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Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study

Pain can be present in up to 50% of people with post-COVID-19 condition. Understanding the complexity of post-COVID pain can help with better phenotyping of this post-COVID symptom. The aim of this study is to describe the complex associations between sensory-related, psychological, and cognitive va...

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Autores principales: Fernández-de-las-Peñas, César, Liew, Bernard X. W., Herrero-Montes, Manuel, del-Valle-Loarte, Pablo, Rodríguez-Rosado, Rafael, Ferrer-Pargada, Diego, Neblett, Randy, Paras-Bravo, Paula
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696487/
https://www.ncbi.nlm.nih.gov/pubmed/36422588
http://dx.doi.org/10.3390/pathogens11111336
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author Fernández-de-las-Peñas, César
Liew, Bernard X. W.
Herrero-Montes, Manuel
del-Valle-Loarte, Pablo
Rodríguez-Rosado, Rafael
Ferrer-Pargada, Diego
Neblett, Randy
Paras-Bravo, Paula
author_facet Fernández-de-las-Peñas, César
Liew, Bernard X. W.
Herrero-Montes, Manuel
del-Valle-Loarte, Pablo
Rodríguez-Rosado, Rafael
Ferrer-Pargada, Diego
Neblett, Randy
Paras-Bravo, Paula
author_sort Fernández-de-las-Peñas, César
collection PubMed
description Pain can be present in up to 50% of people with post-COVID-19 condition. Understanding the complexity of post-COVID pain can help with better phenotyping of this post-COVID symptom. The aim of this study is to describe the complex associations between sensory-related, psychological, and cognitive variables in previously hospitalized COVID-19 survivors with post-COVID pain, recruited from three hospitals in Madrid (Spain) by using data-driven path analytic modeling. Demographic (i.e., age, height, and weight), sensory-related (intensity or duration of pain, central sensitization-associated symptoms, and neuropathic pain features), psychological (anxiety and depressive levels, and sleep quality), and cognitive (catastrophizing and kinesiophobia) variables were collected in a sample of 149 subjects with post-COVID pain. A Bayesian network was used for structural learning, and the structural model was fitted using structural equation modeling (SEM). The SEM model fit was excellent: RMSEA < 0.001, CFI = 1.000, SRMR = 0.063, and NNFI = 1.008. The only significant predictor of post-COVID pain was the level of depressive symptoms ([Formula: see text] , p = 0.001). Higher levels of anxiety were associated with greater central sensitization-associated symptoms by a magnitude of [Formula: see text] (p = 0.008). Males reported less severe neuropathic pain symptoms (−1.50 SD S-LANSS score, p < 0.001) than females. A higher level of depressive symptoms was associated with worse sleep quality ([Formula: see text] , p < 0.001), and greater levels of catastrophizing ([Formula: see text] , p < 0.001). This study presents a model for post-COVID pain where psychological factors were related to central sensitization-associated symptoms and sleep quality. Further, maladaptive cognitions, such as catastrophizing, were also associated with depression. Finally, females reported more neuropathic pain features than males. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in COVID-19 survivors with post-COVID pain and can represent a first step for the development of a theoretical/conceptual framework for post-COVID pain.
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spelling pubmed-96964872022-11-26 Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study Fernández-de-las-Peñas, César Liew, Bernard X. W. Herrero-Montes, Manuel del-Valle-Loarte, Pablo Rodríguez-Rosado, Rafael Ferrer-Pargada, Diego Neblett, Randy Paras-Bravo, Paula Pathogens Article Pain can be present in up to 50% of people with post-COVID-19 condition. Understanding the complexity of post-COVID pain can help with better phenotyping of this post-COVID symptom. The aim of this study is to describe the complex associations between sensory-related, psychological, and cognitive variables in previously hospitalized COVID-19 survivors with post-COVID pain, recruited from three hospitals in Madrid (Spain) by using data-driven path analytic modeling. Demographic (i.e., age, height, and weight), sensory-related (intensity or duration of pain, central sensitization-associated symptoms, and neuropathic pain features), psychological (anxiety and depressive levels, and sleep quality), and cognitive (catastrophizing and kinesiophobia) variables were collected in a sample of 149 subjects with post-COVID pain. A Bayesian network was used for structural learning, and the structural model was fitted using structural equation modeling (SEM). The SEM model fit was excellent: RMSEA < 0.001, CFI = 1.000, SRMR = 0.063, and NNFI = 1.008. The only significant predictor of post-COVID pain was the level of depressive symptoms ([Formula: see text] , p = 0.001). Higher levels of anxiety were associated with greater central sensitization-associated symptoms by a magnitude of [Formula: see text] (p = 0.008). Males reported less severe neuropathic pain symptoms (−1.50 SD S-LANSS score, p < 0.001) than females. A higher level of depressive symptoms was associated with worse sleep quality ([Formula: see text] , p < 0.001), and greater levels of catastrophizing ([Formula: see text] , p < 0.001). This study presents a model for post-COVID pain where psychological factors were related to central sensitization-associated symptoms and sleep quality. Further, maladaptive cognitions, such as catastrophizing, were also associated with depression. Finally, females reported more neuropathic pain features than males. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in COVID-19 survivors with post-COVID pain and can represent a first step for the development of a theoretical/conceptual framework for post-COVID pain. MDPI 2022-11-12 /pmc/articles/PMC9696487/ /pubmed/36422588 http://dx.doi.org/10.3390/pathogens11111336 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Fernández-de-las-Peñas, César
Liew, Bernard X. W.
Herrero-Montes, Manuel
del-Valle-Loarte, Pablo
Rodríguez-Rosado, Rafael
Ferrer-Pargada, Diego
Neblett, Randy
Paras-Bravo, Paula
Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study
title Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study
title_full Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study
title_fullStr Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study
title_full_unstemmed Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study
title_short Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study
title_sort data-driven path analytic modeling to understand underlying mechanisms in covid-19 survivors suffering from long-term post-covid pain: a spanish cohort study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696487/
https://www.ncbi.nlm.nih.gov/pubmed/36422588
http://dx.doi.org/10.3390/pathogens11111336
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