Classification of patients with bipolar disorder using k-means clustering

• Published in: PloS one Vol. 14; no. 1; p. e0210314
• Main Authors: Fuente-Tomas, Lorena de la, Arranz, Belen, Safont, Gemma, Sierra, Pilar, Sanchez-Autet, Monica, Garcia-Blanco, Ana, Garcia-Portilla, Maria P.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.01.2019; Public Library of Science (PLoS)
• Subjects: Adult; Anthropometry; Biology and Life Sciences; Biomarkers; Bipolar disorder; Bipolar Disorder - classification; Bipolar Disorder - diagnosis; Bipolar Disorder - psychology; Body mass; Body mass index; Body size; Care and treatment; Classification; Cluster Analysis; Clustering; Clustering (Computers); Cognition; Cognition & reasoning; Cognitive ability; Cross-Sectional Studies; Decision making; Demography; Domains; Drug therapy; Female; Hospitals; Humans; Illnesses; Male; Medicine and Health Sciences; Mental disorders; Mental health; Metabolic disorders; Metabolic syndrome; Middle Aged; Patient outcomes; Patients; Pharmacology; Precision Medicine; Profilers; Psychiatry; Psychometrics; Psychopathology; Quality of life; Reproducibility of Results; Risk factors; Severity of Illness Index; Shared decision making; Social Sciences; Spain; Statistical analysis; Suicide; Temporal perception; Vector quantization; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0210314

Bipolar disorder (BD) is a heterogeneous disorder needing personalized and shared decisions. We aimed to empirically develop a cluster-based classification that allocates patients according to their severity for helping clinicians in these processes. Naturalistic, cross-sectional, multicenter study. We included 224 subjects with BD (DSM-IV-TR) under outpatient treatment from 4 sites in Spain. We obtained information on socio-demography, clinical course, psychopathology, cognition, functioning, vital signs, anthropometry and lab analysis. Statistical analysis: k-means clustering, comparisons of between-group variables, and expert criteria. We obtained 12 profilers from 5 life domains that classified patients in five clusters. The profilers were: Number of hospitalizations and of suicide attempts, comorbid personality disorder, body mass index, metabolic syndrome, the number of comorbid physical illnesses, cognitive functioning, being permanently disabled due to BD, global and leisure time functioning, and patients' perception of their functioning and mental health. We obtained preliminary evidence on the construct validity of the classification: (1) all the profilers behaved correctly, significantly increasing in severity as the severity of the clusters increased, and (2) more severe clusters needed more complex pharmacological treatment. We propose a new, easy-to-use, cluster-based severity classification for BD that may help clinicians in the processes of personalized medicine and shared decision-making.