritional status and lead to hospitalization. Thus, efforts to control vomiting are crucial. The ability to predict which patients are most likely to vomit is limited; machine learning (ML) is a promising approach. Preliminary work completed for this study includes development of an enterprise data warehouse sourced from Epic suitable for ML named SickKids Enterprise-wide Data in Azure Repository (SEDAR) and validation of vomiting outcomes in SEDAR. Next, a standardized process for model training, evaluation and deployment was conducted by the study team. This was implemented to train a retrospective model to predict vomiting (0-96 hours post prediction time), which demonstrated satisfactory performance during a prospective silent trial. The care pathway and patient-specific report to facilitate clinical care based on a positive prediction has also been created by the study team, expending on a previously developed antiemetic care pathway based on clinical practice guidelines. The patient-specific report lists each patient's risk of vomiting (0-96 hours post prediction time), vomiting prior to prediction time, planned chemotherapy or procedures, current antiemetic orders and history of vomiting with the most recent admission.

For model deployment, pharmacists will be charged with bringing information about patients' vomiting risk to the attention of the medical team and implementing care pathway-consistent interventions. Pharmacists will receive a list of high-risk patients and the developed tools (care pathway and patient-specific report) each morning. Outcomes will be evaluated for a one-year period pre- and post-deployment. Primary outcome will be any vomiting within the 96-hour period post prediction time. Secondary outcomes will be the number of vomiting episodes within the 96-hour period, care pathway-consistent care, antiemetic administrations and antiemetic costs.

The study team includes pediatric pharmacists, pediatric oncologists and experts in machine learning, clinical epidemiology, implementation sciences, care pathway development and biostatistics.

Vomiting is one of the most distressing aspects of cancer therapy and, with current approaches, medical management is failing a substantial number of patients. This work will contribute to precision medicine by identifying patients with the highest need for individualized review and therapy optimization. This effort is anticipated to improve the quality of care and quality of life for pediatric cancer patients.

Study Type

Interventional

Enrollment (Estimated)

1332

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Canada
    • Ontario
      • Toronto, Ontario, Canada, M5G1X8
        • The Hospital for Sick Children

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • All pediatric patients admitted to the oncology service at SickKids

Exclusion Criteria:

  • Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Supportive Care
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: ML model
ML model to predict the risk of vomiting within the next 96 hours.
Other: ML-based intervention
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.
Other Names:
  • Machine learning model

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Vomiting post prediction time
Time Frame: 0-96 hours post prediction time
The primary outcome will be any vomiting (a binary variable) 0-96 hours post prediction time. In the SickKids EHR, vomiting is described in the flowsheets by emesis volume, emesis count, emesis amount description, emesis color/appearance, and any vomiting/retching/gagging. Multiple descriptors can be used at a specific time stamp but no one descriptor is used consistently. Thus, the best measure of vomiting is a binary variable (yes/no) where yes represents any vomiting entry within the 96-hour window. Vomiting determination using this approach was validated. In a retrospective assessment, patients who received etoposide, ifosfamide or treosulfan were identified and 60 patients were randomly selected with stratification by age and HCT status. (Patel P et al, 2023)
0-96 hours post prediction time

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of episodes of vomiting
Time Frame: 0-96 hours post prediction time
The number of episodes of vomiting will be determined by counting the number of distinct time stamps with any vomiting description.
0-96 hours post prediction time
Care pathway-consistent care
Time Frame: 0-96 hours post prediction time
Care pathway-consistent care will be measured as a binary outcome (yes/no).
0-96 hours post prediction time
Number of antiemetic administrations
Time Frame: 0-96 hours post prediction time
All antiemetic administrations within the 96-hour window will be counted. The total number of administrations overall and by specific antiemetic agent will be tabulated.
0-96 hours post prediction time
Antiemetic costs
Time Frame: 0-96 hours post prediction time
Medication costs are available in SEDAR. The total costs of antiemetics per kg overall and by specific antiemetic agent will be tabulated.
0-96 hours post prediction time

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of SEDAR Predictions and Interventions
Time Frame: 0-96 hours post prediction time
The number of times notes in SEDAR refer to the predictions and interventions to address them including antiemetic modifications during the prediction window will be descriptively reported. The role of the note writer will be documented (such as pharmacist or fellow).
0-96 hours post prediction time

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The Hospital for Sick Children

Investigators

  • Principal Investigator: Lillian Sung, MD, PhD, The Hospital for Sick Children
  • Principal Investigator: Lee Dupuis, RPh, PhD, The Hospital for Sick Children
  • Principal Investigator: Priya Patel, PharmD, The Hospital for Sick Children
  • Principal Investigator: Adam Yan, MD, MBI, The Hospital for Sick Children
  • Principal Investigator: Lawrence Guo, PhD, The Hospital for Sick Children
  • Principal Investigator: Santiago Arciniegas, MSc, The Hospital for Sick Children

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 18, 2025

Primary Completion (Actual)

September 2, 2026

Study Completion (Estimated)

March 18, 2027

Study Registration Dates

First Submitted

March 13, 2025

First Submitted That Met QC Criteria

March 13, 2025

First Posted (Actual)

March 20, 2025

Study Record Updates

Last Update Posted (Actual)

September 4, 2026

Last Update Submitted That Met QC Criteria

September 3, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 3373

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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