Lemanska Agnieszka, Chen Tao, Dearnaley David P, Jena Rajesh, Sydes Matthew R, Faithfull Sara
Faculty of Health and Medical Sciences, School of Health Sciences, University of Surrey, Guildford, UK.
Department of Chemical and Process Engineering, University of Surrey, Guildford, UK.
Qual Life Res. 2017 Aug;26(8):2103-2116. doi: 10.1007/s11136-017-1548-y. Epub 2017 Mar 28.
To investigate the role of symptom clusters in the analysis and utilisation of patient reported outcome measures (PROMs) for data modelling and clinical practice. To compare symptom clusters with scales, and to explore their value in PROMs interpretation and symptom management.
A dataset called RT01 (ISCRTN47772397) of 843 prostate cancer patients was used. PROMs were reported with the University of California, Los Angeles Prostate Cancer Index (UCLA-PCI). Symptom clusters were explored with hierarchical cluster analysis (HCA) and average linkage method (correlation > 0.6). The reliability of the Urinary Function Scale was evaluated with Cronbach's Alpha. The strength of the relationship between the items was investigated with Spearman's correlation. Predictive accuracy of the clusters was compared to the scales by receiver operating characteristic (ROC) analysis. Presence of urinary symptoms at 3 years measured with the late effects on normal tissue: subjective, objective, management tool (LENT/SOM) was an endpoint.
Two symptom clusters were identified (urinary cluster and sexual cluster). The grouping of symptom clusters was different than UCLA-PCI Scales. Two items of the urinary function scales ("number of pads" and "urinary leak interfering with sex") were excluded from the urinary cluster. The correlation with the other items in the scale ranged from 0.20 to 0.21 and 0.31 to 0.39, respectively. Cronbach's Alpha showed low correlation of those items with the Urinary Function Scale (0.14-0.36 and 0.33-0.44, respectively). All urinary function scale items were subject to a ceiling effect. Clusters had better predictive accuracy, AUC = 0.70 -0.65, while scales AUC = 0.67-0.61.
This study adds to the knowledge on how cluster analysis can be applied for the interpretation and utilisation of PROMs. We conclude that multiple-item scales should be evaluated and that symptom clusters provide a study-specific approach for modelling and interpretation of PROMs.
探讨症状群在患者报告结局测量指标（PROMs）的分析及用于数据建模和临床实践中的作用。比较症状群与量表，并探究它们在PROMs解释及症状管理中的价值。
使用了一个名为RT01（ISCRTN47772397）的数据集，其中包含843例前列腺癌患者。PROMs通过加利福尼亚大学洛杉矶分校前列腺癌指数（UCLA-PCI）进行报告。采用层次聚类分析（HCA）和平均连锁法（相关性>0.6）探索症状群。用Cronbach's Alpha评估尿功能量表的信度。用Spearman相关性研究各条目之间关系的强度。通过受试者工作特征（ROC）分析比较症状群与量表的预测准确性。以正常组织晚期效应：主观、客观、管理工具（LENT/SOM）测量的3年时泌尿症状的存在情况作为一个终点。
识别出两个症状群（泌尿症状群和性症状群）。症状群的分组与UCLA-PCI量表不同。尿功能量表的两个条目（“尿垫数量”和“影响性生活的尿失禁”）被排除在泌尿症状群之外。它们与量表中其他条目的相关性分别为0.20至0.21和0.31至0.39。Cronbach's Alpha显示这些条目与尿功能量表的相关性较低（分别为0.14 - 0.36和0.33 - 0.44）。所有尿功能量表条目均存在天花板效应。症状群具有更好的预测准确性，曲线下面积（AUC）=0.70 - 0.65，而量表的AUC =0.67 - 0.61。
本研究增加了关于如何将聚类分析应用于PROMs的解释和利用的知识。我们得出结论，应评估多条目量表，并且症状群为PROMs的建模和解释提供了一种针对特定研究的方法。
