The Long Coronavirus Disease (COVID) Symptom and Severity Score: Development, Validation, and Application

Gengchen Ye; Yanan Zhu; Wenrui Bao; Heping Zhou; Jiandong Lai; Yuchen Zhang; Juanping Xie; Qingbo Ma; Zhaoyao Luo; Shaohui Ma; Yichu Guo; Xuanting Zhang; Ming Zhang; Xuan Niu

doi:10.1016/j.jval.2024.04.009

The Long Coronavirus Disease (COVID) Symptom and Severity Score: Development, Validation, and Application

Value Health. 2024 Apr 17:S1098-3015(24)02341-6. doi: 10.1016/j.jval.2024.04.009. Online ahead of print.

Authors

Gengchen Ye¹, Yanan Zhu², Wenrui Bao³, Heping Zhou², Jiandong Lai², Yuchen Zhang⁴, Juanping Xie⁵, Qingbo Ma⁶, Zhaoyao Luo¹, Shaohui Ma¹, Yichu Guo¹, Xuanting Zhang¹, Ming Zhang⁷, Xuan Niu⁸

Affiliations

¹ Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, China, Shaanxi Province, Xi'an.
² Medical Imaging Centre, Ankang Central Hospital, China, Shaanxi Province, Ankang.
³ School of Future Technology, Xi'an Jiaotong University, China, Shaanxi Province, Xi'an.
⁴ Department of Nuclear Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, China, Shaanxi Province, Xi'an.
⁵ School of Medicine, Ankang University, China, Shaanxi Province, Ankang.
⁶ Master of Biomedical Engineering (Research-oriented), Ankang Vocational and Technical College, China, Shaanxi Province, Ankang.
⁷ Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, China, Shaanxi Province, Xi'an. Electronic address: zhangming01@mail.xjtu.edu.cn.
⁸ Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, China, Shaanxi Province, Xi'an. Electronic address: niuxuan@xjtu.edu.cn.

PMID: 38641060
DOI: 10.1016/j.jval.2024.04.009

Abstract

Objectives: The primary focus of this research is the proposition of a methodological framework for the clinical application of the long coronavirus disease (COVID) Symptom and Severity Score (LC-SSS). This tool is not just a self-reported assessment instrument developed and validated but serves as a standardized, quantifiable means to monitor the diverse and persistent symptoms frequently observed in individuals suffering from long COVID.

Methods: A three-stage process was used to develop, validate, and establish scoring standards for the LC-SSS. Validation measures included correlations with other patient-reported measures, confirmatory factor analysis, Cronbach's α for internal consistency, and test-retest reliability. Scoring standards were determined using K-means clustering, with comparative assessments made against Hierarchical clustering and the Gaussian Mixture Model (GMM).

Results: The LC-SSS showed correlations with EQ-5D-5L (r_s = -0.55), EQ-VAS (r_s = -0.368), PHQ-9 (r_s = 0.538), BAI (r_s = 0.689), and ISI (r_s = 0.516), confirming its construct validity. Structural validity was good with a CFI of 0.969, with Cronbach's α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (ICC = 0.732). K-means clustering identified three distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies.

Conclusions: The LC-SSS provides a robust and valid tool for assessing long COVID. The severity categories established via K-means clustering demonstrate significant variation in symptom severity, informing personalized treatment and improving care quality for long COVID patients.

Keywords: cluster; long COVID; patient-reported outcome.