Converting PROMIS®-29 v2.0 profile data to SF-36 physical and mental component summary scores in patients with cardiovascular disorders.

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Gregor Liegl, Felix H Fischer, Carl N Martin, Maria Rönnefarth, Annelie Blumrich, Michael Ahmadi, Leif-Hendrik Boldt, Kai-Uwe Eckardt, Matthias Endres, Frank Edelmann, Holger Gerhardt, Ulrike Grittner, Arash Haghikia, Norbert Hübner, Ulf Landmesser, David Leistner, Knut Mai, Jil Kollmus-Heege, Dominik N Müller, Christian H Nolte, Sophie K Piper, Kai M Schmidt-Ott, Tobias Pischon, Simrit Rattan, Ira Rohrpasser-Napierkowski, Katharina Schönrath, Jeanette Schulz-Menger, Oliver Schweizerhof, Joachim Spranger, Joachim E Weber, Martin Witzenrath, Sein Schmidt, Matthias Rose
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引用次数: 0

Abstract

Background: Health-related quality of life (HRQL) has become an important outcome parameter in cardiology. The MOS 36-ltem Short-Form Health Survey (SF-36) and the PROMIS-29 are two widely used generic measures providing composite HRQL scores. The domains of the SF-36, a well-established instrument utilized for several decades, can be aggregated to physical (PCS) and mental (MCS) component summary scores. Alternative scoring algorithms for correlated component scores (PCSc and MCSc) have also been suggested. The PROMIS-29 is a newer but increasingly used HRQL measure. Analogous to the SF-36, physical and mental health summary scores can be derived from PROMIS-29 domain scores, based on a correlated factor solution. So far, scores from the PROMIS-29 are not directly comparable to SF-36 results, complicating the aggregation of research findings. Thus, our aim was to provide algorithms to convert PROMIS-29 data to well-established SF-36 component summary scores.

Methods: Data from n = 662 participants of the Berlin Long-term Observation of Vascular Events (BeLOVE) study were used to estimate linear regression models with either PROMIS-29 domain scores or aggregated PROMIS-29 physical/mental health summary scores as predictors and SF-36 physical/mental component summary scores as outcomes. Data from a subsequent assessment point (n = 259) were used to evaluate the agreement between empirical and predicted SF-36 scores.

Results: PROMIS-29 domain scores as well as PROMIS-29 health summary scores showed high predictive value for PCS, PCSc, and MCSc (R2 ≥ 70%), and moderate predictive value for MCS (R2 = 57% and R2 = 40%, respectively). After applying the regression coefficients to new data, empirical and predicted SF-36 component summary scores were highly correlated (r > 0.8) for most models. Mean differences between empirical and predicted scores were negligible (|SMD|<0.1).

Conclusions: This study provides easy-to-apply algorithms to convert PROMIS-29 data to well-established SF-36 physical and mental component summary scores in a cardiovascular population. Applied to new data, the agreement between empirical and predicted SF-36 scores was high. However, for SF-36 mental component summary scores, considerably better predictions were found under the correlated (MCSc) than under the original factor model (MCS). Additionally, as a pertinent byproduct, our study confirmed construct validity of the relatively new PROMIS-29 health summary scores in cardiology patients.

将心血管疾病患者的 PROMIS®-29 v2.0 资料数据转换为 SF-36 身心部分总分。
背景:健康相关生活质量(HRQL)已成为心脏病学的一个重要结果参数。MOS 36-ltem 短式健康调查(SF-36)和 PROMIS-29 是两种广泛使用的通用测量方法,可提供综合 HRQL 分数。SF-36 是一种使用了几十年的成熟工具,其各领域可汇总为身体(PCS)和精神(MCS)部分的总分。也有人提出了相关成分分数(PCSc 和 MCSc)的替代评分算法。PROMIS-29 是一种较新的 HRQL 测量方法,但使用得越来越多。与 SF-36 类似,根据相关因子解决方案,可从 PROMIS-29 领域得分中得出身体和心理健康总分。到目前为止,PROMIS-29 的得分还不能直接与 SF-36 的结果进行比较,这使得研究结果的汇总变得更加复杂。因此,我们的目标是提供一种算法,将 PROMIS-29 数据转换为成熟的 SF-36 成分总分:方法:使用柏林血管事件长期观察(BeLOVE)研究的 n = 662 名参与者的数据来估计线性回归模型,以 PROMIS-29 领域得分或综合 PROMIS-29 身体/心理健康汇总得分作为预测因素,以 SF-36 身体/心理组件汇总得分作为结果。后续评估点的数据(n = 259)用于评估经验分数与预测 SF-36 分数之间的一致性:结果:PROMIS-29 领域得分和 PROMIS-29 健康总分对 PCS、PCSc 和 MCSc 有较高的预测价值(R2 ≥ 70%),对 MCS 有中等的预测价值(R2 = 57% 和 R2 = 40%)。将回归系数应用于新数据后,在大多数模型中,经验值和预测值的 SF-36 成分总分高度相关(r > 0.8)。经验分数和预测分数之间的平均差异可以忽略不计(|SMD|结论:本研究提供了易于应用的算法,可将 PROMIS-29 数据转换为心血管人群中成熟的 SF-36 身体和精神部分总分。应用于新数据时,经验分数和预测 SF-36 分数之间的一致性很高。然而,对于 SF-36 精神部分的总分,在相关模型(MCSc)下的预测结果要比在原始因子模型(MCS)下的预测结果好得多。此外,作为一项相关的副产品,我们的研究证实了相对较新的 PROMIS-29 健康总分在心脏病患者中的构建有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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