{"title":"长冠状病毒病(COVID)症状和严重程度评分:开发、验证和应用。","authors":"","doi":"10.1016/j.jval.2024.04.009","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms 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 with long COVID.</p></div><div><h3>Methods</h3><p>A 3-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.</p></div><div><h3>Results</h3><p>The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (r<sub>s</sub> = −0.55), EuroQol visual analog scale (r<sub>s</sub> = −0.368), Patient Health Questionnaire-9 (r<sub>s</sub> = 0.538), Beck Anxiety Inventory (r<sub>s</sub> = 0.689), and Insomnia Severity Index (r<sub>s</sub> = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach’s α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies.</p></div><div><h3>Conclusions</h3><p>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 patients with long COVID.</p></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1098301524023416/pdfft?md5=86160a63dcd23c8386405b2f4c4a4dfc&pid=1-s2.0-S1098301524023416-main.pdf","citationCount":"0","resultStr":"{\"title\":\"The Long COVID Symptoms and Severity Score: Development, Validation, and Application\",\"authors\":\"\",\"doi\":\"10.1016/j.jval.2024.04.009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms 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 with long COVID.</p></div><div><h3>Methods</h3><p>A 3-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.</p></div><div><h3>Results</h3><p>The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (r<sub>s</sub> = −0.55), EuroQol visual analog scale (r<sub>s</sub> = −0.368), Patient Health Questionnaire-9 (r<sub>s</sub> = 0.538), Beck Anxiety Inventory (r<sub>s</sub> = 0.689), and Insomnia Severity Index (r<sub>s</sub> = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach’s α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 distinct severity categories in individuals living with long COVID, providing a basis for personalized treatment strategies.</p></div><div><h3>Conclusions</h3><p>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 patients with long COVID.</p></div>\",\"PeriodicalId\":23508,\"journal\":{\"name\":\"Value in Health\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1098301524023416/pdfft?md5=86160a63dcd23c8386405b2f4c4a4dfc&pid=1-s2.0-S1098301524023416-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Value in Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1098301524023416\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Value in Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1098301524023416","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
The Long COVID Symptoms and Severity Score: Development, Validation, and Application
Objectives
The primary focus of this research is the proposition of a methodological framework for the clinical application of the long COVID symptoms 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 with long COVID.
Methods
A 3-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.
Results
The LC-SSS showed correlations with EuroQol 5-Dimension 5-Level (rs = −0.55), EuroQol visual analog scale (rs = −0.368), Patient Health Questionnaire-9 (rs = 0.538), Beck Anxiety Inventory (rs = 0.689), and Insomnia Severity Index (rs = 0.516), confirming its construct validity. Structural validity was good with a comparative fit index of 0.969, with Cronbach’s α of 0.93 indicating excellent internal consistency. Test-retest reliability was also satisfactory (intraclass correlation coefficient 0.732). K-means clustering identified 3 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 patients with long COVID.
期刊介绍:
Value in Health contains original research articles for pharmacoeconomics, health economics, and outcomes research (clinical, economic, and patient-reported outcomes/preference-based research), as well as conceptual and health policy articles that provide valuable information for health care decision-makers as well as the research community. As the official journal of ISPOR, Value in Health provides a forum for researchers, as well as health care decision-makers to translate outcomes research into health care decisions.