Journal Article

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

del-Valle-Loarte, Pablo, Fernández-de-las-Peñas, César, Neblett, Randy, Paras-Bravo, Paula, Herrero-Montes, Manuel, Ferrer-Pargada, Diego, Liew, Bernard X. W., Rodríguez-Rosado, Rafael

2022

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Anxiety / Bayesian analysis / bayesian network / Blacklisting / Care and treatment / Chronic pain / Clinical trials / Cognitive ability / Cohort analysis / Complexity / Coronaviruses / COVID-19 / Data analysis / Data collection / Data mining / Development and progression / Fatigue / Females / Infections / Machine learning / Males / Mathematical models / Mental depression / Methods / Missing data / Modelling / Multivariate statistical analysis / Nervous system / Neuralgia / Pain / Phenotyping / post-COVID / Psychological aspects / Psychological factors / Severe acute respiratory syndrome coronavirus 2 / Sleep / Structural equation modeling / Structural models / Survival / Variables