Detailed Information on Publication Record
2020
Clinical phenotypes and classification algorithm for complex regional pain syndrome
DIMOVA, V., M. S. HERRNBERGER, F. ESCOLANO-LOZANO, H .L. RITTNER, Eva VLČKOVÁ et. al.Basic information
Original name
Clinical phenotypes and classification algorithm for complex regional pain syndrome
Authors
DIMOVA, V. (guarantor), M. S. HERRNBERGER, F. ESCOLANO-LOZANO, H .L. RITTNER, Eva VLČKOVÁ (203 Czech Republic, belonging to the institution), C. SOMMER, C. MAIHOFNER and F. BIRKLEIN
Edition
Neurology, Philadelphia, LIPPINCOTT WILLIAMS & WILKINS, 2020, 0028-3878
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
30210 Clinical neurology
Country of publisher
United States of America
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 9.910
RIV identification code
RIV/00216224:14740/20:00118241
Organization unit
Central European Institute of Technology
UT WoS
000524415800016
Keywords in English
complex regional pain syndrome
Tags
International impact, Reviewed
Změněno: 16/3/2021 20:46, Mgr. Pavla Foltynová, Ph.D.
Abstract
V originále
Objective We pursued the hypothesis that complex regional pain syndrome (CRPS) signs observed by neurologic examination display a structure allowing for alignment of patients to particular phenotype clusters. Methods Clinical examination data were obtained from 3 independent samples of 444, 391, and 202 patients with CRPS. The structure among CRPS signs was analyzed in sample 1 and validated with sample 2 using hierarchical clustering. For patients with CRPS in sample 3, an individual phenotype score was submitted to k-means clustering. Pain characteristics, quantitative sensory testing, and psychological data were tested in this sample as descriptors for phenotypes. Results A 2-cluster structure emerged in sample 1 and was replicated in sample 2. Cluster 1 comprised minor injury eliciting CRPS, motor signs, allodynia, and glove/stocking-like sensory deficits, resembling a CRPS phenotype most likely reflecting a CNS pathophysiology (the central phenotype). Cluster 2, which consisted of edema, skin color changes, skin temperature changes, sweating, and trophic changes, probably represents peripheral inflammation, the peripheral phenotype. In sample 3, individual phenotype scores were calculated as the sum of the mean values of signs from each cluster, where signs from cluster 1 were coded with 1 and from cluster 2 with -1. A k-means algorithm separated groups with 78, 36, and 88 members resembling the peripheral, central, and mixed phenotypes, respectively. The central phenotype was characterized by cold hyperalgesia at the affected limb. Conclusions Statistically determined CRPS phenotypes may reflect major pathophysiologic mechanisms of peripheral inflammation and central reorganization.