背景:健康相关生活质量(HRQL)已成为心脏病学的重要结果参数。MOS36-Item简式健康调查(SF-36)和PROMIS-29是两种广泛使用的通用指标,可提供综合HRQL评分。SF-36是一种使用了几十年的成熟仪器,可以汇总为身体(PCS)和心理(MCS)组件汇总分数。还提出了用于相关分量得分(PCSc和MCSc)的替代评分算法。PROMIS-29是一种较新但越来越多地使用的HRQL措施。类似于SF-36,身体和心理健康总结得分可以从PROMIS-29领域得分得出,基于相关因子解。到目前为止,PROMIS-29的分数不能直接与SF-36的结果相当,使研究结果的汇总复杂化。因此,我们的目标是提供将PROMIS-29数据转换为完善的SF-36分量汇总分数的算法.
方法:来自柏林血管事件长期观察(BeLOVE)研究的n=662名参与者的数据用于估计线性回归模型,其中PROMIS-29领域得分或汇总的PROMIS-29身体/心理健康汇总得分作为预测因子,SF-36身体/心理成分汇总得分作为结果。来自随后的评估点(n=259)的数据用于评估经验和预测的SF-36评分之间的一致性。
结果:PROMIS-29领域得分以及PROMIS-29健康总结得分对PCS显示出较高的预测价值,PCSc,和MCSc(R2≥70%),和MCS的中等预测值(R2=57%,R2=40%,分别)。将回归系数应用于新数据后,大多数模型的经验和预测的SF-36分量汇总得分高度相关(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|<0.1)。
结论:这项研究提供了易于应用的算法,可以将PROMIS-29数据转换为心血管人群中完善的SF-36身体和心理成分汇总得分。应用于新数据,经验和预测的SF-36分数之间的一致性很高.然而,对于SF-36心理成分汇总分数,与原始因子模型(MCS)相比,在相关模型(MCSc)下发现的预测要好得多。此外,作为相关的副产品,我们的研究证实了在心脏病患者中相对较新的PROMIS-29健康总结评分的结构效度.
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.