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

被引：2

作者：

Fernandez-de-Las-Penas, Cesar ^{[1
]}

Liew, Bernard X. W. ^{[2
]}

Herrero-Montes, Manuel ^{[3
,4
]}

Del-Valle-Loarte, Pablo ^{[5
]}

Rodriguez-Rosado, Rafael ^{[5
]}

Ferrer-Pargada, Diego ^{[6
]}

Neblett, Randy ^{[7
]}

Paras-Bravo, Paula ^{[3
,4
]}

机构：

[1] Univ Rey Juan Carlos URJC, Dept Phys Therapy Occupat Therapy Phys Med & Reha, Alcorcon 28922, Spain

[2] Univ Essex, Sch Sport Rehabil & Exercise Sci, Colchester CO4 3SQ, Essex, England

[3] Univ Cantabria, Dept Enfermeria, Santander 39008, Spain

[4] Inst Invest Sanitaria Valdecilla IDIVAL, Grp Invest Enfermeria, Santander 39011, Spain

[5] Hosp Univ Severo Ochoa, Dept Internal Med, Leganes 28911, Spain

[6] Hosp Univ Marques de Valdecilla, Serv Neumol, Cantabria 39008, Spain

[7] PRIDE Res Fdn, Dallas, TX 75235 USA

来源：

PATHOGENS | 2022年 / 11卷 / 11期

关键词：

pain; COVID-19; post-COVID; bayesian network; structural equation modeling; CENTRAL SENSITIZATION INVENTORY; VALIDATION; PERCEPTION; SEX/GENDER; VERSION; WOMEN; SCALE;

D O I：

10.3390/pathogens11111336

中图分类号：

Q93 [微生物学];

学科分类号：

071005 ; 100705 ;

摘要：

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 (beta=0.241, p = 0.001). Higher levels of anxiety were associated with greater central sensitization-associated symptoms by a magnitude of beta=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 (beta=0.406, p < 0.001), and greater levels of catastrophizing (beta=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.

引用

页数：12