Smith Adam B, Greenwood Darren C, Williams Paul, Kwon Joseph, Petrou Stavros, Horton Mike, Osborne Thomas, Milne Ruairidh, Sivan Manoj
Leeds Institute for Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, United Kingdom.
Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Patient Relat Outcome Meas. 2025 Jan 25;16:55-66. doi: 10.2147/PROM.S490870. eCollection 2025.
Long COVID (LC) is a clinical syndrome with persistent, fluctuating symptoms subsequent to COVID-19 infection. LC has significant detrimental effects on health-related quality of life (HRQoL), activities of daily living (ADL), and work productivity. Condition-specific patient-reported outcome measures (PROMs), such as the modified COVID-19 Yorkshire Rehabilitation Scale (C19-YRSm) do not provide the health utility data required for cost-utility analyses of LC interventions. The aim of this study was to derive a mapping algorithm for the C19-YRSm to enable health utilities to be generated from this PROM.
Data were collected from a large study evaluating LC services in the UK. A total of 1434 people with LC had completed both the C19-YRSm and the EQ-5D. Correlation and linear regression analyses were applied to determine items from the C19-YRSm and covariates for inclusion in the algorithm. Model fit, mean differences across the range of EQ-5D-3L utility scores, and Bland-Altman plots were evaluated. Responsiveness (standardised response mean; SRM) of the mapped utilities was investigated on a subset of participants with repeat assessments.
There was a strong level of association between 8 items and one domain on the C19-YRSm with the EQ-5D single-item dimensions. Model fit was good (R = 0.7). The mean difference between observed and mapped scores was <0.10 for the range from 0 to 1 indicating good targeting for positive values of the EQ-5D-3L. The SRM for the mapped EQ-5D-3L was 0.37 compared to 0.17 for the observed utility scores, suggesting the mapped EQ-5D-3L is more responsive to change.
A simple, responsive, and robust mapping algorithm was developed to generate enable EQ-5D-3L health utilities from the C19-YRSm. This will facilitate economic evaluations of LC interventions, treatment, and management, as well as further helping to describe and characterise patients with LC irrespective of any treatment and interventions.
新冠后综合征（LC）是一种在新冠病毒感染后出现持续、波动症状的临床综合征。LC对健康相关生活质量（HRQoL）、日常生活活动（ADL）和工作生产力有显著的不利影响。特定疾病的患者报告结局指标（PROMs），如改良的新冠病毒感染约克郡康复量表（C19 - YRSm），并未提供LC干预措施成本效用分析所需的健康效用数据。本研究的目的是为C19 - YRSm推导一种映射算法，以便能够从该PROM生成健康效用值。
数据来自一项在英国评估LC服务的大型研究。共有1434名LC患者完成了C19 - YRSm和EQ - 5D评估。应用相关性和线性回归分析来确定C19 - YRSm中的项目以及纳入算法的协变量。评估了模型拟合度、EQ - 5D - 3L效用得分范围内的平均差异以及Bland - Altman图。在一部分进行重复评估的参与者中研究了映射效用值的反应性（标准化反应均值；SRM）。
C19 - YRSm上的8个项目和1个领域与EQ - 5D单项维度之间存在很强的关联水平。模型拟合良好（R = 0.7）。对于0到1的范围，观察得分与映射得分之间的平均差异<0.10，表明对EQ - 5D - 3L的正值有良好的针对性。映射的EQ - 5D - 3L的SRM为0.37，而观察到的效用得分的SRM为0.17，这表明映射的EQ - 5D - 3L对变化更敏感。
开发了一种简单、敏感且稳健的映射算法，以便从C19 - YRSm生成EQ - 5D - 3L健康效用值。这将有助于对LC干预措施、治疗和管理进行经济评估，并进一步帮助描述和刻画LC患者，而不考虑任何治疗和干预措施。
