Ignite Creation Date:
2024-05-06 @ 7:52 PM
Last Modification Date:
2024-10-26 @ 3:15 PM
Study NCT ID:
NCT06161181
Status:
COMPLETED
Last Update Posted:
2023-12-19
First Post:
2023-11-30
Brief Title:
Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients
Sponsor:
European Institute of Oncology
Organization:
European Institute of Oncology
Study Overview
Official Title:
Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients the Study Protocol
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 R159521-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 2750 MBC patients from 2010 to 2020
MethodsDesign
Main objectives
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 within a cohort of 100 Metastatic Breast Cancer MBC patients over a three-month period Adherence is assessed by calculating the number of pills taken divided by the prescribed amount
Secondary Objectives
Identify clinical factors comorbidities pain presence tumor type treatment type psychological parameters personality traits anxiety depression self-efficacy for coping with cancer sense of coherence and risk perception and QoL variables that serve as predictors for patients adherence to OATs These predictors are utilized to assess nonadherence to OATs among MBC patients and enhance the initial version of a machine learning model developed in the retrospective study R159521-IEO 1704 Data for the secondary endpoints are collected using the European Organization for Research and Treatment of Cancer Quality of Life questionnaire EORTC-QLQ-C30 the European Organization for Research and Treatment of Cancer 23-item Breast Cancer-specific Questionnaire EORTC-QLQ-BR23 and the Brief Pain Inventory BPI Furthermore to evaluate psychological variables the following measures are used the State-Trait Anxiety Inventory STAI-Y the Beck Depression Inventory-II BDI-II the Big Five Inventory BFI the Cancer Behavior Inventory CBI Short form CBI-BI the Sense of Coherence SOC-13 and Risk Perception utilizing two Visual Analog Scales
Trial Duration and Study Design
The study is designed as a 3-month randomized controlled study conducted at the European Institute of Oncology IEO More specifically a sample of 100 patients is enrolled consecutively and admitted to the Division of Medical Senology with an MBC diagnosis Patients who signed the informed consent are given a unique identifier and assigned to either the control or intervention arm in a 11 ratio Earliest the system asks to confirm all inclusion and exclusion criteria Then an independent researcher generates a random sequence using the statistical language R R Core Team 2020
Experimental Group - TREAT TREatment Adherence SupporT 50 MBC patients receive the DSS for three months Patients are instructed to use the DSS ad libitum Further Patients are explicitly informed that TREAT does not replace clinical consultations but it is designed to assist in managing oral treatment and enhancing adherence through education based on evidence-based information Control Group 50 MBC patients not subjected to the intervention receive standard medical advice
The protocol foresees three assessment time points T1 1-Month T2 2-Month and T3 3-Month At the baseline T0 all patients fill out validated questionnaires to measure adherence clinical psychological and QoL variables The expected time to complete all the given questionnaires at baseline is approximately 40 minutes Furthermore all patients have to fill a weekly adherence medication diary for three months Each month all participants receive a brief telephone interview in which they are monitored for compliance with the research protocol At T1 T2 and T3 all behavioral psychological and QoL measures are filled out and an interview online or vis-à-vis is performed Variables that are not sensitive to change such as personality and anxiety trait are collected only at T0
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 R159521-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 2750 MBC patients from 2010 to 2020
MethodsDesign
Main objectives
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 within a cohort of 100 Metastatic Breast Cancer MBC patients over a three-month period Adherence is assessed by calculating the number of pills taken divided by the prescribed amount
Secondary Objectives
Identify clinical factors comorbidities pain presence tumor type treatment type psychological parameters personality traits anxiety depression self-efficacy for coping with cancer sense of coherence and risk perception and QoL variables that serve as predictors for patients adherence to OATs These predictors are utilized to assess nonadherence to OATs among MBC patients and enhance the initial version of a machine learning model developed in the retrospective study R159521-IEO 1704 Data for the secondary endpoints are collected using the European Organization for Research and Treatment of Cancer Quality of Life questionnaire EORTC-QLQ-C30 the European Organization for Research and Treatment of Cancer 23-item Breast Cancer-specific Questionnaire EORTC-QLQ-BR23 and the Brief Pain Inventory BPI Furthermore to evaluate psychological variables the following measures are used the State-Trait Anxiety Inventory STAI-Y the Beck Depression Inventory-II BDI-II the Big Five Inventory BFI the Cancer Behavior Inventory CBI Short form CBI-BI the Sense of Coherence SOC-13 and Risk Perception utilizing two Visual Analog Scales
Trial Duration and Study Design
The study is designed as a 3-month randomized controlled study conducted at the European Institute of Oncology IEO More specifically a sample of 100 patients is enrolled consecutively and admitted to the Division of Medical Senology with an MBC diagnosis Patients who signed the informed consent are given a unique identifier and assigned to either the control or intervention arm in a 11 ratio Earliest the system asks to confirm all inclusion and exclusion criteria Then an independent researcher generates a random sequence using the statistical language R R Core Team 2020
Experimental Group - TREAT TREatment Adherence SupporT 50 MBC patients receive the DSS for three months Patients are instructed to use the DSS ad libitum Further Patients are explicitly informed that TREAT does not replace clinical consultations but it is designed to assist in managing oral treatment and enhancing adherence through education based on evidence-based information Control Group 50 MBC patients not subjected to the intervention receive standard medical advice
The protocol foresees three assessment time points T1 1-Month T2 2-Month and T3 3-Month At the baseline T0 all patients fill out validated questionnaires to measure adherence clinical psychological and QoL variables The expected time to complete all the given questionnaires at baseline is approximately 40 minutes Furthermore all patients have to fill a weekly adherence medication diary for three months Each month all participants receive a brief telephone interview in which they are monitored for compliance with the research protocol At T1 T2 and T3 all behavioral psychological and QoL measures are filled out and an interview online or vis-à-vis is performed Variables that are not sensitive to change such as personality and anxiety trait are collected only at T0
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None