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

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 stratifica-tion to guide personalized treatment. However, previous methods of QST interpretation have dem-onstrated 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 allo-cated to neuropathy and neuropathic pain groups through clinical assessment and nerve conduc-tion 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 hyper-algesia' 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 sen-sory phenotypes in people living with HIV. Differences in self-reported pain outcomes between sen-sory phenotypes has the potential to guide future stratified trials and eventually more targeted therapy.Perspective: This 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.(c) 2022 The Author(s). Published by Elsevier Inc. on behalf of United States Association for the Study of Pain, Inc. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/)