Suppr超能文献

将 PROMIS 物理功能和疼痛干扰映射到改良的下腰痛残疾问卷。

Mapping PROMIS physical function and pain interference to the modified low back pain disability questionnaire.

机构信息

Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Avenue, JJN3-1, Cleveland, OH, 44195, USA.

Center for Outcomes Research and Evaluation, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.

出版信息

Qual Life Res. 2022 Dec;31(12):3467-3482. doi: 10.1007/s11136-022-03174-3. Epub 2022 Jul 6.

Abstract

PURPOSE

The Modified Low Back Pain Disability Questionnaire (MDQ) is a commonly used tool to assess functioning of patients with low back pain (LBP). Recently, the Patient-Reported Outcomes Measurement Information System (PROMIS) was suggested as an alternative platform to assess LBP patient-reported health. We sought to map between the MDQ and PROMIS Physical Function (PROMIS-PF) and Pain Interference (PROMIS-PI) scales using multiple methods.

METHODS

In a retrospective analysis of LBP patients seen at Cleveland Clinic 11/14/18-12/11/19, T-scores from each PROMIS scale were mapped to MDQ total score individually and together. MDQ item and total scores were mapped to each PROMIS scale. Linear regression as well as linear and equipercentile equating were used. Split sample internal validation using root mean squared error (RMSE), mean absolute error (MAE), and correlations were used to assess accuracy of mapping equations.

RESULTS

13585 patients completed the three scales. In the derivation cohort, average age was 59.0 (SD = 15.8); 53.3% female and 82.9% white. Average MDQ total, PROMIS-PF, and PROMIS-PI T-scores were 40.3 (SD = 19.0), 37.2 (SD = 7.6), and 62.9 (SD = 7.2), respectively. For estimating MDQ total scores, methods that used both PROMIS-PF and PROMIS-PI had closest estimated means, lowest RMSE and MAE, and highest correlations. For estimating each of PROMIS-PF and PROMIS-PI T-scores, the best performing method was equipercentile equating using the MDQ items.

CONCLUSIONS

We created and internally validated maps between MDQ and PROMIS-PF and PROMIS-PI using linear regression, linear and equipercentile equating. Our equations can be used by researchers wishing to translate scores between these scales.

摘要

目的

改良下腰痛残疾问卷(MDQ)是一种常用于评估腰痛(LBP)患者功能的工具。最近,患者报告的结果测量信息系统(PROMIS)被建议作为评估 LBP 患者报告健康的替代平台。我们试图使用多种方法将 MDQ 与 PROMIS 身体功能(PROMIS-PF)和疼痛干扰(PROMIS-PI)量表进行映射。

方法

在克利夫兰诊所 2018 年 11 月 14 日至 2019 年 12 月 11 日就诊的 LBP 患者的回顾性分析中,单独和共同将每个 PROMIS 量表的 T 分数映射到 MDQ 总分。将 MDQ 项目和总分映射到每个 PROMIS 量表。使用线性回归以及线性和等百分位等价法。使用均方根误差(RMSE)、平均绝对误差(MAE)和相关性进行拆分样本内部验证,以评估映射方程的准确性。

结果

13585 名患者完成了这三个量表。在推导队列中,平均年龄为 59.0(SD=15.8);53.3%为女性,82.9%为白人。平均 MDQ 总分、PROMIS-PF 和 PROMIS-PI T 分数分别为 40.3(SD=19.0)、37.2(SD=7.6)和 62.9(SD=7.2)。对于估计 MDQ 总分,使用 PROMIS-PF 和 PROMIS-PI 的方法具有最接近的估计平均值、最低的 RMSE 和 MAE,以及最高的相关性。对于估计每个 PROMIS-PF 和 PROMIS-PI T 分数,表现最佳的方法是使用 MDQ 项目进行等百分位等价线性等价。

结论

我们使用线性回归、线性和等百分位等价法创建并内部验证了 MDQ 与 PROMIS-PF 和 PROMIS-PI 之间的映射。希望在这些量表之间转换分数的研究人员可以使用我们的方程。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验