FibromyalgiaNCT04918602
Predictive Models on Pain and Severity in FM Patients
An observational study of Fibromyalgia, run by University of Castilla-La Mancha at 1 site in Spain.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Fibromyalgia
- How many people
- 150 participants
- Where
- 1 siteSpain
- Study dates
- Jun 2021to Nov 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Members enrolled in a local fibromyalgia association.
Who may be able to join
- Age between 18 and 65 years.
- Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
- Understanding of spoken and written Spanish.
Who cannot join
- Diagnosed psychiatric pathology.
- Rheumatic pathology not medically controlled.
- Neurological pathologies that make evaluations difficult.
Trial locations and contacts
1 site in 1 country.
Spain1 site
Hospital General Nuestra Señora del Prado
Talavera de la Reina, Toledo, 45600
Rubén Arroyo Fernández, MSc, 925803600 ext. 86589, rubenarroyofernandez@gmail.com
About this study
Official title: 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.
Read the 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
- Medical subject headings
- Fibromyalgia
What is being measured
The primary outcome is the main question the study is designed to answer.
Pain intensity
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.
Measured: Baseline.
Disease severity.
It will be measured using the Polysymptomatic Distress Scale (PDS) (or Fibromyalgia Severity Scale), composed of the sum of the following two scales:
- Widespread Pain Index (WPI): Questionnaire in which a total of 19 body areas are represented. The subject has to mark the regions where the pain appears. It represents a measure of the extent of pain, with a maximum score of 19 points.
- Symptom Severity Scale (SSS): Questionnaire that measures the severity of the symptoms associated with fibromyalgia, such as fatigue, non-restorative sleep, cognitive problems, headaches, abdominal pain or cramps and depression. It represents a measure of somatic and non-somatic symptoms of fibromyalgia, with a maximum score of 12 points.
Measured: Baseline.
Referred pain area after suprathreshold pressure stimulation.
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).
Measured: Baseline.
Secondary outcomes (4)
Fibromyalgia Impact Quality-of-Life.
It will be measured with the version adapted to the Spanish of the Fibromyalgia Impact Questionnaire (FIQ).
Measured: Baseline.
Anxiety.
The version adapted to Spanish from the State Scale (STAI-ES) of the State-Trait Anxiety Inventory (STAI) will be used.
Measured: Baseline.
Pain catastrophizing.
The Spanish version of the Pain Catastrophizing Scale (PCS) will be used.
Measured: Baseline.
Depression.
The adaptation to the Spanish of Beck Depression Inventory II will be used.
Measured: Baseline.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2021
- Study startJun 2021 (estimated)
- Primary completionNov 2021 (estimated)
- Study completionNov 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for FibromyalgiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 150 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Castilla-La Mancha
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04918602
- Sponsor's protocol ID
- IA Fibromyalgia
- Status last verified
- Jun 2021
- First submitted
- 2 Jun 2021
- Last updated
- 9 Jun 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Moreno-Sanchez PA, Arroyo-Fernandez R, Bravo-Esteban E, Ferri-Morales A, van Gils M. Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI. Int J Med Inform. 2024 Jan;181:105280. doi: 10.1016/j.ijmedinf.2023.105280. Epub 2023 Nov 4.
PubMed 37952406
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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.
- ClinicalTrials.gov ID
- NCT04918602
- Acronym / sponsor ID
- IA Fibromyalgia
- Conditions
- Fibromyalgia
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 150 participants (estimated)
- Lead sponsor
- University of Castilla-La ManchaUniversity, hospital or other
- Study start
- Jun 2021 (estimated)
- Primary completion
- Nov 2021 (estimated)
- Study completion
- Nov 2021 (estimated)
- First posted
- 9 Jun 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Members enrolled in a local fibromyalgia association.
Key inclusion criteria (3 listed)
- Age between 18 and 65 years.
- Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
- Understanding of spoken and written Spanish.
Key exclusion criteria (3 listed)
- Diagnosed psychiatric pathology.
- Rheumatic pathology not medically controlled.
- Neurological pathologies that make evaluations difficult.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Pain intensity
Time frame: Baseline.
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.
Disease severity.
Time frame: Baseline.
It will be measured using the Polysymptomatic Distress Scale (PDS) (or Fibromyalgia Severity Scale), composed of the sum of the following two scales: 1. Widespread Pain Index (WPI): Questionnaire in which a total of 19 body areas are represented. The subject has to mark the regions where the pain appears. It represents a measure of the extent of pain, with a maximum score of 19 points. 2. Symptom Severity Scale (SSS): Questionnaire that measures the severity of the symptoms associated with fibromyalgia, such as fatigue, non-restorative sleep, cognitive problems, headaches, abdominal pain or cramps and depression. It represents a measure of somatic and non-somatic symptoms of fibromyalgia, with a maximum score of 12 points.
Referred pain area after suprathreshold pressure stimulation.
Time frame: Baseline.
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).
4 secondary outcomes
Fibromyalgia Impact Quality-of-Life.
Time frame: Baseline.
Anxiety.
Time frame: Baseline.
Pain catastrophizing.
Time frame: Baseline.
Depression.
Time frame: Baseline.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jun 2021.
- Derived from this study: PubMed 37952406Moreno-Sanchez PA, Arroyo-Fernandez R, Bravo-Esteban E, Ferri-Morales A, van Gils M. Assessing the relevance of mental health factors in fibromyalgia severity: A data-driven case study using explainable AI. Int J Med Inform. 2024 Jan;181:105280. doi: 10.1016/j.ijmedinf.2023.105280. Epub 2023 Nov 4.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Fibromyalgia, run by University of Castilla-La Mancha at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 9 Jun 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Rubén Arroyo Fernández, MSc (925803600 ext. 86589, rubenarroyofernandez@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Pain intensity (time frame: Baseline.). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04918602, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jun 2021.