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Cross-Sectional Comparative Study on Central Sensitization-Psychosocial Associated Comorbidities and Psychological Characteristics in Breast Cancer Survivors with Nociceptive Pain and Pain with Neuropathic Features and without Pain.

Irene de la Rosa-DíazLaura Barrero-SantiagoPaz Acosta-RamírezMarina Martín-Peces-BarbaEsther Iglesias-HernándezBastien PlissetNicolás LutinierMargot BelzanneRoy La ToucheMónica Grande-Alonso
Published in: Life (Basel, Switzerland) (2022)
The frequency of a high Central Sensitization Inventory (CSI) total score and the prevalence of pain have already been established among breast cancer survivors (BCS). However, the psychological factors' influence based on the clinical features of pain is still unknown, as well as BCS characteristics with no pain. Thus, our main aim was to evaluate the presence of a high CSI total score in BCS with pain and compare it with BCS without pain and to evaluate the influence of psychosocial factors. A cross-sectional comparative study was designed to compare BCS with nociceptive pain ( n = 19), pain with neuropathic features ( n = 19) or no pain ( n = 19), classified by the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS). CSI, pain catastrophizing, fear of movement, anxiety and depression symptoms were analyzed and compared among the three groups. The CSI total score was higher in both BCS pain groups compared to BCS without pain, but there were no statistical differences between the pain groups. The same observation was made when comparing pain catastrophizing. The neuropathic feature group showed greater levels of fear of movement, anxiety and depression compared to the no pain group. Thus, CS-psychosocial associated comorbidities and pain-catastrophizing thoughts were more prevalent among BCS with pain, regardless of the clinical features of pain. BCS with neuropathic pain features showed greater psychological disturbances.
Keyphrases
  • chronic pain
  • neuropathic pain
  • pain management
  • spinal cord
  • spinal cord injury
  • mental health
  • depressive symptoms
  • deep learning