Phenotypes in gambling disorder using sociodemographic and clinical clustering analysis : an unidentified new subtype?
Jiménez-Murcia, Susana (Hospital Universitari de Bellvitge)
Granero, Roser (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut)
Fernández-Aranda, Fernando (Universitat de Barcelona. Departament de Ciències Clíniques)
Stinchfield, Randy (University of Minnesota Medical School. Department of Psychiatry)
Tremblay, Joel (Université du Québec à Trois-Rivières. Département de Psychoéducation)
Steward, Trevor (Hospital Universitari de Bellvitge)
Mestre-Bach, Gemma (Hospital Universitari de Bellvitge)
Lozano Madrid, María (Hospital Universitari de Bellvitge)
Mena Moreno, Teresa (Hospital Universitari de Bellvitge)
Mallorquí-Bagué, Núria (Hospital Universitari de Bellvitge)
Perales, José C. (Universidad de Granada. Departamento de Psicología Experimental)
Navas, Juan F.. (Universidad de Granada. Departamento de Psicología Experimental)
Soriano-Mas, Carles (Hospital Universitari de Bellvitge)
Aymamí, Neus (Hospital Universitari de Bellvitge)
Gomez-Peña, Mónica (Hospital Universitari de Bellvitge)
Agüera, Zaida (Hospital Universitari de Bellvitge)
Del Pino-Gutiérrez, Amparo (Universitat de Barcelona)
Martín-Romera, Virginia (Universidad de Alcalá. Departamento de Psicología)
Menchón Magriñá, José Manuel (Hospital Universitari de Bellvitge)

Fecha:	2019
Resumen:	Background: Gambling disorder (GD) is a heterogeneous disorder which has clinical manifestations that vary according to variables in each individual. Considering the importance of the application of specific therapeutic interventions, it is essential to obtain clinical classifications based on differentiated phenotypes for patients diagnosed with GD. Objectives: To identify gambling profiles in a large clinical sample of n = 2,570 patients seeking treatment for GD. Methods: An agglomerative hierarchical clustering method defining a combination of the Schwarz Bayesian Information Criterion and log-likelihood was used, considering a large set of variables including sociodemographic, gambling, psychopathological, and personality measures as indicators. Results: Three-mutually-exclusive groups were obtained. Cluster 1 (n = 908 participants, 35. 5%), labeled as "high emotional distress," included the oldest patients with the longest illness duration, the highest GD severity, and the most severe levels of psychopathology. Cluster 2 (n = 1,555, 60. 5%), labeled as "mild emotional distress," included patients with the lowest levels of GD severity and the lowest levels of psychopathology. Cluster 3 (n = 107, 4. 2%), labeled as "moderate emotional distress," included the youngest patients with the shortest illness duration, the highest level of education and moderate levels of psychopathology. Conclusion: In this study, the general psychopathological state obtained the highest importance for clustering.
Ayudas:	Ministerio de Economía y Competitividad PSI2015-68701-R Instituto de Salud Carlos III PI14/00290 Instituto de Salud Carlos III PI17/01167 Agència de Gestió d'Ajuts Universitaris i de Recerca 2018/FI_B200174
Nota:	Investigación subvencionada por la Delegación del Gobierno para el Plan Nacional sobre Drogas (2017I067)
Nota:	Altres ajuts: MSSSI/18MSP001-2017I067
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	Frontiers in psychiatry, Vol. 10 (2019) , p. 173, ISSN 1664-0640

DOI: 10.3389/fpsyt.2019.00173
PMID: 30984045

10 p, 2.2 MB

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