Machine LearningNCT06902688
Timely Ordering of Pharmacogenetic Testing
A study testing ML-based intervention in people with Machine Learning and Prediction Models, run by The Hospital for Sick Children at 1 site in Canada.
Trial summary
The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question[s] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months: Increases the offering of pharmacogenetic… Read more about this study
- Who is it for?
- Ages 6 months to 18 years
- Conditions the study is for
- Machine Learning, Prediction Models, Pediatrics
- What is being tested?
- ML-based intervention
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 275 participants
- Where
- 1 siteCanada
- Study dates
- 10 Jun 2025to 10 Jun 2027
- Phase
- Interventional
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 6 months to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
Who may be able to join
- Inpatient at The Hospital for Sick Children
- Between 6 months to 18 years old
Who cannot join
- Prior pharmacogenetic testing and/or prior receipt of a targeted medication
- Current Intensive Care Unit (ICU) admission
- Expected hospital discharge is prior to midnight on the day of admission
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Timely Ordering of Pharmacogenetic Testing in Pediatric Oncology
The goal of this trial is to learn if a machine learning (ML) model can help optimize drug therapy in the pediatric population. The main question[s] it aims to answer are whether a machine learning model predicting receipt of a targeted medication within the next three months:
- Increases the offering of pharmacogenetic testing prior to receipt of a targeted medication
- Increases the number of patients with pharmacogenetic results prior to receipt of a targeted medication
- Increases the number of patients who have alteration in medication choice or dose based on pharmacogenetic results
This trial only focuses on the prediction and provision of participants with a high-risk of receiving a medication with a pharmacogenetic indication in the next three months.
Read the detailed description
This study aims to evaluate the effectiveness of a ML model in predicting patients at high risk of requiring a "targeted medication" within the next three months. A machine learning model will predict, the morning following admission to any inpatient service, whether there will be receipt of a targeted medication within the next three months. The research team will be notified regarding eligible patients each morning, and the research team or pharmacogenomics team will approach the patient's primary care team as applicable. By leveraging ML, this study seeks to enhance the identification of patients who would benefit from such medications in a timely and resource-efficient manner.
The study team identified specific medications as indications for pharmacogenetic testing based on prevalence and level of evidence for modifying prescribing practices. These pre-selected medications are referred to as "targeted medications" and are as follows: azathioprine, brivaracetam, clobazam, clopidogrel, flecainide, phenytoin, tacrolimus, voriconazole and warfarin. Only systemically administered (oral, subcutaneous, intramuscular or intravenous) medications or prescriptions (e.g. not topical, intrathecal or intravitreal) are included. Phenytoin was only considered if given orally (to exclude emergency administration without a plan for ongoing treatment).
Pharmacogenetic testing will be offered to participants and conducted as addressed in an associated pharmacogenetic testing protocol (REB# 1000053445 PI: Iris Cohn).
- Conditions
- Machine Learning, Prediction Models, Pediatrics, Precision Medicine
- Being tested
- ML-based intervention
- Keywords
- precision medicine, machine learning, pharmacogenetics, prediction models, pediatrics
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ML-based interventionOther
A ML-based model will predict and identify participants that are at high-risk of receiving a targeted medication within three months after their hospital admission date.
| Study group | Role | What they receive |
|---|---|---|
| ML model | Experimental | Participants predicted by an ML model to receive a "targeted medication" within three months following admission. Receives: Other: ML-based intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of Patients with Pharmacogenetic Testing
The primary outcome will be the proportion of patients with pharmacogenetic testing offered among those who receive a medication with a pharmacogenetic indication within three months of prediction time. Testing must be offered prior to receipt of the first targeted medication.
Measured: Day 1 to 3 months
Secondary outcomes (2)
Number of patients with pharmacogenetic results available prior to receipt of targeted medication
Measured via chart review
Measured: Day 1
Number of patients who have alteration in medication choice or dose based on pharmacogenetic results
Measured via chart review
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Mar 2025
- Study start10 Jun 2025
- Primary completion1 Sep 2026
- Study completion10 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Machine LearningStudies 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
- 275 participants (estimated)
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 Children
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06902688
- Sponsor's protocol ID
- 3423
- Status last verified
- Sep 2026
- First submitted
- 13 Mar 2025
- Last updated
- 4 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No