J 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

DOI

UT WoS

000524415800016

Keywords in English

complex regional pain syndrome

Tags

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.