Defining Quality of Life Levels to Enhance Clinical Interpretation in Multiple Sclerosis: Application of a Novel Clustering Method. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Michel, Pierre; Baumstarck, Karine; Boyer, Laurent; Fernandez, Oscar; Flachenecker, Peter; Pelletier, Jean; Loundou, Anderson; Ghattas, Badih; Auquier, Pascal
• Is Part Of: Medical care. Volume 55:Issue 1(2017)
• Publication Date: 2017-01
• Subjects: multiple sclerosis -- quality of life -- unsupervised classification -- clustering -- MusiQoL -- SF-36.; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000000117
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Background: To enhance the use of quality of life (QoL) measures in clinical practice, it is pertinent to help clinicians interpret QoL scores. Objective: The aim of this study was to define clusters of QoL levels from a specific questionnaire (MusiQoL) for multiple sclerosis (MS) patients using a new method of interpretable clustering based on unsupervised binary trees and to test the validity regarding clinical and functional outcomes. Methods: In this international, multicenter, cross-sectional study, patients with MS were classified using a hierarchical top-down method of Clustering using Unsupervised Binary Trees. The clustering tree was built using the 9 dimension scores of the MusiQoL in 2 stages, growing and tree reduction (pruning and joining). A 3-group structure was considered, as follows: "high, " "moderate, " and "low" QoL levels. Clinical and QoL data were compared between the 3 clusters. Results: A total of 1361 patients were analyzed: 87 were classified with "low, " 1173 with "moderate, " and 101 with "high" QoL levels. The clustering showed satisfactory properties, including repeatability (using bootstrap) and discriminancy (using factor analysis). The 3 clusters consistently differentiated patients based on sociodemographic and clinical characteristics, and the QoL scores were assessed using a generic questionnaire, ensuring the clinical validity of the clustering. Conclusions: The study suggests that Clustering using Unsupervised Binary Trees is an original, innovative, and relevant classification method to define clusters of QoL levels in MS patients. Abstract : Supplemental Digital Content is available in the text.