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Timely Ordering of Pharmacogenetic Testing

T

The Hospital for Sick Children

Status

Not yet enrolling

Conditions

Pediatrics
Prediction Models
Machine Learning
Precision Medicine

Treatments

Other: ML-based intervention

Study type

Interventional

Funder types

Other

Identifiers

Details and patient eligibility

About

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.

Full 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).

Enrollment

275 estimated patients

Sex

All

Ages

6 months to 18 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • Inpatient at The Hospital for Sick Children
  • Between 6 months to 18 years old

Exclusion criteria

  • 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 design

Primary purpose

Supportive Care

Allocation

N/A

Interventional model

Single Group Assignment

Masking

None (Open label)

275 participants in 1 patient group

ML model
Experimental group
Description:
Participants predicted by an ML model to receive a "targeted medication" within three months following admission.
Treatment:
Other: ML-based intervention

Trial contacts and locations

1

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Central trial contact

Agata Wolochacz, BMSc; Lillian Sung, MD, PhD

Data sourced from clinicaltrials.gov

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