Clinical trial NCT06886451: Vomiting Prevention in Children With Cancer
Official title: Prevention of Vomiting in Pediatric Oncology Inpatients Using Machine Learning
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Chemotherapy Induced Nausea and Vomiting
Quality of Life (QOL)
Pediatric Cancer
Keywords: Vomiting
Machine learning
Quality of life
Pediatric oncology
Lead sponsor: The Hospital for Sick Children
Sponsor class: OTHER
First posted: 2025-03-20
Enrollment: 1332
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: SUPPORTIVE_CARE

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.

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

Primary outcomes:
measure: Vomiting post prediction time
timeFrame: 0-96 hours post prediction time
Secondary outcomes:
measure: Number of episodes of vomiting
timeFrame: 0-96 hours post prediction time
measure: Care pathway-consistent care
timeFrame: 0-96 hours post prediction time
measure: Number of antiemetic administrations
timeFrame: 0-96 hours post prediction time
measure: Antiemetic costs
timeFrame: 0-96 hours post prediction time
Other outcomes:
measure: Number of SEDAR Predictions and Interventions
timeFrame: 0-96 hours post prediction time
Locations:
city: Toronto
country: Canada
facility: The Hospital for Sick Children
state: Ontario
Study officials:
affiliation: The Hospital for Sick Children
name: Lillian Sung, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Hospital for Sick Children
name: Lee Dupuis, RPh, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Hospital for Sick Children
name: Priya Patel, PharmD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Hospital for Sick Children
name: Adam Yan, MD, MBI
role: PRINCIPAL_INVESTIGATOR
affiliation: The Hospital for Sick Children
name: Lawrence Guo, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Hospital for Sick Children
name: Santiago Arciniegas, MSc
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.