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Defining Quality of Life Levels to Enhance Clinical Interpretation in Multiple Sclerosis: Application of a Novel Clustering Method

Michel, Pierre PhD; Baumstarck, Karine MD, PhD; Boyer, Laurent MD, PhD; Fernandez, Oscar MD, PhD; Flachenecker, Peter MD, PhD; Pelletier, Jean MD, PhD; Loundou, Anderson PhD; Ghattas, Badih PhD; Auquier, Pascal MD, PhDon behalf of the MusiQoL Study Group

doi: 10.1097/MLR.0000000000000117
Applied Methods

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.

Supplemental Digital Content is available in the text.

*EA3279 Self-Perceived Health Assessment Research Unit and Department of Public Health, Nord University Hospital, APHM, Aix-Marseille University

Department of Mathematics, Faculté des Sciences de Luminy, Aix-Marseille University, Marseille, France

Institute of Clinical Neurosciences, Hospital Regional Universitario Carlos Haya, Málaga, Spain

§Neurological Rehabilitation Center Quellenhof, Bad Wildbad, Germany

Departments of Neurology and CRMBM CNRS6612, Timone University Hospital, APHM, Marseille, France

Supported by Merck Serono SA—Geneva, Switzerland, a branch of Merck Serono SA, Coinsins, Switzerland, an affiliate of Merck KGaA, Darmstadt, Germany.

The authors declare no conflict of interest.

Reprints: Karine Baumstarck, MD, PhD, Aix-Marseille University, EA 3279—Public Health, Chronic Diseases and Quality of Life—Research Unit, Marseille 13284, France. E-mail:

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