Vomiting Prevention in Children With Cancer

NCT06886451 | Recruiting

• 1 other identifier: 3373
• Study Type: interventional
• Target: 1,332
• Locations: 1 country
• Sites: 1

Brief Summary

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. 1.Reduce the number of vomiting episodes
2. 2.Increase the proportion receiving care pathway-consistent care
3. 3.Impact on number of administrations and costs of antiemetic medications

Conditions

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

Keywords

Vomiting; Machine learning; Quality of life; Pediatric oncology

Outcome Measures

Primary Outcomes (1)

• Vomiting 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 Outcomes (4)

• Number of episodes of vomiting

  0-96 hours post prediction time

• Care pathway-consistent care

  0-96 hours post prediction time

• Number of antiemetic administrations

  0-96 hours post prediction time

• Antiemetic costs

  0-96 hours post prediction time

Other Outcomes (1)

• Number of SEDAR Predictions and Interventions

  0-96 hours post prediction time

Study Arms (1)

ML model

EXPERIMENTAL

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

Other: ML-based intervention

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.

Also known as: Machine learning model

ML model

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• All pediatric patients admitted to the oncology service at SickKids

You may not qualify if:

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

Sponsors & Collaborators

• The Hospital for Sick Children: lead

Study Sites (1)

The Hospital for Sick Children

Toronto, Ontario, 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: 37178438 BACKGROUND

MeSH Terms

Conditions

Vomiting; Neoplasms

Condition Hierarchy (Ancestors)

Signs and Symptoms, Digestive; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• 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

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Lillian Sung, MD, PhD

CONTACT

416-813-5287; lillian.sung@sickkids.ca

Agata Wolochacz, BMSc

CONTACT

416-813-7654; agata.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: March 18, 2025
• Primary Completion (Estimated): March 18, 2027
• Study Completion (Estimated): March 18, 2027
• Last Updated: March 5, 2026

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)