Predictive Models on Pain and Severity in FM Patients

NCT ID: NCT04918602

Last Updated: 2021-06-09

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 150 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-06-30
• Study Completion Date: 2021-11-30

Brief Summary

The primary goal of this research project is to develop different prediction models in fibromyalgia disease through the application of machine learning techniques and to assess the explainability of the results.

As specific objectives the research project intends: to predicting Fibromyalgia severity of patients based on clinical variables; to assess the relevance of social-psycho-demographic variables on the fibromyalgia severity of the patients; to predict the pain suffered by the patients as well as the impact of the fibromyalgia on patient's life; to categorize fibromyalgia group of patients depending on their levels of Fibromyalgia severity.

Detailed Description

Fibromyalgia (FM) is a condition characterized by chronic musculoskeletal pain whose pathophysiology is still unclear. Furthermore, this pathology is frequently associated with sleep disturbances, pronounced fatigue, morning stiffness, poor quality of life, cognitive disturbances (mainly memory problems) and psychological problems (depression, anxiety and stress).

FM is associated with greater negative affect, which implies a general state of anguish composed of aversive emotions such as sadness, fear, anger and guilt. Patients with FM commonly suffer from high rates of anxiety, depression, pain catastrophizing, and stress levels, which are associated with a worsening of symptoms, including own cognitive.

Machine learning (ML) and data mining had been successfully applied, over the past few decades, to build computer-aided diagnosis (CAD) systems for diagnosing complex health issues with good accuracy and efficiency by recognizing potentially useful, original, and comprehensible patterns in health data. Thus, machine learning provides useful tools for multivariate data analysis allowing predictions based on the established models and hence offering a suitable advantage for risk assessment of many diseases including heart failure. Machine learning offers advantages not only for clinical prediction but also for feature ranking improving the interpretation of the outputs by clinical professionals.

Explainable ML models, also known as interpretable ML models, allow healthcare experts to make reasonable and data-driven decisions to provide personalized treatment that can ultimately lead to high quality of service in healthcare. These models fall into eXplainable Artificial Intelligence (XAI) field, defined as suite of ML techniques that 1) produce more explainable models while maintaining a high level of learning performance, and 2) enable humans to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners.

Conditions

Fibromyalgia

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• Age between 18 and 65 years.
• Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
• Understanding of spoken and written Spanish.

Exclusion Criteria

• Diagnosed psychiatric pathology.
• Rheumatic pathology not medically controlled.
• Neurological pathologies that make evaluations difficult.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of Castilla-La Mancha

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hospital General Nuestra Señora del Prado

Talavera de la Reina, Toledo, Spain

Countries

Spain

Central Contacts

Rubén Arroyo Fernández, MSc

Role: CONTACT

rubenarroyofernandez@gmail.com

925803600 ext. 86589

Facility Contacts

Rubén Arroyo Fernández, MSc

Role: primary

rubenarroyofernandez@gmail.com

925803600 ext. 86589

Other Identifiers

IA Fibromyalgia

Identifier Type: -

Identifier Source: org_study_id