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Predictive Models on Pain and Severity in FM Patients

2. juni 2021 oppdatert av: University of Castilla-La Mancha

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.

Studieoversikt

Status

Har ikke rekruttert ennå

Forhold

Detaljert beskrivelse

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.

Studietype

Observasjonsmessig

Registrering (Forventet)

150

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiekontakt

Studiesteder

  • Spania
    • Toledo
      • Talavera De La Reina, Toledo, Spania, 45600
        • Hospital General Nuestra Señora del Prado
        • Ta kontakt med:

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

18 år og eldre (Voksen, Eldre voksen)

Tar imot friske frivillige

Nei

Kjønn som er kvalifisert for studier

Alle

Prøvetakingsmetode

Ikke-sannsynlighetsprøve

Studiepopulasjon

Members enrolled in a local fibromyalgia association.

Beskrivelse

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.

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Pain intensity
Tidsramme: 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.
Baseline.
Disease severity.
Tidsramme: 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.
Baseline.
Referred pain area after suprathreshold pressure stimulation.
Tidsramme: 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).
Baseline.

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Fibromyalgia Impact Quality-of-Life.
Tidsramme: Baseline.
It will be measured with the version adapted to the Spanish of the Fibromyalgia Impact Questionnaire (FIQ).
Baseline.
Anxiety.
Tidsramme: Baseline.
The version adapted to Spanish from the State Scale (STAI-ES) of the State-Trait Anxiety Inventory (STAI) will be used.
Baseline.
Pain catastrophizing.
Tidsramme: Baseline.
The Spanish version of the Pain Catastrophizing Scale (PCS) will be used.
Baseline.
Depression.
Tidsramme: Baseline.
The adaptation to the Spanish of Beck Depression Inventory II will be used.
Baseline.

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Sponsor

University of Castilla-La Mancha

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Forventet)

1. juni 2021

Primær fullføring (Forventet)

1. november 2021

Studiet fullført (Forventet)

1. november 2021

Datoer for studieregistrering

Først innsendt

2. juni 2021

Først innsendt som oppfylte QC-kriteriene

2. juni 2021

Først lagt ut (Faktiske)

9. juni 2021

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

9. juni 2021

Siste oppdatering sendt inn som oppfylte QC-kriteriene

2. juni 2021

Sist bekreftet

1. juni 2021

Mer informasjon

Begreper knyttet til denne studien

Ytterligere relevante MeSH-vilkår

Andre studie-ID-numre

  • IA Fibromyalgia

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .

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