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

COVID-19 bayesian network pain post-COVID structural equation modeling

Journal

Pathogens (Basel, Switzerland)
ISSN: 2076-0817
Titre abrégé: Pathogens
Pays: Switzerland
ID NLM: 101596317

Informations de publication

Date de publication:
12 Nov 2022
Historique:
received: 23 10 2022
revised: 05 11 2022
accepted: 10 11 2022
entrez: 24 11 2022
pubmed: 25 11 2022
medline: 25 11 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 36422588
pii: pathogens11111336
doi: 10.3390/pathogens11111336
pmc: PMC9696487
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Comunidad de Madrid
ID : LONG COVID EXP CM
Organisme : Fundación Marques de Valdecilla
ID : Next-Val 2021

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Auteurs

César Fernández-de-Las-Peñas (C)

Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Universidad Rey Juan Carlos (URJC), 28922 Alcorcón, Spain.

Bernard X W Liew (BXW)

School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester CO4 3SQ, UK.

Manuel Herrero-Montes (M)

Departamento de Enfermería, Universidad de Cantabria, 39008 Santander, Spain.
Grupo de Investigación en Enfermería, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain.

Pablo Del-Valle-Loarte (P)

Department of Internal Medicine, Hospital Universitario Severo Ochoa, 28911 Leganes, Spain.

Rafael Rodríguez-Rosado (R)

Department of Internal Medicine, Hospital Universitario Severo Ochoa, 28911 Leganes, Spain.

Diego Ferrer-Pargada (D)

Servicio de Neumología, Hospital Universitario Marqués de Valdecilla, 39008 Cantabria, Spain.

Randy Neblett (R)

PRIDE Research Foundation, Dallas, TX 75235, USA.

Paula Paras-Bravo (P)

Departamento de Enfermería, Universidad de Cantabria, 39008 Santander, Spain.
Grupo de Investigación en Enfermería, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), 39011 Santander, Spain.

Classifications MeSH