Pierre Michel1, Karine Baumstarck, Laurent Boyer, Oscar Fernandez, Peter Flachenecker, Jean Pelletier, Anderson Loundou, Badih Ghattas, Pascal Auquier. 1. *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.
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.
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 MSpatients.
Authors: Pierre Michel; Pascal Auquier; Karine Baumstarck; Anderson Loundou; Badih Ghattas; Christophe Lançon; Laurent Boyer Journal: Qual Life Res Date: 2015-04-09 Impact factor: 4.147