Vomiting Prevention in Children With Cancer

Condition(s) studied

Investigational drug(s) / intervention(s)

ML-based intervention: 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 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.

Eligibility

Primary outcome measure(s)

• Vomiting post prediction time — 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)

Trial sites (1)

More The Hospital for Sick Children trials in Canada

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