Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome
Identification of subgroups of patients with chronic pain provides meaningful insights into the characteristics of a specific population, helping to identify individuals at risk of chronification and to determine appropriate therapeutic strategies. This paper proposes the use of spectral clustering...
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doaj-5b8dc414d5f14befa24031d0e914a0ff2021-06-30T23:44:34ZengMDPI AGSymmetry2073-89942021-06-01131042104210.3390/sym13061042Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel SyndromeOscar J. Pellicer-Valero0José D. Martín-Guerrero1César Fernández-de-las-Peñas2Ana I. De-la-Llave-Rincón3Jorge Rodríguez-Jiménez4Esperanza Navarro-Pardo5Margarita I. Cigarán-Méndez6Intelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València, 46010 Valencia, SpainIntelligent Data Analysis Laboratory, Department of Electronic Engineering, ETSE (Engineering School), Universitat de València, 46010 Valencia, SpainDepartment of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartment of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartment of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, 28922 Madrid, SpainDepartamento de Psicología Evolutiva y de la Educación, Universitat de València, 46010 Valencia, SpainDepartment of Psychology, Universidad Rey Juan Carlos, 28922 Madrid, SpainIdentification of subgroups of patients with chronic pain provides meaningful insights into the characteristics of a specific population, helping to identify individuals at risk of chronification and to determine appropriate therapeutic strategies. This paper proposes the use of spectral clustering (SC) to distinguish subgroups (clusters) of individuals with carpal tunnel syndrome (CTS), making use of the obtained patient profiling to argue about potential management implications. SC is a powerful algorithm that builds a similarity graph among the data points (the patients), and tries to find the subsets of points that are strongly connected among themselves, but weakly connected to others. It was chosen due to its advantages with respect to other simpler clustering techniques, such as k-means, and the fact that it has been successfully applied to similar problems. Clinical (age, duration of symptoms, pain intensity, function, and symptom severity), psycho-physical (pressure pain thresholds—PPTs—over the three main nerve trunks of the upper extremity, cervical spine, carpal tunnel, and tibialis anterior), psychological (depressive levels), and motor (pinch tip grip force) variables were collected in 208 women with clinical/electromyographic diagnosis of CTS, whose symptoms usually started unilaterally but eventually evolved into bilateral symmetry. SC was used to identify clusters of patients without any previous assumptions, yielding three clusters. Patients in cluster 1 exhibited worse clinical features, higher widespread pressure pain hyperalgesia, higher depressive levels, and lower pinch tip grip force than the other two. Patients in cluster 2 showed higher generalized thermal pain hyperalgesia than the other two. Cluster 0 showed less hypersensitivity to pressure and thermal pain, less severe clinical features, and more normal motor output (tip grip force). The presence of subgroups of individuals with different altered nociceptive processing (one group being more sensitive to pressure pain and another group more sensitive to thermal pain) could lead to different therapeutic programs.https://www.mdpi.com/2073-8994/13/6/1042carpal tunnel syndromespectral clusteringpaingroupssensitization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Oscar J. Pellicer-Valero José D. Martín-Guerrero César Fernández-de-las-Peñas Ana I. De-la-Llave-Rincón Jorge Rodríguez-Jiménez Esperanza Navarro-Pardo Margarita I. Cigarán-Méndez |
spellingShingle |
Oscar J. Pellicer-Valero José D. Martín-Guerrero César Fernández-de-las-Peñas Ana I. De-la-Llave-Rincón Jorge Rodríguez-Jiménez Esperanza Navarro-Pardo Margarita I. Cigarán-Méndez Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome Symmetry carpal tunnel syndrome spectral clustering pain groups sensitization |
author_facet |
Oscar J. Pellicer-Valero José D. Martín-Guerrero César Fernández-de-las-Peñas Ana I. De-la-Llave-Rincón Jorge Rodríguez-Jiménez Esperanza Navarro-Pardo Margarita I. Cigarán-Méndez |
author_sort |
Oscar J. Pellicer-Valero |
title |
Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome |
title_short |
Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome |
title_full |
Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome |
title_fullStr |
Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome |
title_full_unstemmed |
Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome |
title_sort |
spectral clustering reveals different profiles of central sensitization in women with carpal tunnel syndrome |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2021-06-01 |
description |
Identification of subgroups of patients with chronic pain provides meaningful insights into the characteristics of a specific population, helping to identify individuals at risk of chronification and to determine appropriate therapeutic strategies. This paper proposes the use of spectral clustering (SC) to distinguish subgroups (clusters) of individuals with carpal tunnel syndrome (CTS), making use of the obtained patient profiling to argue about potential management implications. SC is a powerful algorithm that builds a similarity graph among the data points (the patients), and tries to find the subsets of points that are strongly connected among themselves, but weakly connected to others. It was chosen due to its advantages with respect to other simpler clustering techniques, such as k-means, and the fact that it has been successfully applied to similar problems. Clinical (age, duration of symptoms, pain intensity, function, and symptom severity), psycho-physical (pressure pain thresholds—PPTs—over the three main nerve trunks of the upper extremity, cervical spine, carpal tunnel, and tibialis anterior), psychological (depressive levels), and motor (pinch tip grip force) variables were collected in 208 women with clinical/electromyographic diagnosis of CTS, whose symptoms usually started unilaterally but eventually evolved into bilateral symmetry. SC was used to identify clusters of patients without any previous assumptions, yielding three clusters. Patients in cluster 1 exhibited worse clinical features, higher widespread pressure pain hyperalgesia, higher depressive levels, and lower pinch tip grip force than the other two. Patients in cluster 2 showed higher generalized thermal pain hyperalgesia than the other two. Cluster 0 showed less hypersensitivity to pressure and thermal pain, less severe clinical features, and more normal motor output (tip grip force). The presence of subgroups of individuals with different altered nociceptive processing (one group being more sensitive to pressure pain and another group more sensitive to thermal pain) could lead to different therapeutic programs. |
topic |
carpal tunnel syndrome spectral clustering pain groups sensitization |
url |
https://www.mdpi.com/2073-8994/13/6/1042 |
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