Ignite Creation Date:
2025-12-24 @ 11:40 PM
Ignite Modification Date:
2025-12-30 @ 11:39 AM
Study NCT ID:
NCT06886451
Status:
RECRUITING
Last Update Posted:
2025-05-15
First Post:
2025-03-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Vomiting Prevention in Children With Cancer
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D014839', 'term': 'Vomiting'}, {'id': 'D009369', 'term': 'Neoplasms'}], 'ancestors': [{'id': 'D012817', 'term': 'Signs and Symptoms, Digestive'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1332}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-03-18', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-05', 'completionDateStruct': {'date': '2027-03-18', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-05-12', 'studyFirstSubmitDate': '2025-03-13', 'studyFirstSubmitQcDate': '2025-03-13', 'lastUpdatePostDateStruct': {'date': '2025-05-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-03-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-03-18', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Number of SEDAR Predictions and Interventions', 'timeFrame': '0-96 hours post prediction time', 'description': 'The number of times notes in SEDAR 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).'}], 'primaryOutcomes': [{'measure': 'Vomiting post prediction time', 'timeFrame': '0-96 hours post prediction time', 'description': '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)'}], 'secondaryOutcomes': [{'measure': 'Number of episodes of vomiting', 'timeFrame': '0-96 hours post prediction time', 'description': 'The number of episodes of vomiting will be determined by counting the number of distinct time stamps with any vomiting description.'}, {'measure': 'Care pathway-consistent care', 'timeFrame': '0-96 hours post prediction time', 'description': 'Care pathway-consistent care will be measured as a binary outcome (yes/no).'}, {'measure': 'Number of antiemetic administrations', 'timeFrame': '0-96 hours post prediction time', 'description': 'All antiemetic administrations within the 96-hour window will be counted. The total number of administrations overall and by specific antiemetic agent will be tabulated.'}, {'measure': 'Antiemetic costs', 'timeFrame': '0-96 hours post prediction time', 'description': 'Medication costs are available in SEDAR. The total costs of antiemetics per kg overall and by specific antiemetic agent will be tabulated.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Vomiting', 'Machine learning', 'Quality of life', 'Pediatric oncology'], 'conditions': ['Chemotherapy Induced Nausea and Vomiting', 'Quality of Life (QOL)', 'Pediatric Cancer']}, 'referencesModule': {'references': [{'pmid': '37178438', 'type': 'BACKGROUND', 'citation': 'Patel P, Robinson PD, Phillips R, Baggott C, Devine K, Gibson P, Guilcher GMT, Holdsworth MT, Neumann E, Orsey AD, Spinelli D, Thackray J, van de Wetering M, Cabral S, Sung L, Dupuis LL. Treatment of breakthrough and prevention of refractory chemotherapy-induced nausea and vomiting in pediatric cancer patients: Clinical practice guideline update. Pediatr Blood Cancer. 2023 Aug;70(8):e30395. doi: 10.1002/pbc.30395. Epub 2023 May 13.'}]}, 'descriptionModule': {'briefSummary': "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.\n\nThe main questions it aims to answer are whether an ML model predicting the risk of vomiting within the next 96 hours will:\n\nPrimary\n\n1\\. Reduce the proportion with any vomiting within the 96-hour window\n\nSecondary\n\n1. Reduce the number of vomiting episodes\n2. Increase the proportion receiving care pathway-consistent care\n3. Impact on number of administrations and costs of antiemetic medications\n\nNewly 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.", 'detailedDescription': "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.\n\nFor 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, antiemetic administrations and antiemetic costs.\n\nThe study team includes pediatric pharmacists, pediatric oncologists and experts in machine learning, clinical epidemiology, implementation sciences, care pathway development and biostatistics.\n\nVomiting 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* All pediatric patients admitted to the oncology service at SickKids\n\nExclusion Criteria:\n\n* Pediatric patients admitted to the oncology service at SickKids that are discharged prior to prediction time'}, 'identificationModule': {'nctId': 'NCT06886451', 'briefTitle': 'Vomiting Prevention in Children With Cancer', 'organization': {'class': 'OTHER', 'fullName': 'The Hospital for Sick Children'}, 'officialTitle': 'Prevention of Vomiting in Pediatric Oncology Inpatients Using Machine Learning', 'orgStudyIdInfo': {'id': '3373'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'ML model', 'description': 'ML model to predict the risk of vomiting within the next 96 hours.', 'interventionNames': ['Other: ML-based intervention']}], 'interventions': [{'name': 'ML-based intervention', 'type': 'OTHER', 'otherNames': ['Machine learning model'], 'description': '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.', 'armGroupLabels': ['ML model']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'M5G1X8', 'city': 'Toronto', 'state': 'Ontario', 'status': 'RECRUITING', 'country': 'Canada', 'contacts': [{'name': 'Lillian Sung, MD, PhD', 'role': 'CONTACT', 'email': 'lillian.sung@sickkids.ca', 'phone': '416-813-5287'}], 'facility': 'The Hospital for Sick Children', 'geoPoint': {'lat': 43.70643, 'lon': -79.39864}}], 'centralContacts': [{'name': 'Lillian Sung, MD, PhD', 'role': 'CONTACT', 'email': 'lillian.sung@sickkids.ca', 'phone': '416-813-5287'}, {'name': 'Agata Wolochacz, BMSc', 'role': 'CONTACT', 'email': 'agata.wolochacz@sickkids.ca', 'phone': '4166187599'}], 'overallOfficials': [{'name': 'Lillian Sung, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Hospital for Sick Children'}, {'name': 'Lee Dupuis, RPh, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Hospital for Sick Children'}, {'name': 'Priya Patel, PharmD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Hospital for Sick Children'}, {'name': 'Adam Yan, MD, MBI', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Hospital for Sick Children'}, {'name': 'Lawrence Guo, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Hospital for Sick Children'}, {'name': 'Santiago Arciniegas, MSc', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'The Hospital for Sick Children'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The Hospital for Sick Children', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Chief Clinical Data Scientist, Paediatric Oncologist', 'investigatorFullName': 'Lillian Sung', 'investigatorAffiliation': 'The Hospital for Sick Children'}}}}