Wounds and Injuries

All Conditions

Neoplasms

Symptoms and General Pathology

Fractures, Bone

Neoplasm Metastasis

Neoplastic Processes

Patients affected by spinal metastases that are not surgically treated

Extraction of clinical and radiographic data related to patients' history from medical records

Case series review of clinical and radiographic data

Giovanni Barbanti Brodano, MD

Cristiana Griffoni, PhD

Peter Vajkoczy

Aron Lazary

Jorrit-Jan Verlaan

Train-METASTRA is a retrospective study that will be performed in order to collect a large and harmonised amount of clinical and imaging data concerning vertebral metastases, focusing in particular on the risk of fractures. This type of dataset will be created from the medical records of 2000 patients admitted in the last ten years in the four European clinical centers participating in METASTRA project: "COMPUTER-AIDED EFFECTIVE FRACTURE RISK STRATIFICATION OF PATIENTS WITH VERTEBRAL METASTASES FOR PERSONALISED TREATMENT THROUGH ROBUST COMPUTATIONAL MODELS VALIDATED IN CLINICAL SETTINGS", funded by the European Union under the call "HORIZON-HLTH-2022-TOOL-12-two-stage/Computational models for new patient stratification strategies". The project is coordinated by the University of Bologna (UNIBO) (PI prof. Luca Cristofolini) and involves 15 European partners, including Sarl Voisin Consulting Life Sciences VCLS, University of Szeged (Hungary), University of Sheffield (UK) and FrontEndART (Hungary).

This type of dataset is not currently available in the literature and it will be pivotal to the development of the METASTRA computational models for the stratification of the risk of fracture of patients affected by spinal metastases.

Multicentric European Study In Patients With Vertebral Metastases

Retrospective Multicentric European Study In Patients With Vertebral Metastases (Train-Metastra)

The primary objective of the study is to identify the main relevant characteristics to be used for stratifying the patients with vertebral metastases based on their risk of fracture.

The primary objective of the study is to identify the main relevant demographic, clinical and radiographic characteristics to be used for stratifying the patients with vertebral metastases based on their risk of fracture.

To train the data-driven computational models (METASTRA-AI) developed within the project

To inform the physiology-based biomechanical models (METASTRA-VPH) developed within the project

Istituto Ortopedico Rizzoli

Skin and Connective Tissue Diseases

Breast Neoplasms

Breast Diseases

Skin Diseases

50 MBC patients receive the DSS for three months. Patients are instructed to use the DSS ad libitum.

50 MBC patients not subjected to the intervention receive standard medical advice.

TREAT (TREatment Adherence SupporT) is a web-based DSS that comprises four sections:

i) Metastatic Breast Cancer: contains information about MBC and its physical and psychological consequences;

ii) Adherence to Cancer Therapies: contains information about adherence in the cancer population;

iii) Promoting Adherence: contains information about resources, barriers, and available interventions used to foster adherence;

iv) My Adherence Diary.

Decision Support System

Marianna Masiero, PhD

Marianna Masiero, Phd

Gabriella pravettoni, PhD

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.

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.

Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients

Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients: the Study Protocol

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

Identify clinical factors (comorbidities, pain presence, tumor type, treatment type), psychological parameters (personality traits, anxiety, depression, self-efficacy for coping with cancer and sense of coherence), and QoL variables that serve as predictors for patients' adherence to OATs.

Evaluate risk perception using visual analogue scale that serve as predictors for patients' adherence to OATs.

Pfizer

European Institute of Oncology

Blood and Lymph Conditions

Immune System Diseases

Behaviors and Mental Disorders

Rare Diseases

Lymphoma

Lymphoma, Non-Hodgkin

Neoplasms by Histologic Type

Lymphoproliferative Disorders

Lymphatic Diseases

Immunoproliferative Disorders

Quality of Life

Lymphosarcoma

Symptom frequency and severity level of NHL patients will be evaluated with an online web-based application. NHL patients, who will be applying symptom self-management with an online decision support system, will be expected to additionally manage their symptom self-management using with this system. Participants in the intervention group will be asked to evaluate the frequency and severity of symptoms through the online symptom evaluation system on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy courses, and to use the decision support system for symptom self-management according to symptom severity. During the follow-up period, a short SMS message will be sent to the patients on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy cycles. In the third month, after the follow-up phase of the study is completed, the patients in the intervention group who come to the hospital for follow-up or treatment will receive post-tests.

Symptom frequency and severity level of NHL patients will be evaluated with an online web-based application. NHL patients, who will be treated with the symptom self-management with education booklet. Participants will be asked to evaluate the frequency and severity of symptoms through the online symptom evaluation system on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy courses for three months. They will be able to do symptom self-management within the scope of the patient education booklet sent to them via SMS. During the follow-up period, a short SMS message will be sent to the patients on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy cycles. In the third month, after the follow-up phase of the study is completed, the patients in the intervention group who come to the hospital for follow-up or treatment will receive post-tests.

The online decision support system will appear with mild, moderate, or high severity according to symptom severity. If the patient's symptom is of mild category severity, a green area will appear on the screen and patients will see information on self-management strategies in this area. If patient's symptom is of moderate category severity, a yellow area will appear on the screen and patients will see information on self-management strategies and symptom monitoring at home in this area. In moderate symptom severity, when a yellow area occurs, patients should be asked, "Is your fever above 38℃?" according to their needs. Information about the treatment steps to be followed will be given. If the patient's symptom is in a severe category, a red area will appear on the screen and the patients will be warned to apply to the healthcare institution within this area.

The effectiveness of the online decision support system developed for symptom self-management

Symptom frequency and severity level of NHL patients will be evaluated with an online web-based application. NHL patients, who will be treated with the symptom self-management with education booklet. Participants will be asked to evaluate the frequency and severity of symptoms through the online symptom evaluation system on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy courses for three months. They will be able to do symptom self-management within the scope of the patient education booklet sent to them via SMS.

Symptom self-management with an education booklet

Merve Gozde Sezgin, MSc, RN

Hicran Bektas, Ph.D., RN

This study aims to evaluate the effect of the decision support system developed for symptom self-management on symptom management, quality of life, and unplanned hospital admissions in Non-Hodgkin lymphoma (NHL) patients. Since NHL patients often experience disease and treatment-related side effects after discharge from the hospital, it would be beneficial to develop web-based decision support systems that can support symptom management at home. A mobile-compatible symptom self-management decision support system will be developed and tested with five patients, based on the needs of NHL patients, evidence-based guidelines, and expert opinions. A randomized controlled trial design with a single-blind and active control group will be applied. NHL patients will be pretested and randomized (intervention: 26, control: 26). The intervention group will use the decision support system developed for symptom self-management for three months. The researchers will share their phone numbers with the patients and be contacted via the 24/7 contact button or the phone. The effectiveness of the decision support system developed for symptom self-management is planned to be evaluated at the beginning and after 12 weeks.

Non-Hodgkin lymphoma (NHL) accounts for approximately 41% of the entire hematological cancer population. Improving symptom self-management of patients in NHL treatment and care is among the important goals. Symptom self-management in NHL patients is considered an important factor in the early detection and control of symptoms. Although treatment and care have an important role in the concept of symptom self-management in NHL patients, it is reported that the symptom self-management levels of the patients are generally low. Since NHL patients often experience disease and treatment-related side effects after discharge from the hospital, it seems necessary to develop web-based decision support systems that can support symptom management at home. Decision support system applications developed for symptom self-management; help patients manage their symptoms, coordinate treatment plans, receive comprehensive care, update their knowledge and encourage continuous learning. The study aims to evaluate the effect of the decision support system developed for symptom self-management in NHL patients on symptom management, quality of life, and unplanned hospital admissions.

In the study, a web-based decision support system for symptom self-management in NHL patients will be developed. The website content and decision support system will be prepared in line with current literature, national and international websites, and guidelines, evaluated with expert opinions and readability index, and will be finalized by testing with preliminary application. The study was designed as a randomized study with a single-blind and active control group. This study will be carried out with an NHL patient who is followed up and treated in the Hematology Outpatient Clinic and Daytime Chemotherapy Unit and meets the inclusion criteria of the sample. It will be prepared in line with the Consolidated Standard of Reporting Trials - CONSORT 2018 guidelines.

In the study, the sample size was calculated based on 80% power and 5% confidence interval on the G\*POWER software package. The study will be conducted with cancer patients (n=52), 26 in the intervention group and 26 in the control group. Patients with NHL diagnosis (Diffuse Large B-Cell Lymphoma and Follicular Lymphoma), over 18 years of age, who received at least two cycles of chemotherapy, had an ECOG performance score of 0-1-2, were treated with chemotherapy for the first time due to NHL diagnosis and had no verbal communication disorder. Literate patients, have internet access, and use the internet will be included.

As a randomization method, the "block randomization method" will be chosen so that the participants in the intervention and control groups have similar characteristics. An independent researcher who was not involved in the research will be assigned to the intervention and control groups with the mobile application program. Randomization will be done in a computer environment using the http://www.r-bloggers.com/example-2014-2-block-/randomization/ website and put in opaque envelopes. After obtaining informed consent from patients who meet the inclusion criteria, pre-tests will be performed by the researcher. After the pre-tests are applied, the participants will be assigned to the groups with opaque envelopes prepared by an independent researcher. Thus, assignment bias will be prevented in the randomization process. As the participants do not know the hypothesis of the research and in which group they are, it will be ensured that the participants are blinded. For the patients in the control group not to understand the application to the intervention group, the link of the website prepared for the intervention group, which contains only information on the evaluation of symptom frequency and severity, will be opened to the patients in the control group with a password. Algorithms in the decision support system intervention protocol developed for symptom self-management to be tested in the intervention group will not be included in the website shared with the control group. Blinding the patients will be ensured by having a website that will be applied to both the intervention and control groups. Since the attempt is made by the researcher, the researcher cannot be blinded.

The decision support system designed for symptom self-management will be evaluated by experts, researchers by calculating the readability formula, and patients with NHL who will be included in the preliminary application. Pre-application will be made with NHL patients who meet the inclusion criteria and consent to participate in the study. It is aimed to make a preliminary application to 10% (n=5) of the sample. Patients who were taken into pre-application will not be included in the study.

A website for symptom self-management strategies of NHL patients will be developed. On the website, patients will be asked to evaluate the frequency and severity of symptoms with a visual analog scale. The severity of symptoms was categorized as mild, moderate, or severe in line with evidence-based guidelines; In line with the algorithms created according to the severity of the symptoms, the patients in the intervention group will be provided to use the decision support systems developed for symptom self-management. It is planned that the patients in the intervention group will use the decision support system for the evaluation of symptom frequency and severity and symptom self-management on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy courses. In addition, a short SMS message will be sent to the patients on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy cycles to remind the user of the decision support system developed for self-management. Three repeated SMS will be sent to patients who do not use the decision support system, and if the system is still not used, patients will be contacted by phone. Patients in the control group will benefit from routine hospital services for three months, and only the link of the website developed for symptom self-management strategies of NHL patients to evaluate symptom frequency and severity with a visual analog scale will be shared with patients. On the 3rd, 7th, and 10th days of the chemotherapy course, they will be asked to evaluate their symptom experience and severity. To remind the patients in the control group to evaluate the frequency and severity of symptoms, a short SMS message will be sent to the patients on the 3rd, 7th, and 10th days of the 3rd, 4th, and 5th chemotherapy cycles. In addition, the link to the "Lymphoma Patient Guide" on the Turkish Society of Hematology page for the symptom management system will be sent to the control group via SMS.

NHL patients meeting the sampling criteria will be assigned to the intervention and control groups by block randomization (1:1). NHL patients in the intervention group will be introduced to the decision support system regarding symptom self-management in face-to-face interviews. The decision support system will be asked to use it for three months on the 3rd, 7th, and 10th days of the 3rd, 4th and 5th chemotherapy cycles. A short SMS message will be sent to the patients on the 3rd, 7th, and 10th days of the 3rd, 4th and 5th chemotherapy courses. Participants will be able to contact researchers via the 24/7 contact button or by phone.

In the control group, SMS will be sent to the control group on the 3rd, 7th, and 10th days after each chemotherapy to remind them about the frequency and severity of symptoms during the three-month follow-up. The "Lymphoma Patient Guide" link on the Turkish Society of Hematology page for the symptom management system will be sent to the patients via SMS.

Participants will be divided into groups according to the randomization list created to assign each patient to a group. Two independent researchers will conduct the assignment of participants to the intervention and control groups and the evaluation of outcome measurement data. Researchers will not be blinded as they make the interventions. However, participants will be blinded because they do not know the study's hypotheses and which group they are in.

The Effect of Decision Support System on Symptom Self-Management in Non-Hodgkin Lymphoma Patients

The Effect of the Decision Support System Developed for Symptom Self-Management in Non-Hodgkin Lymphoma Patients on Symptom Management, Quality of Life, and Unplanned Hospital Admissions

The Rotterdam Symptom Checklist developed by De Haes et al. (1996) is used to evaluate the distress caused by the symptoms experienced by cancer patients. It consists of two sub-dimensions, Psychological and Physical Symptom. Scale items are scored on a Likert type ranging from one to four. The scale consists of 27 items in total, with the Psychological Symptom sub-dimension 8 items and the Physical Symptom sub-dimension 19 items. The Psychological Symptom subscale is the lowest 8 points, the highest 32 points, and the Physical Symptom subscale is the lowest 19 points and the highest 76 points. As the score obtained from the scale increases, the distress experienced also increases.

The Functional Assessment of Cancer Therapy \[FACT-G (Version 4)\] Scale was developed by the "Center on Outcomes, Research and Education Northwestern Healthcare" headquartered in Evanston / Illinois in the United States, and the FACT-G (Version 4) evaluates the quality of life of malignant patients. FACT-G (Version 4) scales have been translated into many different languages. The scale items contain sentences evaluating the last week. A 5-point Likert-type scale (0: not at all, 1: very little, 2: a little, 3: quite a lot, 4: a lot) is used for the answers in the scale, which consists of 27 items. The total score is between 0-108. With FACT-G (Version 4), four dimensions of quality of life are evaluated: physical condition, social and family status, emotional status, and activity status. A high total score indicates that they perform their daily activities more.

As a result of the use of the decision support system developed for self-management of the patients in the intervention group, unplanned hospital admissions are expected to decrease. For this purpose, an Unplanned Hospital Application Form was prepared by the researchers to evaluate unplanned hospital admissions. With this form, unplanned hospital or emergency service admissions in the last three months, unplanned hospitalization, unplanned outpatient admission due to chemotherapy-related problems, and unplanned need for a new drug will be evaluated in patients with NHL in the intervention group. Each application frequency will be evaluated and the groups will be compared in this respect.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Multicentric European Study In Patients With Vertebral Metastases	NCT06220071	Spinal Metastas...	Case series rev...	18 Years -	Istituto Ortopedico Rizzoli
Enhancing Therapy Adherence Among Metastatic Breast Cancer Patients	NCT06161181	Metastatic Brea...	Decision Suppor...	18 Years -	European Institute of Oncology
The Effect of Decision Support System on Symptom Self-Management in Non-Hodgkin Lymphoma Patients	NCT05443165	Decision Suppor... Non-Hodgkin Lym...	The effectivene... Symptom self-ma...	18 Years -	Akdeniz University

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