Vomiting Prevention in Children With Cancer

NCT ID: NCT06886451

Last Updated: 2025-05-15

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1332 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-03-18
• Study Completion Date: 2027-03-18

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. 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.

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.

Conditions

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

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: NONE

Study Groups

ML model

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

Group Type: EXPERIMENTAL

ML-based intervention

Intervention Type: 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.

Interventions

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.

Intervention Type: OTHER

Other Intervention Names

Machine learning model

Eligibility Criteria

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

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

The Hospital for Sick Children

OTHER

Sponsor Role: lead

Responsible Party

Lillian Sung

Chief Clinical Data Scientist, Paediatric Oncologist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Lillian Sung, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Lee Dupuis, RPh, PhD

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Priya Patel, PharmD

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Adam Yan, MD, MBI

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Lawrence Guo, PhD

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Santiago Arciniegas, MSc

Role: PRINCIPAL_INVESTIGATOR

The Hospital for Sick Children

Locations

The Hospital for Sick Children

Toronto, Ontario, Canada

Site Status: RECRUITING

Countries

Canada

Central Contacts

Lillian Sung, MD, PhD

Role: CONTACT

lillian.sung@sickkids.ca

416-813-5287

Agata Wolochacz, BMSc

Role: CONTACT

agata.wolochacz@sickkids.ca

4166187599

Facility Contacts

Lillian Sung, MD, PhD

Role: primary

lillian.sung@sickkids.ca

416-813-5287

References

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.

Reference Type: BACKGROUND

PMID: 37178438 (View on PubMed)

Other Identifiers

3373

Identifier Type: -

Identifier Source: org_study_id