ML-Based Intervention for Pediatrics

Overseen By Agata Wolochacz, BMSc

Age: < 65

Sex: Any

Trial Phase: Academic

Sponsor: The Hospital for Sick Children

Disqualifiers: Prior pharmacogenetic testing, ICU admission, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

275 Participants Needed

Trial Summary

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It focuses on predicting and providing pharmacogenetic testing for future medication needs.

What data supports the effectiveness of the ML-based intervention treatment for pediatrics?

Research shows that machine learning (ML) can improve predictions in pediatric care, such as better predicting mortality in intensive care and outcomes in chronic respiratory diseases. This suggests that ML-based interventions could enhance treatment decisions and patient care in children. ^{1 2 3 4 5}

Is the ML-based intervention generally safe for children?

The safety of medical treatments in children is closely monitored, with adverse events (unwanted effects) being a significant concern. Reports to the FDA show that adverse events in children are tracked, and while there are differences in drug reactions among age groups, the data helps in understanding and improving safety measures for pediatric treatments. ^{6 7 8 9 10}

How does the ML-based intervention for pediatric asthma differ from other treatments?

The ML-based intervention for pediatric asthma is unique because it uses machine learning (a type of computer algorithm) to analyze healthcare data, helping to identify different types of asthma and predict its progression, which can lead to more personalized and timely treatments compared to traditional methods. ^{1 4 11 12 13}

What is the purpose of this trial?

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.

Research Team

Lillian Sung, MD, PhD

Principal Investigator

The Hospital for Sick Children

Eligibility Criteria

This trial is for pediatric patients who are at high risk of needing a medication that could be influenced by their genetic makeup within the next three months. Specific eligibility criteria have not been provided.

Inclusion Criteria

Inpatient at The Hospital for Sick Children

Exclusion Criteria

Expected hospital discharge is prior to midnight on the day of admission

I have had genetic testing or received targeted medication before.

I am currently admitted to the ICU.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1 day

1 visit (in-person)

Prediction and Notification

Machine learning model predicts patients at high risk of requiring a targeted medication within the next three months. The research team is notified of eligible patients each morning.

3 months

Pharmacogenetic Testing

Pharmacogenetic testing is offered and conducted for participants predicted to receive a targeted medication.

Up to 3 months

Follow-up

Participants are monitored for changes in medication choice or dose based on pharmacogenetic results.

3 months

Treatment Details

Interventions

• ML-based intervention

Trial Overview The study is testing whether a machine learning model can improve drug therapy by predicting which children will need pharmacogenetic testing before they receive certain medications, and if this leads to changes in medication choice or dosage.

Participant Groups

Group I: ML model Experimental Treatment 1 Intervention

Participants predicted by an ML model to receive a "targeted medication" within three months following admission.

Who Is Running the Clinical Trial?

The Hospital for Sick Children

Lead Sponsor

Trials

724

Recruited

6,969,000+

Findings from Research

A study of 106,122 individual case safety reports (ICSRs) for pediatric patients (0-18 years) submitted to the FDA from 2004 to 2011 revealed that 'nervous system drugs' were the most commonly reported, highlighting the need for focused safety monitoring in this category.

The analysis showed significant differences in reported drug classes and adverse events across different age groups, suggesting that drug safety evaluations for children should be tailored to specific age categories to improve signal detection and safety assessments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Pediatric Drug Safety Surveillance in FDA-AERS: A Description of Adverse Events from GRiP Project. de Bie, S., Ferrajolo, C., Straus, SM., et al.[2018]

In a study analyzing pediatric hospitalizations, 24 patients experienced 29 adverse events (AEs) related to medical care, with a rate of 3.61% across all pediatric hospitalizations, which is lower than the 6.4% rate seen in non-elderly adults.

A significant 65.5% of these adverse events were deemed preventable, highlighting the need for targeted prevention strategies, especially since medication-related events accounted for 37.9% of the incidents.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Clinical safety paediatric patients]. Requena, J., Miralles, JJ., Mollar, J., et al.[2012]

A simulation training program significantly improved the self-efficacy of medical students and interns in identifying and reporting patient safety hazards, indicating its effectiveness as a training tool.

Participants were particularly good at recognizing patient misidentification hazards, but often missed safety issues related to electronic health records (EHRs), highlighting a need for targeted interventions in that area.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

"Good Catch, Kiddo"-Enhancing Patient Safety in the Pediatric Emergency Department Through Simulation. Shaikh, U., Natale, JE., Till, DA., et al.[2023]

In a systematic review of 17 studies involving children, the incidence of adverse drug reactions (ADRs) in hospitalized children was found to be 9.53%, with severe reactions accounting for 12.29%, highlighting the significant risk of ADRs in this population.

The study revealed that 2.09% of pediatric hospital admissions were due to ADRs, with 39.3% of these being life-threatening, indicating a critical need for improved drug safety monitoring and preventive strategies in pediatric healthcare.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Incidence of adverse drug reactions in paediatric in/out-patients: a systematic review and meta-analysis of prospective studies. Impicciatore, P., Choonara, I., Clarkson, A., et al.[2023]

References

1. England pubmed.ncbi.nlm.nih.gov

Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy. [2019]

2. United States pubmed.ncbi.nlm.nih.gov

A Machine Learning Classifier Improves Mortality Prediction Compared With Pediatric Logistic Organ Dysfunction-2 Score: Model Development and Validation. [2022]

3. United States pubmed.ncbi.nlm.nih.gov

State of the art in clinical decision support applications in pediatric perioperative medicine. [2021]

4. England pubmed.ncbi.nlm.nih.gov

Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Pediatric Drug Safety Surveillance in FDA-AERS: A Description of Adverse Events from GRiP Project. [2018]

7. United Arab Emirates pubmed.ncbi.nlm.nih.gov

Adverse Drug Events in Children: How Big is the Problem? [2019]

8. Spain pubmed.ncbi.nlm.nih.gov

[Clinical safety paediatric patients]. [2012]

9. United States pubmed.ncbi.nlm.nih.gov

"Good Catch, Kiddo"-Enhancing Patient Safety in the Pediatric Emergency Department Through Simulation. [2023]

10. England pubmed.ncbi.nlm.nih.gov

Incidence of adverse drug reactions in paediatric in/out-patients: a systematic review and meta-analysis of prospective studies. [2023]

11. England pubmed.ncbi.nlm.nih.gov

Machine learning: A modern approach to pediatric asthma. [2022]

12. United States pubmed.ncbi.nlm.nih.gov

Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review. [2022]

13. China pubmed.ncbi.nlm.nih.gov

Application of machine learning to predict the outcome of pediatric traumatic brain injury. [2021]