Clinical trial NCT06161181: Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients
Official title: Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients: the Study Protocol
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Metastatic Breast Cancer
Keywords: adherence
oral anticancer treatment
risk predictive model
decision support system
Lead sponsor: European Institute of Oncology
Sponsor class: OTHER
Collaborators: name: Pfizer
First posted: 2023-12-07
Enrollment: 94
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: OTHER

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

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 (R1595/21-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-B/I), 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 1:1 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Decision Support System Effectiveness
timeFrame: 3 Months
Secondary outcomes:
measure: Clinical, Psychological and Quality of Life Predictors of Adherence
timeFrame: 3 Months
measure: Psychological Predictors of Adherence
timeFrame: 3 Months
Other outcomes:
Locations:
city: Milan
country: Italy
facility: European Institute fo Oncology
state: MI
Study officials:
affiliation: Istituto Europeo di Oncologia
name: Gabriella pravettoni, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, Filiberti A, Flechtner H, Fleishman SB, de Haes JC, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993 Mar 3;85(5):365-76. doi: 10.1093/jnci/85.5.365.
citation: Antonovsky A. The structure and properties of the sense of coherence scale. Soc Sci Med. 1993 Mar;36(6):725-33. doi: 10.1016/0277-9536(93)90033-z.
citation: Bohlmann A, Mostafa J, Kumar M. Machine Learning and Medication Adherence: Scoping Review. JMIRx Med. 2021 Nov 24;2(4):e26993. doi: 10.2196/26993.
citation: Cardoso F, Paluch-Shimon S, Senkus E, Curigliano G, Aapro MS, Andre F, Barrios CH, Bergh J, Bhattacharyya GS, Biganzoli L, Boyle F, Cardoso MJ, Carey LA, Cortes J, El Saghir NS, Elzayat M, Eniu A, Fallowfield L, Francis PA, Gelmon K, Gligorov J, Haidinger R, Harbeck N, Hu X, Kaufman B, Kaur R, Kiely BE, Kim SB, Lin NU, Mertz SA, Neciosup S, Offersen BV, Ohno S, Pagani O, Prat A, Penault-Llorca F, Rugo HS, Sledge GW, Thomssen C, Vorobiof DA, Wiseman T, Xu B, Norton L, Costa A, Winer EP. 5th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 5). Ann Oncol. 2020 Dec;31(12):1623-1649. doi: 10.1016/j.annonc.2020.09.010. Epub 2020 Sep 23. No abstract available.
citation: Cleeland CS, Ryan KM. Pain assessment: global use of the Brief Pain Inventory. Ann Acad Med Singap. 1994 Mar;23(2):129-38.
citation: Gennari A, Andre F, Barrios CH, Cortes J, de Azambuja E, DeMichele A, Dent R, Fenlon D, Gligorov J, Hurvitz SA, Im SA, Krug D, Kunz WG, Loi S, Penault-Llorca F, Ricke J, Robson M, Rugo HS, Saura C, Schmid P, Singer CF, Spanic T, Tolaney SM, Turner NC, Curigliano G, Loibl S, Paluch-Shimon S, Harbeck N; ESMO Guidelines Committee. Electronic address: clinicalguidelines@esmo.org. ESMO Clinical Practice Guideline for the diagnosis, staging and treatment of patients with metastatic breast cancer. Ann Oncol. 2021 Dec;32(12):1475-1495. doi: 10.1016/j.annonc.2021.09.019. Epub 2021 Oct 19. No abstract available.
citation: Jansen LA, Appelbaum PS, Klein WM, Weinstein ND, Cook W, Fogel JS, Sulmasy DP. Unrealistic optimism in early-phase oncology trials. IRB. 2011 Jan-Feb;33(1):1-8. No abstract available.
citation: Karanasiou GS, Tripoliti EE, Papadopoulos TG, Kalatzis FG, Goletsis Y, Naka KK, Bechlioulis A, Errachid A, Fotiadis DI. Predicting adherence of patients with HF through machine learning techniques. Healthc Technol Lett. 2016 Sep 27;3(3):165-170. doi: 10.1049/htl.2016.0041. eCollection 2016 Sep.
citation: Komatsu H, Yagasaki K, Yamaguchi T, Mori A, Kawano H, Minamoto N, Honma O, Tamura K. Effects of a nurse-led medication self-management programme in women with oral treatments for metastatic breast cancer: A mixed-method randomised controlled trial. Eur J Oncol Nurs. 2020 Aug;47:101780. doi: 10.1016/j.ejon.2020.101780. Epub 2020 Jun 14.
citation: Lin C, Clark R, Tu P, Bosworth HB, Zullig LL. Breast cancer oral anti-cancer medication adherence: a systematic review of psychosocial motivators and barriers. Breast Cancer Res Treat. 2017 Sep;165(2):247-260. doi: 10.1007/s10549-017-4317-2. Epub 2017 Jun 1.
citation: Marshall VK, Visovsky C, Advani P, Mussallem D, Tofthagen C. Cancer treatment-specific medication beliefs among metastatic breast cancer patients: a qualitative study. Support Care Cancer. 2022 Aug;30(8):6807-6815. doi: 10.1007/s00520-022-07101-7. Epub 2022 May 9.
citation: Merluzzi TV, Nairn RC, Hegde K, Martinez Sanchez MA, Dunn L. Self-efficacy for coping with cancer: revision of the Cancer Behavior Inventory (version 2.0). Psychooncology. 2001 May-Jun;10(3):206-17. doi: 10.1002/pon.511.
citation: Mirzadeh SI, Arefeen A, Ardo J, Fallahzadeh R, Minor B, Lee JA, Hildebrand JA, Cook D, Ghasemzadeh H, Evangelista LS. Use of machine learning to predict medication adherence in individuals at risk for atherosclerotic cardiovascular disease. Smart Health (Amst). 2022 Dec;26:100328. doi: 10.1016/j.smhl.2022.100328. Epub 2022 Oct 4.
citation: Montagna E, Zagami P, Masiero M, Mazzocco K, Pravettoni G, Munzone E. Assessing Predictors of Tamoxifen Nonadherence in Patients with Early Breast Cancer. Patient Prefer Adherence. 2021 Sep 15;15:2051-2061. doi: 10.2147/PPA.S285768. eCollection 2021.
citation: Yerrapragada G, Siadimas A, Babaeian A, Sharma V, O'Neill TJ. Machine Learning to Predict Tamoxifen Nonadherence Among US Commercially Insured Patients With Metastatic Breast Cancer. JCO Clin Cancer Inform. 2021 Aug;5:814-825. doi: 10.1200/CCI.20.00102.
citation: Scioscia G, Tondo P, Foschino Barbaro MP, Sabato R, Gallo C, Maci F, Lacedonia D. Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in obstructive sleep apnea (OSA). Inform Health Soc Care. 2022 Jul 3;47(3):274-282. doi: 10.1080/17538157.2021.1990300. Epub 2021 Nov 8.
citation: Zhu X, Peng B, Yi Q, Liu J, Yan J. Prediction Model of Immunosuppressive Medication Non-adherence for Renal Transplant Patients Based on Machine Learning Technology. Front Med (Lausanne). 2022 Feb 18;9:796424. doi: 10.3389/fmed.2022.796424. eCollection 2022.
citation: Scott NW, Fayers P, Aaronson NK, et al. EORTC QLQ-C30 Reference Values Manual. (2nd ed.). EORTC Quality of Life Group., 2008
citation: Pedrabissi, L., & Santinello, M. (1989). Verifica della validità dello STAI forma Y di Spielberger [Verification of the validity of the STAI, Form Y, by Spielberger]. Giunti Organizzazioni Speciali, 191-192, 11-14.
citation: Beck AT, Steer RA, Brown G. Beck Depression Inventory-II (BDI-II). APA PsycTests. Epub ahead of print 1996
citation: Sica C, Ghisi M. The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In: M. A. Lange. Leading-edge psychological tests and testing research. Nova Science Publishers, 2007, pp. 27-50.
citation: Serpentini S, Del Bianco P, Chirico A, Merluzzi TV, Martino R, Lucidi F, De Salvo GL, Trentin L, Capovilla E. Self-efficacy for coping: utility of the Cancer behavior inventory (Italian) for use in palliative care. BMC Palliat Care. 2019 Apr 5;18(1):34. doi: 10.1186/s12904-019-0420-y.
citation: Spielberger CD, Gonzalez-Reigosa F, Martinez-Urrutia A, et al. The State-Trait Anxiety Inventory. Rev Interam Psicol J Psychol 1971; 5: 3-4
citation: Sprangers MA, Groenvold M, Arraras JI, Franklin J, te Velde A, Muller M, Franzini L, Williams A, de Haes HC, Hopwood P, Cull A, Aaronson NK. The European Organization for Research and Treatment of Cancer breast cancer-specific quality-of-life questionnaire module: first results from a three-country field study. J Clin Oncol. 1996 Oct;14(10):2756-68. doi: 10.1200/JCO.1996.14.10.2756.
citation: Ubbiali A, Chiorri C, Hampton P, Donati D. Italian Big Five Inventory. Psychometric properties of the Italian adaptation of the Big Five Inventory (BFI). Bollettino di Psicologia applicata 2013;59(266):37-48
citation: Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39(5), 806-820.
citation: R: A language and environment for statistical computing. R Foundation for Statistical Computing. URL: https://www. R-project.org