Objective. Fibromyalgia (FM) is a complex and heterogeneous disease characterized by persistent and widespread musculoskeletal pain and the presence of several accompanying symptoms. The aim of the study was to identify and characterize the different symptom patterns within a heterogeneous sample of patients with FM, using validated questionnaires of self-evaluation, administered by a computerized web-based platform. Materials and methods. We studied a total of 353 patients (85.3% women), mean age 51 years, and mean disease duration of 4.7 years (range 1-18 years). By a web platform specifically implemented (http://fibromialgiaitalia.it/site/), we administered the Italian version of the Revised Fibromyalgia Impact Questionnaire (FIQR) and the composite index of disease Fibromyalgia Status Assessment (FAS). The identification of the different sub-groups (cluster analysis) on the basis of symptoms, was conducted by hierarchical clustering of type agglomerative. Results. The domains considered most important by patients were fatigue, unrefreshing sleep, pain, stiffness, difficulty in concentration and memory loss. A high percentage of patients reported experiencing pain in the neck (81.4%), upper back (70.1%) and lower back pain (83.2%). The solution Hierarchical Cluster has revealed the presence of a three compartment model (p <0.0001). The three sub-groups divided the sample into different (p <0.0001) levels of severity: the Cluster 1 includes patients (n = 117) with low scores in all domains of FIQR; Cluster 3 groups the patients (n = 116) with high values; Cluster 2 patients (n = 120) with intermediate values on the most relevant domains but with low component psycho-affective (depression and anxiety). Conclusions. The results indicate that an Internet-based assessment is easy to use, more accurate and allows for patient-centered data over FM to show the existence of three distinct subgroups.
Scopo del lavoro. La fibromialgia (FM) è una complessa ed eterogenea affezione caratterizzata dalla persistenza di dolore muscoloscheletrico diffuso e da molteplici sintomi di accompagnamento. L'obiettivo dello studio è stato quello di identificare e caratterizzare differenti pattern sintomatologici all'interno di un eterogeneo campione di pazienti con FM, attraverso l’impiego di questionari validati di auto-valutazione, somministrati mediante piattaforma WEB. Materiali e metodi. Abbiamo reclutato 353 pazienti (85,3% donne), di età media pari a 51 anni e durata media di malattia di 4,7 anni (range 1-18 anni). Mediante piattaforma WEB appositamente implementata (http://fibromialgiaitalia.it/site/), sono stati somministrati la versione italiana del revised Fibromyalgia Impact Questionnaire (FIQR) e l'indice composito di malattia Fibromialgia Assessment Status (FAS). L’identificazione dei sottogruppi (Cluster Analysis) in base ai sintomi, è stata condotta mediante clustering gerarchico di tipo agglomerativo. Risultati. I domini considerati più rilevanti dai pazienti sono risultati stanchezza, sonno non ristoratore, dolore, rigidità, difficoltà di concentrazione e perdita di memoria. Un’elevata percentuale di pazienti ha riferito cerviconucalgia (81,4%), dorsalgia (70,1%) e lombalgia (83,2%). La soluzione gerarchica agglomerativa svelava la presenza di un modello tricompartimentale (p<0.0001). I tre sottogruppi suddividono il campione in differenti (p<0.0001) livelli di severità: il Cluster 1 comprende pazienti (n=117) con bassi punteggi in tutti i domini del FIQR; il Cluster 3 raggruppa i pazienti (n=116) con elevati valori; il Cluster 2 pazienti (n=120) con moderato controllo del dolore e bassa componente psico-affettiva. Conclusioni. I risultati indicano che la valutazione Internet-based è più precisa e consente di ottenere dati centrati sul paziente FM oltre a dimostrare l'esistenza di tre differenti sottogruppi.
Cluster analysis e pattern sintomatologici nella fibromialgia - risultati di uno studio policentrico su piattaforma web-based / Draghessi, Antonella. - (2016 Feb 25).
Cluster analysis e pattern sintomatologici nella fibromialgia - risultati di uno studio policentrico su piattaforma web-based
Draghessi, Antonella
2016-02-25
Abstract
Objective. Fibromyalgia (FM) is a complex and heterogeneous disease characterized by persistent and widespread musculoskeletal pain and the presence of several accompanying symptoms. The aim of the study was to identify and characterize the different symptom patterns within a heterogeneous sample of patients with FM, using validated questionnaires of self-evaluation, administered by a computerized web-based platform. Materials and methods. We studied a total of 353 patients (85.3% women), mean age 51 years, and mean disease duration of 4.7 years (range 1-18 years). By a web platform specifically implemented (http://fibromialgiaitalia.it/site/), we administered the Italian version of the Revised Fibromyalgia Impact Questionnaire (FIQR) and the composite index of disease Fibromyalgia Status Assessment (FAS). The identification of the different sub-groups (cluster analysis) on the basis of symptoms, was conducted by hierarchical clustering of type agglomerative. Results. The domains considered most important by patients were fatigue, unrefreshing sleep, pain, stiffness, difficulty in concentration and memory loss. A high percentage of patients reported experiencing pain in the neck (81.4%), upper back (70.1%) and lower back pain (83.2%). The solution Hierarchical Cluster has revealed the presence of a three compartment model (p <0.0001). The three sub-groups divided the sample into different (p <0.0001) levels of severity: the Cluster 1 includes patients (n = 117) with low scores in all domains of FIQR; Cluster 3 groups the patients (n = 116) with high values; Cluster 2 patients (n = 120) with intermediate values on the most relevant domains but with low component psycho-affective (depression and anxiety). Conclusions. The results indicate that an Internet-based assessment is easy to use, more accurate and allows for patient-centered data over FM to show the existence of three distinct subgroups.File | Dimensione | Formato | |
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