Predictive Models on Pain and Severity in FM Patients
Development of Predictive Models Based on Artificial Intelligence for the Analysis of the Psychosocial Profile of the Patient With Fibromyalgia on Pain and Severity of the Disease.
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.
Descripción general del estudio
Estado
Condiciones
Descripción detallada
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.
Tipo de estudio
Inscripción (Anticipado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Rubén Arroyo Fernández, MSc
- Número de teléfono: 86589 925803600
- Correo electrónico: rubenarroyofernandez@gmail.com
Ubicaciones de estudio
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Toledo
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Talavera De La Reina, Toledo, España, 45600
- Hospital General Nuestra Señora del Prado
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Contacto:
- Rubén Arroyo Fernández, MSc
- Número de teléfono: 86589 925803600
- Correo electrónico: rubenarroyofernandez@gmail.com
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
Acepta Voluntarios Saludables
Géneros elegibles para el estudio
Método de muestreo
Población de estudio
Descripción
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.
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
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Pain intensity
Periodo de tiempo: Baseline.
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It will be measured with a visual analog scale (VAS) of 100 millimeters in length.
The subject has to indicate the level ofpain he feels, being 0 the absence of pain and 100 the maximum imaginable.
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Baseline.
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Disease severity.
Periodo de tiempo: Baseline.
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It will be measured using the Polysymptomatic Distress Scale (PDS) (or Fibromyalgia Severity Scale), composed of the sum of the following two scales:
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Baseline.
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Referred pain area after suprathreshold pressure stimulation.
Periodo de tiempo: Baseline.
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A pressure algometer (Force Ten™, Wagner Instruments, USA) will be used. It will be performed on the infraspinatus muscle (point equidistant between the midpoint of the spine of the scapula, the inferior angle of the scapula and the midpoint of the medial border of the scapula) at a constant suprathreshold pressure (20% above the pressure pain threshold) for 60 seconds. After the stimulation, the subject should draw the induced pain area on a digital bodychart using the Navigate Pain application (Navigate Pain, Aalborg University, Denmark). |
Baseline.
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Fibromyalgia Impact Quality-of-Life.
Periodo de tiempo: Baseline.
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It will be measured with the version adapted to the Spanish of the Fibromyalgia Impact Questionnaire (FIQ).
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Baseline.
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Anxiety.
Periodo de tiempo: Baseline.
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The version adapted to Spanish from the State Scale (STAI-ES) of the State-Trait Anxiety Inventory (STAI) will be used.
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Baseline.
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Pain catastrophizing.
Periodo de tiempo: Baseline.
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The Spanish version of the Pain Catastrophizing Scale (PCS) will be used.
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Baseline.
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Depression.
Periodo de tiempo: Baseline.
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The adaptation to the Spanish of Beck Depression Inventory II will be used.
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Baseline.
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Colaboradores e Investigadores
Patrocinador
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Anticipado)
Finalización primaria (Anticipado)
Finalización del estudio (Anticipado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- IA Fibromyalgia
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
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