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Masiero M, Spada GE, Sanchini V, Munzone E, Pietrobon R, Teixeira L, Valencia M, Macchiavelli A, Fragale E, Pezzolato M, Pravettoni G
A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial
“A Machine Learning Model to Predict Patients' Adherence Behavior and Decision Support System in Metastatic Breast Cancer Patients: Protocol for a Randomized Controlled Study”
Marianna Masiero;
Gea Elena Spada;
Virginia Sanchini;
Elisabetta Munzone;
Ricardo Pietrobon;
Lucas Teixeira;
Mirtha Valencia;
Alina Macchiavelli;
Elisa Fragale;
Massimo Pezzolato;
Gabriella Pravettoni
ABSTRACT
Background:
Adherence to oral anticancer treatment is a critical issue in the disease trajectory of breast cancer patients. Given the impact of non-adherence on clinical outcomes and the associated economic burden for the healthcare system, finding effective ways to increase treatment adherence is particularly relevant.
Objective:
The primary endpoint is to evaluate the effectiveness of a decision support system (DSS) in promoting adherence to oral anticancer treatments in a sample of metastatic breast cancer patients. The secondary endpoint is to collect a set of new physical, psychosocial, behavioral, and quality-of-life variables that might be used to refine the preliminary version of a machine-learning model to predict patients' adherence behavior.
Methods:
The current prospective observational, randomized controlled study is nested in a large-scale international project named “Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer - Tracking Number 65080791), aimed to develop a predictive model of non-adherence and associated DSS, and guidelines to foster patients' engagement and therapy adherence among metastatic breast cancer patients. A hundred patients consecutively admitted at the European Institute of Oncology at the Division of Medical Senology will be enrolled. 50 metastatic breast cancer patients will be exposed to the DSS for three months (experimental group) and 50 patients will be not exposed to the intervention (control group). Furthermore, each participant will fill out a weekly medication diary and a set of standardized self-reports at established time points. The study was approved by the European Institute of Oncology (IEO) Ethical Committee (n. R1786/22-IEO 1907).
Results:
The recruitment process will start in May 2023.
Conclusions:
The contribution of the machine learning techniques through introducing the risk-predictive models integrated into the DSS will permit to support medication adherence in cancer patients. Clinical Trial: NA
Citation
Please cite as:
Masiero M, Spada GE, Sanchini V, Munzone E, Pietrobon R, Teixeira L, Valencia M, Macchiavelli A, Fragale E, Pezzolato M, Pravettoni G
A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial