Fernández-de-las-Peñas, César and Liew, Bernard XW and Herrero-Montes, Manuel and del-Valle-Loarte, Pablo and Rodríguez-Rosado, Rafael and Ferrer-Pargada, Diego and Neblett, Randy and Paras-Bravo, Paula (2022) Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study. Pathogens, 11 (11). p. 1336. DOI https://doi.org/10.3390/pathogens11111336
Fernández-de-las-Peñas, César and Liew, Bernard XW and Herrero-Montes, Manuel and del-Valle-Loarte, Pablo and Rodríguez-Rosado, Rafael and Ferrer-Pargada, Diego and Neblett, Randy and Paras-Bravo, Paula (2022) Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study. Pathogens, 11 (11). p. 1336. DOI https://doi.org/10.3390/pathogens11111336
Fernández-de-las-Peñas, César and Liew, Bernard XW and Herrero-Montes, Manuel and del-Valle-Loarte, Pablo and Rodríguez-Rosado, Rafael and Ferrer-Pargada, Diego and Neblett, Randy and Paras-Bravo, Paula (2022) Data-Driven Path Analytic Modeling to Understand Underlying Mechanisms in COVID-19 Survivors Suffering from Long-Term Post-COVID Pain: A Spanish Cohort Study. Pathogens, 11 (11). p. 1336. DOI https://doi.org/10.3390/pathogens11111336
Abstract
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 (β=0.241, p = 0.001). Higher levels of anxiety were associated with greater central sensitization-associated symptoms by a magnitude of β=0.406 (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 (β=0.406, p < 0.001), and greater levels of catastrophizing (β=0.345, 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.
Item Type: | Article |
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Uncontrolled Keywords: | pain; COVID-19; post-COVID; bayesian network; structural equation modeling |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Nov 2022 14:47 |
Last Modified: | 07 Aug 2024 20:13 |
URI: | http://repository.essex.ac.uk/id/eprint/33935 |
Available files
Filename: pathogens-11-01336-v2.pdf
Licence: Creative Commons: Attribution 3.0