NCT Number: NCT07546188
This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions.
Interested in participating?
Request Info18 year and older
All sexes
Observational
Charleroi, Hainaut, 6060, Belgium
Location status: Recruiting
Delphine Pranger, MD
PRINCIPAL_INVESTIGATOR
Marie Detrait, MD, PhD
CONTACT
Marie Detrait, MD, PhD
PRINCIPAL_INVESTIGATOR
Marie Detrait, MedSC
CONTACT
Stéphanie De Prophetis, Nurse
SUB_INVESTIGATOR
Non- Hodgkin Lymphomas require rigorous treatment protocols, including intensive intravenous chemotherapy or targeted oral therapies. Secondary immunosuppression necessitates oral anti-infective prophylaxis (such as valacyclovir or Bactrim forte) to prevent opportunistic complications. However, the literature reports figures of up to 50% of patients experiencing adherence difficulties on oral therapies, compromising treatment efficacy, increasing the risk of severe infections, prolonged hospitalizations, and consequently, additional costs for the healthcare system. This project proposes to develop an innovative artificial intelligence (AI) tool, based on real-world data, to detect early signs of non-adherence and enable targeted intervention by healthcare teams. Our approach combines analysis of clinical data (patient, disease, dispensing history, laboratory results, drug interactions) and machine learning algorithms (supervised machine learning and neural networks) to identify at-risk profiles. The tool will generate a real-time alert and offer the patient's referring physician and coordinating nurse tailored recommendations, such as an automated reminder, a dedicated nursing consultation, etc. An intuitive interface will allow clinicians and nurses to visualize compliance trends and act quickly. This project relies on a multidisciplinary team (hematologists, advanced practice nurses (APNs), data scientists, AI experts) and patient partners to validate the tool in real-world conditions.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
For the retrospective group of 20 patients.
Follow-up of the patients for the prospective group
Time frame: 2027
Description: ROC-AUC : Receiver Operating Characteristic - Area Under the Curve is a performance metric for binary classification prediction algorithms. ROC Curve: Plots the True Positive Rate (sensitivity) against the False Positive Rate (1-specificity) at various classification thresholds. AUC: The area under this curve (ranging from 0 to 1). A higher AUC indicates better model performance-1.0 is perfect, 0.5 is random guessing. ROC-AUC evaluates how well the model distinguishes between classes, regardless of the classification threshold.
Time Frame: When the data will be avalaible, at the end of 2027
Time frame: When the data will be avalaible, at the end of 2027
F1-Score is a performance metric for classification algorithms, the harmonic mean of Precision (correct positive predictions / total positive predictions) and Recall (correct positive predictions / actual positives).
Formula: F1 = 2 × (Precision × Recall) / (Precision + Recall) Range: 0 to 1, where 1 is perfect precision and recall, and 0 is the worst.
F1-Score balances precision and recall, making it ideal when you need to avoid both false positives and false negatives.
Time frame: 2027
Recall for the positive Class is a metric for binary classification that answers:
"What proportion of actual positives was correctly identified by the model?"
Formula: Recall = True Positives / (True Positives + False Negatives)
Range: 0 to 1, where 1 means all positives were correctly predicted, and 0 means none were.
High recall means the model is good at capturing most positive cases, but it may also include more false positives. It's critical when missing a positive (false negative) is costly.
Contact information is provided by the study sponsor or research team.
Aline Gillain, MedSciences
CONTACT
Marie Detrait, MD, PhD
CONTACT
Grand Hôpital de Charleroi
Other
AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients, Integrating the Complete Care Pathway and an Interoperable Clinical Interface With Algorithms Paired With Explainability Tools.
Acronym: LNH-AI-Tools
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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