A Comparison of Self-reported Pain Measures Between Sensory Phenotypes in HIV-associated Sensory Neuropathy

Abstract

Painful HIV-associated neuropathy (HIV-SN) is a prevalent co-morbidity of HIV infection. Sensory phenotyping, using quantitative sensory testing (QST) could allow for improved stratification to guide personalized treatment. However, previous methods of QST interpretation have demonstrated limited association with self-reported pain measures. This study sought to identify differences in self-reported pain measures between composite QST-derived sensory phenotypes, and to examine any differences in participants reporting multi-site, multi-etiology chronic pain. In this cross-sectional observational study of participants with HIV (n = 133), individuals were allocated to neuropathy and neuropathic pain groups through clinical assessment and nerve conduction testing. They completed symptom-based questionnaires and underwent standardized QST. Participants were assigned, by pre-determined algorithm, to a QST-derived sensory phenotype. Symptoms were compared between sensory phenotypes. Symptom characteristics and Neuropathic Pain Symptom Inventory scores differed between QST-derived sensory phenotypes: ‘sensory loss’ was associated with more paroxysmal and paraesthetic symptoms compared to ‘thermal hyperalgesia’ and ‘healthy’ phenotypes (P = .023–0.001). Those with painful HIV-SN and additional chronic pain diagnoses were more frequently allocated to the ‘mechanical hyperalgesia’ phenotype compared to those with painful HIV-SN alone (P = .006). This study describes heterogeneous sensory phenotypes in people living with HIV. Differences in self-reported pain outcomes between sensory phenotypes has the potential to guide future stratified trials and eventually more targeted therapy. PerspectiveThis article presents quantitative sensory testing derived phenotypes, thought to reflect differing pathophysiological pain mechanisms and relates them to self-reported pain measures in people with HIV infection. This could help clinicians stratify patients to individualize analgesic interventions more effectively.