Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles

Abstract

Patients with neuropathic pain are heterogeneous in etiology, pathophysiology, and clinical appearance. They exhibit a variety of pain-related sensory symptoms and signs (sensory profile). Different sensory profiles might indicate different classes of neurobiological mechanisms, and hence subgroups with different sensory profiles might respond differently to treatment. The aim of the investigation was to identify subgroups in a large sample of patients with neuropathic pain using hypothesis-free statistical methods on the database of 3 large multinational research networks (German Research Network on Neuropathic Pain (DFNS), IMI-Europain, and Neuropain). Standardized quantitative sensory testing was used in 902 (test cohort) and 233 (validation cohort) patients with peripheral neuropathic pain of different etiologies. For subgrouping, we performed a cluster analysis using 13 quantitative sensory testing parameters. Three distinct subgroups with characteristic sensory profiles were identified and replicated. Cluster 1 (sensory loss, 42%) showed a loss of small and large fiber function in combination with paradoxical heat sensations. Cluster 2 (thermal hyperalgesia, 33%) was characterized by preserved sensory functions in combination with heat and cold hyperalgesia and mild dynamic mechanical allodynia. Cluster 3 (mechanical hyperalgesia, 24%) was characterized by a loss of small fiber function in combination with pinprick hyperalgesia and dynamic mechanical allodynia. All clusters occurred across etiologies but frequencies differed. We present a new approach of subgrouping patients with peripheral neuropathic pain of different etiologies according to intrinsic sensory profiles. These 3 profiles may be related to pathophysiological mechanisms and may be useful in clinical trial design to enrich the study population for treatment responders.

Figure 1.

CONSORT flowchart for test data set. For cluster analysis of sensory profiles in patients with peripheral neuropathic pain, databases from 3 consortia were combined: German Research Network on Neuropathic Pain (DFNS) (shaded in red), IMI-Europain, and Neuropain (shaded in blue). CRPS, complex regional pain syndrome; DB, database.

Figure 2.

Sensory profiles of the 3-cluster solution for test and replication data sets. Sensory profiles of the 3 clusters presented as mean z scores ± 95% confidence interval for the test data set (n = 902, A) and the validation data set (n = 233, B). Note that z transformation eliminates differences due to test site, sex, and age. Positive z scores indicate positive sensory signs (hyperalgesia), whereas negative z values indicate negative sensory signs (hypoaesthesia and hypoalgesia). Dashed lines: 95% confidence interval for healthy subjects (−1.96 < z < +1.96). Note that if the mean of a cluster is within the shaded area, this does not imply that it does not differ from a healthy cohort. Values are significantly different from those of healthy subjects, if their 95% confidence interval does not cross the zero line. Insets show numeric pain ratings for dynamic mechanical allodynia (DMA) on a logarithmic scale (0-100) and frequency of paradoxical heat sensation (PHS) (0-3). Blue symbols: cluster 1 “sensory loss” (42% in A and 53% in B). Red symbols: cluster 2 “thermal hyperalgesia” (33% in A and B). Yellow symbols: cluster 3 “mechanical hyperalgesia” (24% in A and 14% in B). CDT, cold detection threshold; CPT, cold pain threshold; HPT, heat pain threshold; MDT, mechanical detection threshold; MPS, mechanical pain sensitivity; MPT, mechanical pain threshold; NRS, Numerical Rating Scale; PPT, pressure pain threshold; QST, quantitative sensory testing; TSL, thermal sensory limen; VDT, vibration detection threshold; WDT, warm detection threshold; WUR, wind-up ratio.

Figure 3.

Cluster separation projected onto 2-dimensional space. Scatter plot of the 2 quantitative sensory testing (QST)-parameters that gave the best cluster separation: mechanical pain sensitivity (MPS) plotted against warm detection threshold (WDT). Blue dots: cluster 1 “sensory loss” (n = 381 patients); red dots: cluster 2 “thermal hyperalgesia” (n = 302 patients); yellow dots: cluster 3 “mechanical hyperalgesia” (n = 219 patients). Circles indicate centroids of each cluster.

Figure 4.

Frequencies of abnormal quantitative sensory testing (QST) findings for the test data set (n = 902). Each column gives the percentage of patients with abnormal findings for that particular QST parameter (outside the 95% CI of healthy subjects). Positive values indicate positive sensory signs (hyperalgesia), whereas negative values indicate negative sensory signs (hypoaesthesia and hypoalgesia). Dashed lines: Expected value for healthy subjects (±2.5%). A: cluster 1 “sensory loss” (n = 381 patients), B: cluster 2 “thermal hyperalgesia” (n = 302 patients), C: cluster 3 “mechanical hyperalgesia” (n = 219 patients). Significant compared with the expected value (2.5%) on *P < 0.05, **P < 0.01, ***P < 0.001. CDT, cold detection threshold; CPT, cold pain threshold; DMA, dynamic mechanical allodynia; HPT, heat pain threshold; MDT, mechanical detection threshold; MPS, mechanical pain sensitivity; MPT, mechanical pain threshold; NRS, Numerical Rating Scale; PHS, paradoxical heat sensation; PPT, pressure pain threshold; QST, quantitative sensory testing; TSL, thermal sensory limen; VDT, vibration detection threshold; WDT, warm detection threshold; WUR, wind-up ratio.

Figure 5.

Distribution of the 3 clusters within each neuropathic pain etiology. Blue bars: cluster 1 “sensory loss” (n = 381 patients), red bars: cluster 2 “thermal hyperalgesia” (n = 302 patients), yellow bars: cluster 3 “mechanical hyperalgesia” (n = 219 patients). Cluster 1 was most frequent in polyneuropathy, cluster 2 in peripheral nerve injury and radiculopathy, and cluster 3 in postherpetic neuralgia.