Zapobieganie wymiotom u dzieci z rakiem
Zapobieganie wymiotom w pacjentach szpitalnych z uczeniem maszynowym za pomocą uczenia maszynowego
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
- 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.
Przegląd badań
Status
Interwencja / Leczenie
Szczegółowy opis
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.
Typ studiów
Zapisy (Rzeczywisty)
Faza
- Nie dotyczy
Kontakty i lokalizacje
Lokalizacje studiów
-
-
Ontario
-
Toronto, Ontario, Kanada, M5G1X8
- The Hospital for Sick Children
-
-
Kryteria uczestnictwa
Kryteria kwalifikacji
Wiek uprawniający do nauki
- Dziecko
- Dorosły
- Starszy dorosły
Akceptuje zdrowych ochotników
Płeć kwalifikująca się do nauki
Opis
Inclusion Criteria:
- All pediatric patients admitted to the oncology service at SickKids
Exclusion Criteria:
- Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time
Plan studiów
Jak projektuje się badanie?
Szczegóły projektu
- Główny cel: Leczenie podtrzymujące
- Przydział: Nie dotyczy
- Model interwencyjny: Zadanie dla jednej grupy
- Maskowanie: Brak (otwarta etykieta)
Broń i interwencje
Grupa uczestników / Arm |
Interwencja / Leczenie |
|---|---|
|
Eksperymentalny: ML model
ML model to predict the risk of vomiting within the next 96 hours. |
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.
Inne nazwy:
|
Co mierzy badanie?
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
|
Vomiting post prediction time
Ramy czasowe: 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) |
0-96 hours post prediction time
|
Miary wyników drugorzędnych
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
|
Number of episodes of vomiting
Ramy czasowe: 0-96 hours post prediction time
|
The number of episodes of vomiting will be determined by counting the number of distinct time stamps with any vomiting description. |
0-96 hours post prediction time
|
|
Care pathway-consistent care
Ramy czasowe: 0-96 hours post prediction time
|
Care pathway-consistent care will be measured as a binary outcome (yes/no). |
0-96 hours post prediction time
|
|
Specific antiemetic administrations
Ramy czasowe: 0-96 hours post prediction time
|
Specific antiemetic administrations within the 96-hour window will be counted. |
0-96 hours post prediction time
|
Inne miary wyników
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
|
Number of Predictions and Interventions
Ramy czasowe: 0-96 hours post prediction time
|
The number of times notes refer to the predictions and interventions to address them including antiemetic modifications during the prediction window will be descriptively reported. The role of the note writer will be documented (such as pharmacist or fellow). |
0-96 hours post prediction time
|
|
Number of times the prediction report dashboard was opened when a prediction was made.
Ramy czasowe: 0-96 hour prediction window
|
The investigators will also describe the number of times the prediction report dashboard was opened when a prediction was made. |
0-96 hour prediction window
|
|
Nausea
Ramy czasowe: 0-96 hour prediction window
|
Potential nausea events will be identified through automated retrieval of clinical notes containing nausea-related terms. Blinded research staff will subsequently review the notes and adjudicate whether nausea was present. |
0-96 hour prediction window
|
Współpracownicy i badacze
Sponsor
Śledczy
- Główny śledczy: Lillian Sung, MD, PhD, The Hospital for Sick Children
- Główny śledczy: Lee Dupuis, RPh, PhD, The Hospital for Sick Children
- Główny śledczy: Priya Patel, PharmD, The Hospital for Sick Children
- Główny śledczy: Adam Yan, MD, MBI, The Hospital for Sick Children
- Główny śledczy: Lawrence Guo, PhD, The Hospital for Sick Children
Publikacje i pomocne linki
Daty zapisu na studia
Główne daty studiów
Rozpoczęcie studiów (Rzeczywisty)
Zakończenie podstawowe (Rzeczywisty)
Ukończenie studiów (Szacowany)
Daty rejestracji na studia
Pierwszy przesłany
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy wysłany (Rzeczywisty)
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Rzeczywisty)
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia weryfikacja
Więcej informacji
Terminy związane z tym badaniem
Słowa kluczowe
Dodatkowe istotne warunki MeSH
Inne numery identyfikacyjne badania
- 3373
Plan dla danych uczestnika indywidualnego (IPD)
Planujesz udostępniać dane poszczególnych uczestników (IPD)?
Informacje o lekach i urządzeniach, dokumenty badawcze
Bada produkt leczniczy regulowany przez amerykańską FDA
Bada produkt urządzenia regulowany przez amerykańską FDA
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