Outlining the Psychological Profile of Persistent Depression in Fibromyalgia Patients through Personality Assessment Inventory (PAI)

Andrea Doreste, Jesus Pujol, Eva Penelo, Víctor Perez, Laura Blanco-Hinojo, Gerard Martínez-Vilavella, Helena Pardina-Torner, Fabiola Ojeda, Jordi Monfort, Joan Deus*

*Corresponding author for this work

Research output: Working paperPreprint

Abstract

Background. Fibromyalgia (FM) is a complex condition marked by increased pain sensitivity and central sensitization. Studies often explore the link between FM and depressiveanxiety disorders, but few focus on dysthymia or persistent depressive disorder (PDD), which can be more disabling than major depression (MD). Objective. To identify clinical scales and subscales of the Personality Assessment Inventory (PAI) that effectively describe and differentiate the psychological profile of PDD, with or without comorbid MD, in FM patients with PDD previously dimensionally classified by the Millon Clinical Multiaxial Inventory III (MCMI-III). Method. An observational, cross-sectional study was conducted with 66 women (mean age 49.18, SD=8.09) from Hospital del Mar. The PAI, the MCMI-III, and Fibromyalgia Impact Questionnaire (FIQ) were used to assess the sample. Results. The PAI showed strong discriminative ability in detecting PDD, characterized by high scores in cognitive and emotional depression, and low scores in identity alteration, dominance, and grandeur. High scores in cognitive, emotional, and physiological depression, identity alteration, cognitive anxiety, and suicidal ideation, along with low scores in dominance and grandeur, were needed to detect MD with PDD. Discriminant analysis could differentiate 69.6-73.9% of the PDD group and 84.6% of the PDD+MD group. Group comparisons showed that 72.2% of patients with an affective disorder by PAI were correctly classified in the MCMI-III affective disorder group, and 70% without affective disorder were correctly classified. Conclusions. The PAI effectively identifies PDD in FM patients and detects concurrent MD episodes, aiding in better prognostic and therapeutic guidance.
Original languageEnglish
Number of pages17
DOIs
Publication statusE-pub ahead of print - 29 Oct 2024

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