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Subtypes of borderline personality disorder patients: a cluster-analytic approach
BACKGROUND: The borderline personality disorder (BPD) population is notably heterogeneous, and this has potentially important implications for intervention. Identifying distinct subtypes of patients may represent a first step in identifying which treatments work best for which individuals. METHODS:...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5494904/ https://www.ncbi.nlm.nih.gov/pubmed/28680639 http://dx.doi.org/10.1186/s40479-017-0066-4 |
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author | Smits, Maaike L. Feenstra, Dine J. Bales, Dawn L. de Vos, Jasmijn Lucas, Zwaan Verheul, Roel Luyten, Patrick |
author_facet | Smits, Maaike L. Feenstra, Dine J. Bales, Dawn L. de Vos, Jasmijn Lucas, Zwaan Verheul, Roel Luyten, Patrick |
author_sort | Smits, Maaike L. |
collection | PubMed |
description | BACKGROUND: The borderline personality disorder (BPD) population is notably heterogeneous, and this has potentially important implications for intervention. Identifying distinct subtypes of patients may represent a first step in identifying which treatments work best for which individuals. METHODS: A cluster-analysis on dimensional personality disorder (PD) features, as assessed with the SCID-II, was performed on a sample of carefully screened BPD patients (N = 187) referred for mentalization-based treatment. The optimal cluster solution was determined using multiple indices of fit. The validity of the clusters was explored by investigating their relationship with borderline pathology, symptom severity, interpersonal problems, quality of life, personality functioning, attachment, and trauma history, in addition to demographic and clinical features. RESULTS: A three-cluster solution was retained, which identified three clusters of BPD patients with distinct profiles. The largest cluster (n = 145) consisted of patients characterized by “core BPD” features, without marked elevations on other PD dimensions. A second “Extravert/externalizing” cluster of patients (n = 27) was characterized by high levels of histrionic, narcissistic, and antisocial features. A third, smaller “Schizotypal/paranoid” cluster (n = 15) consisted of patients with marked schizotypal and paranoid features. Patients in these clusters showed theoretically meaningful differences in terms of demographic and clinical features. CONCLUSIONS: Three meaningful subtypes of BPD patients were identified with distinct profiles. Differences were small, even when controlling for severity of PD pathology, suggesting a strong common factor underlying BPD. These results may represent a stepping stone toward research with larger samples aimed at replicating the findings and investigating differential trajectories of change, treatment outcomes, and treatment approaches for these subtypes. TRIAL REGISTRATION: The study was retrospectively registered 16 April 2010 in the Nederlands Trial Register, no. NTR2292. |
format | Online Article Text |
id | pubmed-5494904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54949042017-07-05 Subtypes of borderline personality disorder patients: a cluster-analytic approach Smits, Maaike L. Feenstra, Dine J. Bales, Dawn L. de Vos, Jasmijn Lucas, Zwaan Verheul, Roel Luyten, Patrick Borderline Personal Disord Emot Dysregul Research Article BACKGROUND: The borderline personality disorder (BPD) population is notably heterogeneous, and this has potentially important implications for intervention. Identifying distinct subtypes of patients may represent a first step in identifying which treatments work best for which individuals. METHODS: A cluster-analysis on dimensional personality disorder (PD) features, as assessed with the SCID-II, was performed on a sample of carefully screened BPD patients (N = 187) referred for mentalization-based treatment. The optimal cluster solution was determined using multiple indices of fit. The validity of the clusters was explored by investigating their relationship with borderline pathology, symptom severity, interpersonal problems, quality of life, personality functioning, attachment, and trauma history, in addition to demographic and clinical features. RESULTS: A three-cluster solution was retained, which identified three clusters of BPD patients with distinct profiles. The largest cluster (n = 145) consisted of patients characterized by “core BPD” features, without marked elevations on other PD dimensions. A second “Extravert/externalizing” cluster of patients (n = 27) was characterized by high levels of histrionic, narcissistic, and antisocial features. A third, smaller “Schizotypal/paranoid” cluster (n = 15) consisted of patients with marked schizotypal and paranoid features. Patients in these clusters showed theoretically meaningful differences in terms of demographic and clinical features. CONCLUSIONS: Three meaningful subtypes of BPD patients were identified with distinct profiles. Differences were small, even when controlling for severity of PD pathology, suggesting a strong common factor underlying BPD. These results may represent a stepping stone toward research with larger samples aimed at replicating the findings and investigating differential trajectories of change, treatment outcomes, and treatment approaches for these subtypes. TRIAL REGISTRATION: The study was retrospectively registered 16 April 2010 in the Nederlands Trial Register, no. NTR2292. BioMed Central 2017-07-03 /pmc/articles/PMC5494904/ /pubmed/28680639 http://dx.doi.org/10.1186/s40479-017-0066-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Smits, Maaike L. Feenstra, Dine J. Bales, Dawn L. de Vos, Jasmijn Lucas, Zwaan Verheul, Roel Luyten, Patrick Subtypes of borderline personality disorder patients: a cluster-analytic approach |
title | Subtypes of borderline personality disorder patients: a cluster-analytic approach |
title_full | Subtypes of borderline personality disorder patients: a cluster-analytic approach |
title_fullStr | Subtypes of borderline personality disorder patients: a cluster-analytic approach |
title_full_unstemmed | Subtypes of borderline personality disorder patients: a cluster-analytic approach |
title_short | Subtypes of borderline personality disorder patients: a cluster-analytic approach |
title_sort | subtypes of borderline personality disorder patients: a cluster-analytic approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5494904/ https://www.ncbi.nlm.nih.gov/pubmed/28680639 http://dx.doi.org/10.1186/s40479-017-0066-4 |
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