Defining Quality of Life Levels to Enhance Clinical Interpretation in Multiple Sclerosis

Application of a Novel Clustering Method

Bibliographic Data

ID9103682
AuthorsPierre Michel (0000-0002-6442-2566, EA3279 Self-Perceived Health Assessment Research Unit and Department of Public Health, Nord University Hospital, APHM, Aix-Marseille University, corresponding author), Karine Baumstarck (0000-0003-3602-6766, EA3279 Self-Perceived Health Assessment Research Unit and Department of Public Health, Nord University Hospital, APHM, Aix-Marseille University, corresponding author), Laurent Boyer (0000-0002-1375-1706, EA3279 Self-Perceived Health Assessment Research Unit and Department of Public Health, Nord University Hospital, APHM, Aix-Marseille University, corresponding author), Oscar Fernandez (0000-0002-8903-8683, Institute of Clinical Neurosciences, Hospital Regional Universitario Carlos Haya, Málaga, Spain), Peter Flachenecker (Neurological Rehabilitation Center Quellenhof, Bad Wildbad, Germany), Jean Pelletier (0000-0001-9730-7567, Departments of Neurology and CRMBM CNRS6612, Timone University Hospital, APHM, Marseille, France), Anderson Loundou (0000-0003-4301-9476, EA3279 Self-Perceived Health Assessment Research Unit and Department of Public Health, Nord University Hospital, APHM, Aix-Marseille University, corresponding author), Badih Ghattas (0000-0002-6160-9341, Aix-Marseille Université), Pascal Auquier (0000-0003-3496-1996, EA3279 Self-Perceived Health Assessment Research Unit and Department of Public Health, Nord University Hospital, APHM, Aix-Marseille University, corresponding author)
Year2017
Volume55
Issue1
Pagese1-e8
Publication date2017-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000117
PMID24638117
OpenAlexW2326675786
LanguageEN
Citations received2
References cited29

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

Cluster analysis · Hierarchical clustering · Pruning · Quality of life (healthcare) · Amyotrophic Lateral Sclerosis Research · Artificial Intelligence · Cancer survivorship and care · Computer Science · Medicine · Multiple Sclerosis Research Studies

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