Chemotherapy Induced Nausea and VomitingNCT06886451
Vomiting Prevention in Children With Cancer
A study testing ML-based intervention in people with Chemotherapy Induced Nausea and Vomiting and Quality of Life (QOL), run by The Hospital for Sick Children at 1 site in Canada.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Chemotherapy Induced Nausea and Vomiting, Quality of Life (QOL), Pediatric Cancer
- What is being tested?
- ML-based intervention
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,953 participants
- Where
- 1 siteCanada
- Study dates
- 18 Mar 2025to 18 Mar 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- All pediatric patients admitted to the oncology service at SickKids
Who cannot join
- Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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:
Read more
Primary
- Reduce the proportion with any vomiting within the 96-hour window
Secondary
- Reduce the number of vomiting episodes
- Increase the proportion receiving care pathway-consistent care
- Impact on specific antiemetic medication administrations
Exploratory: To determine whether deployment of the ML model reduces the proportion of patients with any nausea within 96 hours and to characterize references to the algorithm in clinical notes following deployment, and to determine the number of times the prediction report dashboard was opened.
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.
Read the 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, and specific antiemetic administrations.
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.
- Being tested
- ML-based intervention
- Keywords
- Vomiting, Machine learning, Quality of life, Pediatric oncology
- Medical subject headings
- Vomiting, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ML-based interventionOther
Also called Machine learning model
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 group | Role | What they receive |
|---|---|---|
| ML model | Experimental | ML model to predict the risk of vomiting within the next 96 hours. Receives: Other: ML-based intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
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)
Measured: 0-96 hours post prediction time
Secondary outcomes (3)
Number of episodes of vomiting
The number of episodes of vomiting will be determined by counting the number of distinct time stamps with any vomiting description.
Measured: 0-96 hours post prediction time
Care pathway-consistent care
Care pathway-consistent care will be measured as a binary outcome (yes/no).
Measured: 0-96 hours post prediction time
Specific antiemetic administrations
Specific antiemetic administrations within the 96-hour window will be counted.
Measured: 0-96 hours post prediction time
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Mar 2025
- Study start18 Mar 2025
- Primary completion2 Sep 2026
- Study completion18 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Chemotherapy Induced Nausea and VomitingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 1,953 participants (actual)
Sponsor and investigators
- Lead sponsor
- The Hospital for Sick Children
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lillian Sung, The Hospital for Sick Children
- Investigators
- Lillian Sung, MD, PhD, Principal investigator, The Hospital for Sick ChildrenLee Dupuis, RPh, PhD, Principal investigator, The Hospital for Sick ChildrenPriya Patel, PharmD, Principal investigator, The Hospital for Sick ChildrenAdam Yan, MD, MBI, Principal investigator, The Hospital for Sick ChildrenLawrence Guo, PhD, Principal investigator, The Hospital for Sick Children
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06886451
- Sponsor's protocol ID
- 3373
- Status last verified
- Oct 2026
- First submitted
- 13 Mar 2025
- Last updated
- 7 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 37178438
Study at a glance
From the registry record, last updated by the sponsor on 7 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prevention of Vomiting in Pediatric Oncology Inpatients Using Machine Learning
- ClinicalTrials.gov ID
- NCT06886451
- Acronym / sponsor ID
- 3373
- Keywords
- Vomiting, Machine learning, Quality of life, Pediatric oncology
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,953 participants (actual)
- Lead sponsor
- The Hospital for Sick ChildrenUniversity, hospital or other
- Study start
- 18 Mar 2025 (actual)
- Primary completion
- 2 Sep 2026 (actual)
- Study completion
- 18 Mar 2027 (estimated)
- First posted
- 20 Mar 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Reduce the proportion with any vomiting within the 96-hour window
Secondary
- Reduce the number of vomiting episodes
- Increase the proportion receiving care pathway-consistent care
- Impact on specific antiemetic medication administrations
Exploratory: To determine whether deployment of the ML model reduces the proportion of patients with any nausea within 96 hours and to characterize references to the algorithm in clinical notes following deployment, and to determine the number of times the prediction report dashboard was opened.
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, and specific antiemetic administrations.
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 design
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| ML model | Experimental | Other: ML-based interventionML model to predict the risk of vomiting within the next 96 hours. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- All pediatric patients admitted to the oncology service at SickKids
Key exclusion criteria (1 listed)
- Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ML-based interventionAlso: Machine learning modelFor 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 | ML model |
Primary outcome
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)
3 secondary outcomes
Number of episodes of vomiting
Time frame: 0-96 hours post prediction time
Care pathway-consistent care
Time frame: 0-96 hours post prediction time
Specific antiemetic administrations
Time frame: 0-96 hours post prediction time
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Oct 2026.
- Background: PubMed 37178438Patel 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Lillian Sung, MD, PhD principal investigator
The Hospital for Sick Children
Lee Dupuis, RPh, PhD principal investigator
The Hospital for Sick Children
Priya Patel, PharmD principal investigator
The Hospital for Sick Children
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing ML-based intervention in people with Chemotherapy Induced Nausea and Vomiting and Quality of Life (QOL), run by The Hospital for Sick Children at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 7 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Vomiting post prediction time (time frame: 0-96 hours post prediction time). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06886451, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Oct 2026; AzTrials last refreshed its copy on 26 Oct 2026.