长冠状病毒病(COVID)症状和严重程度评分:开发、验证和应用。

IF 4.9 2区 医学 Q1 ECONOMICS
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引用次数: 0

摘要

研究目的本研究的主要重点是为长COVID症状和严重程度评分(LC-SSS)的临床应用提出一个方法框架。该工具不仅是一种经过开发和验证的自我报告评估工具,而且还是一种标准化、可量化的手段,用于监测长 COVID 患者经常出现的各种持续性症状。研究方法通过三个阶段来开发、验证和建立 LC-SSS 的评分标准。验证措施包括与其他患者报告措施的相关性、确认性因素分析、内部一致性 Cronbach's α 和测试-再测可靠性。结果 LC-SSS 与 EuroQol 5-Dimension 5-Level 相关(rs = -0.55)、EuroQol 视觉模拟量表(rs = -0.368)、患者健康问卷-9(rs = 0.538)、贝克焦虑量表(rs = 0.689)和失眠严重程度指数(rs = 0.516)相关,证实了其结构效度。结构效度良好,比较拟合指数为 0.969,Cronbach's α 为 0.93,表明内部一致性极佳。重测可靠性也令人满意(类内相关系数为 0.732)。K-means 聚类确定了长时 COVID 患者的 3 个不同严重程度类别,为个性化治疗策略提供了依据。通过 K-means 聚类确定的严重程度类别显示了症状严重程度的显著差异,为个性化治疗提供了依据,并提高了长程 COVID 患者的护理质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Value in Health
Value in Health 医学-卫生保健
CiteScore
6.90
自引率
6.70%
发文量
3064
审稿时长
3-8 weeks
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.
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