Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients: the Study Protocol
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Enrollment
- 94
- Locations
- 2
- Primary Endpoint
- Decision Support System Effectiveness
Study Overview
Brief Summary
Background: Emerging evidence indicates that patients with advanced cancer, such as those with MBC, often exhibit significant levels of nonadherence to oral anticancer treatments. Leveraging of the machine learning models in clinical practice enables the provision of personalized predictions on medication adherence for individual patients, thereby supporting adherence and facilitating targeted interventions.
Objective: The current protocol aims to assess the efficacy of the DSS, a web-based solution named TREAT (TREatment Adherence SupporT), and a machine learning web application in promoting adherence to oral anticancer treatments within a sample of MBC patients.
Methods and Design: This protocol is part of a project titled "Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients" (Tracking Number 65080791). A sample of 100 MBC patients is enrolled consecutively and admitted to the Division of Medical Senology of the European Institute of Oncology. 50 MBC patients receive the DSS for three months (experimental group), while 50 MBC patients not subjected to the intervention receive standard medical advice (control group). The protocol foresees three assessment time points: T1 (1-Month), T2 (2-Month), and T3 (3-Month). At each time point, participants fill out a set of self-reports evaluating adherence, clinical, psychological, and QoL variables.
Conclusions: our results will inform about the effectiveness of the DSS and risk-predictive models in fostering adherence to oral anticancer treatments in MBC patients.
Detailed Description
Metastatic breast cancer (MBC) represents an incurable condition wherein pharmacological interventions are directed towards deferring disease progression and alleviating symptoms, thereby extending survival rates and preserving the quality of life (QoL) and psychological well-being. Clinical advancements in anticancer treatments have notably augmented survival rates among MBC patients. However, accruing evidence reported that adherence to medications is a critical issue in the disease trajectory of breast cancer patients, particularly in the context of oral anticancer treatments (OATs). Emerging evidence indicates that patients with advanced cancer, such as those with MBC, often exhibit significant levels of nonadherence. MBC patients encounter various barriers to the daily management of OATs, including emotional and physical distress associated with side effects, dosage variations, treatment interruptions, and a lack of disease-related knowledge. Prediction models for adherence have been previously developed and tested across diverse scenarios and diseases. Evidence suggested that leveraging of the machine learning models in clinical practice enables the provision of personalized predictions on medication adherence for individual patients, thereby supporting adherence and facilitating targeted interventions. Even so, existing studies have yet to systematically address medication adherence among MBC patients by designing and implementing a decision support system (DSS) that integrates risk predictive models alongside educational and training tools.
The current protocol aims to assess the efficacy of the DSS, a web-based solution named TREAT (TREatment Adherence SupporT), and a machine learning web application in promoting adherence to oral anticancer treatments within a sample of MBC patients. This protocol is part of a project titled "Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients" (Tracking Number 65080791). The overarching goal of this project is to develop a predictive model of nonadherence, an associated DSS, and guidelines to enhance patient engagement and therapy adherence among MBC patients.
The web-based DSS was developed in the first year of the Pfizer Project (65080791) using a patient-centric approach and comprises four sections: i) Metastatic Breast Cancer; ii) Adherence to Cancer Therapies; iii) Promoting Adherence; iv) My Adherence Diary. Moreover, a machine learning web-based application was designed to focus on predicting patients' risk factors for adherence to anticancer treatment, specifically considering physical status, comorbid conditions, and short- and long-term side effects. This machine learning web-based application was developed through a retrospective study employing physiological, clinical, and quality of life data available in the European Institute of Oncology (Milan, Italy) (R1595/21-IEO 1704). Specifically, multi-modal retrospective data has been retrieved from the Patient Electronic Health Records (EHR) using natural language processing (NLP) in a sample of 2.750 MBC patients (from 2010 to 2020).
Methods/Design
Main objectives
Study Design
- Study Type
- Interventional
- Allocation
- Randomized
- Intervention Model
- Parallel
- Primary Purpose
- Other
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- Female
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Patients > 18 years-old;
- •Having a metastatic breast cancer diagnosis;
- •Taking oral treatment intervention for metastatic breast cancer;
- •Patients with internet access and a personal smartphone or tablet;
- •Patients who have read and signed the informed consent.
Exclusion Criteria
- •Presence of primary psychiatric or neurological conditions;
- •Patients who refused to sign the informed consent.
Outcomes
Primary Outcomes
Decision Support System Effectiveness
Time Frame: 3 Months
Evaluating the effectiveness of the DSS web-based solution and machine learning web application (TREAT - "TREatment Adherence SupporT") in fostering adherence to oral anticancer treatments
Secondary Outcomes
- Clinical, Psychological and Quality of Life Predictors of Adherence(3 Months)
- Psychological Predictors of Adherence(3 Months)