Borderline Personality Features in Inpatients with Bipolar Disorder: Impact on Course and Machine Learning Model Use to Predict Rapid Readmission.
Journal
Journal of psychiatric practice
ISSN: 1538-1145
Titre abrégé: J Psychiatr Pract
Pays: United States
ID NLM: 100901141
Informations de publication
Date de publication:
07 2019
07 2019
Historique:
entrez:
11
7
2019
pubmed:
11
7
2019
medline:
20
6
2020
Statut:
ppublish
Résumé
Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD) also met criteria for the other diagnosis. Yet limited data are available concerning the potential impact of co-occurring BPD and/or BPD features on the course or outcome in patients with BD. Therefore, this study examined this comorbidity utilizing the standardized Borderline Personality Questionnaire (BPQ). This study involved 714 adult patients with a primary diagnosis of BD per DSM-IV criteria who were admitted to the psychiatric unit at an academic hospital in Houston, TX between July 2013 and July 2018. All patients completed the BPQ within 72 hours of admission. Statistical analysis was used to detect correlations between severity of BD, length of stay (LOS), and scores on the BPQ. A machine learning model was constructed to predict the parameters affecting patients' readmission rates within 30 days. Analysis revealed that the severity of certain BPD traits at baseline was associated with mood state and outcome measured by LOS. Inpatients with BD who were admitted during acute depressive episodes had significantly higher mean scores on 7 of the 9 BPQ subscales (P<0.05) compared with those admitted during acute manic episodes. Inpatients with BD with greater BPQ scores on 4 of the 9 BPQ subscales had significantly shorter LOS than those with lower BPQ scores (P<0.05). The machine learning model identified 6 variables as predictors for likelihood of 30-day readmission with a high sensitivity (83%), specificity (77%), and area under the receiver operating characteristic curve of 86%. Although preliminary, these results suggest that inpatients with BD who have higher levels of BPD features were more likely to have depressive rather than manic symptoms, fewer psychotic symptoms, and a shorter LOS. Moreover, machine learning models may be particularly valuable in identifying patients with BD who are at the highest risk for adverse consequences including rapid readmission.
Sections du résumé
BACKGROUND
Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD) also met criteria for the other diagnosis. Yet limited data are available concerning the potential impact of co-occurring BPD and/or BPD features on the course or outcome in patients with BD. Therefore, this study examined this comorbidity utilizing the standardized Borderline Personality Questionnaire (BPQ).
METHODS
This study involved 714 adult patients with a primary diagnosis of BD per DSM-IV criteria who were admitted to the psychiatric unit at an academic hospital in Houston, TX between July 2013 and July 2018. All patients completed the BPQ within 72 hours of admission. Statistical analysis was used to detect correlations between severity of BD, length of stay (LOS), and scores on the BPQ. A machine learning model was constructed to predict the parameters affecting patients' readmission rates within 30 days.
RESULTS
Analysis revealed that the severity of certain BPD traits at baseline was associated with mood state and outcome measured by LOS. Inpatients with BD who were admitted during acute depressive episodes had significantly higher mean scores on 7 of the 9 BPQ subscales (P<0.05) compared with those admitted during acute manic episodes. Inpatients with BD with greater BPQ scores on 4 of the 9 BPQ subscales had significantly shorter LOS than those with lower BPQ scores (P<0.05). The machine learning model identified 6 variables as predictors for likelihood of 30-day readmission with a high sensitivity (83%), specificity (77%), and area under the receiver operating characteristic curve of 86%.
CONCLUSIONS
Although preliminary, these results suggest that inpatients with BD who have higher levels of BPD features were more likely to have depressive rather than manic symptoms, fewer psychotic symptoms, and a shorter LOS. Moreover, machine learning models may be particularly valuable in identifying patients with BD who are at the highest risk for adverse consequences including rapid readmission.
Identifiants
pubmed: 31291208
doi: 10.1097/PRA.0000000000000392
pii: 00131746-201907000-00005
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
279-289Références
Wilson ST, Stanley B, Oquendo MA, et al. Comparing impulsiveness, hostility, and depression in borderline personality disorder and bipolar II disorder. J Clin Psychiatry. 2007;68:1533–1539.
Paris J, Gunderson J, Weinberg I. The interface between borderline personality disorder and bipolar spectrum disorders. Compr Psychiatry. 2007;48:145–154.
Benazzi F. Borderline personality disorder and bipolar II disorder in private practice depressed outpatients. Compr Psychiatry. 2000;41:106–110.
Benazzi F. Borderline personality-bipolar spectrum relationship. Prog Neuropsychopharmacol Biol Psychiatry. 2006;30:68–74.
Zimmerman M, Morgan TA. Problematic boundaries in the diagnosis of bipolar disorder: the interface with borderline personality disorder. Curr Psychiatry Rep. 2013;15:422.
Garno JL, Goldberg JF, Ramirez PM, et al. Bipolar disorder with comorbid cluster B personality disorder features: impact on suicidality. J Clin Psychiatry. 2005;66:339–345.
Latalova K, Prasko J, Kamaradova D, et al. Comorbidity bipolar disorder and personality disorders. Neuro Endocrinol Lett. 2013;34:1–8.
Fonseka TM, Swampillai B, Timmins V, et al. Significance of borderline personality-spectrum symptoms among adolescents with bipolar disorder. J Affect Disord. 2015;170:39–45.
Riemann G, Weisscher N, Post RM, et al. The relationship between self-reported borderline personality features and prospective illness course in bipolar disorder. Int J Bipolar Disord. 2017;5:31.
Poreh AM, Rawlings D, Claridge G, et al. The BPQ: a scale for the assessment of borderline personality based on DSM-IV criteria. J Pers Disord. 2006;20:247–260.
Ceylan V, Kose S, Akin E, et al. Normative data and factorial structure of the Turkish version of the Borderline Personality Questionnaire (Turkish BPQ). Psychiat Clin Psych. 2017;27:143–151. DOI: 10.1080/24750573.2017.1298422.
doi: 10.1080/24750573.2017.1298422
Furnham A, Milner R, Akhtar R, et al. A review of the measures designed to assess DSM-5 personality disorders. Psychology. 2014;5:1646–1686. Available at: http://dx.doi.org/10.4236/psych.2014.514175.
doi: 10.4236/psych.2014.514175
Awad A, Bader-El -Den M, McNicholas J. Patient length of stay and mortality prediction: a survey. Health Serv Manage Res. 2017;30:105–120.
Keefler J, Duder S, Lechman C. Predicting length of stay in an acute care hospital: the role of psychosocial problems. Soc Work Health Care. 2001;33:1–16.
Richter D. Psychiatric inpatient length of stay. An overview of methods, influences and consequences. Fortschr Neurol Psychiatr. 2001;69:19–31.
Hoger C, Zieger H, Presting G, et al. Predictors of length of stay in inpatient child and adolescent psychiatry: failure to validate an evidence-based model. Eur Child Adolesc Psychiatry. 2002;11:281–288.
Imai H, Hosomi J, Nakao H, et al. Characteristics of psychiatric hospitals associated with length of stay in Japan. Health Policy. 2005;74:115–121.
Creed F, Tomenson B, Anthony P, et al. Predicting length of stay in psychiatry. Psychol Med. 1997;27:961–966.
Scheytt D, Kaiser P, Priebe S. Duration of treatment and case cost in different inpatient psychiatric facilities in Berlin. Psychiatr Prax. 1996;23:10–14.
Oiesvold T, Saarento O, Sytema S, et al. The Nordic Comparative Study on Sectorized Psychiatry—length of in-patient stay. Acta Psychiatr Scand. 1999;100:220–228.
Huntley DA, Cho DW, Christman J, et al. Predicting length of stay in an acute psychiatric hospital. Psychiatr Serv. 1998;49:1049–1053.
Stevens A, Hammer K, Buchkremer G. A statistical model for length of psychiatric in-patient treatment and an analysis of contributing factors. Acta Psychiatr Scand. 2001;103:203–211.
Pertile R, Donisi V, Grigoletti L, et al. DRGs and other patient-, service- and area-level factors influencing length of stay in acute psychiatric wards: The Veneto Region experience. Soc Psychiatry Psychiatr Epidemiol. 2010;46:651–660.
Blais MA, Matthews J, Lipkis-Orlando R, et al. Predicting length of stay on an acute care medical psychiatric inpatient service. Adm Policy Ment Health. 2003;31:15–29.
Jiménez RE, Lam RM, Marot M, et al. Observed-predicted length of stay for an acute psychiatric department, as an indicator of inpatient care inefficiencies. Retrospective case-series study. BMC Health Serv Res. 2004;4:4.
Ithman MH, Goplarkrishna G, Beck NC, et al. Predictors of length of stay in an acute psychiatric hospital. J Biosafety Health Educ. 2014;2:119. Doi:10.4172/2332-0893.1000119.
doi: 10.4172/2332-0893.1000119
Compton MT, Craw J, Rudisch BE. Determinants of inpatient psychiatric length of stay in an urban county hospital. Psychiatr Q. 2006;77:173–188.
Machado V, Leonidas C, Santos MA, et al. Psychiatric readmission: an integrative review of the literature. Int Nurs Rev. 2012;59:447–457.
Durbin J, Lin E, Layne C, et al. Is readmission a valid indicator of the quality of inpatient psychiatric care. J Behav Health Serv Res. 2007;34:137–150.
Rumball-Smith J, Hider P. The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. NZ Med J. 2009;122:63–70.
Pope HG Jr, Jonas JM, Hudson JI, et al. The validity of DSM-III borderline personality disorder. A phenomenologic, family history, treatment response, and long-term follow-up study. Arch Gen Psychiatry. 1983;40:23–30.
McGlashan TH. The Chestnut Lodge follow-up study. III. Long-term outcome of borderline personalities. Arch Gen Psychiatry. 1986;43:20–30.
Links PS, Steiner M, Offord DR, et al. Characteristics of borderline personality disorder: a Canadian study. Can J Psychiatry. 1988;33:336–340.
Zanarini MC, Frankenburg FR, Dubo ED, et al. Axis I comorbidity of borderline personality disorder. Am J Psychiatry. 1998;155:1733–1739.
Alnæs R, Torgersen S. Personality and personality disorders among patients with various affective disorders. J Pers Disord. 1991;5:107–121.
Hudziak JJ, Boffeli TJ, Kreisman JJ, et al. Clinical study of the relation of borderline personality disorder to Briquet’s syndrome (hysteria), somatization disorder, antisocial personality disorder, and substance abuse disorders. Am J Psychiatry. 1996;153:1598–1606.
Deltito J, Martin L, Riefkohl J, et al. Do patients with borderline personality disorder belong to the bipolar spectrum? J Affect Disord. 2001;67:221–228.
Akiskal HS. Demystifying borderline personality: critique of the concept and unorthodox reflections on its natural kinship with the bipolar spectrum. Acta Psychiatr Scand. 2004;110:401–407.
Axelson DA, Birmaher B, Strober MA, et al. Course of subthreshold bipolar disorder in youth: diagnostic progression from bipolar disorder not otherwise specified. J Am Acad Child Adolesc Psychiatry. 2011;50:1001–1016.e3.
Hafeman D, Axelson D, Demeter C, et al. Phenomenology of bipolar disorder not otherwise specified in youth: a comparison of clinical characteristics across the spectrum of manic symptoms. Bipolar Disord. 2013;15:240–252.
Drake RE, Mercer-McFadden C, Mueser KT, et al. Review of integrated mental health and substance abuse treatment for patients with dual disorders. Schizophr Bull. 1998;24:589–608.
Horsfall J, Cleary M, Hunt GE, et al. Psychosocial treatments for people with co-occurring severe mental illnesses and substance use disorders (dual diagnosis): a review of empirical evidence. Harv Rev Psychiatry. 2009;17:24–34.
Krawczyk N, Feder KA, Saloner B, et al. The association of psychiatric comorbidity with treatment completion among clients admitted to substance use treatment programs in a US national sample. Drug Alcohol Depend. 2017;175:157–163.
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (Text Revision (DSM-IV-TR)), 4th ed. Washington, DC: American Psychiatric Association; 2000.
Chanen AM, Jovev M, Djaja D, et al. Screening for borderline personality disorder in outpatient youth. J Pers Disord. 2008;22:353–364.
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–2410.
Manyika J, Chui M, Brown B, et al. Big data: the next frontier for innovation, competition, and productivity. Technical report. Washington, DC: McKinsey Global Institute; 2011.
Efron B, Hastie T. Computer-Age Statistical Inference. Cambridge, UK: Cambridge University Press; 2016.
Shalev-Shwartz S, Ben-David S. Understanding Machine Learning: From Theory to Algorithms. Cambridge, UK: Cambridge University Press; 2014.
Drysdale AT, Grosenick L, Downar J, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2017;23:28–38.
Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cogn Neurosci Neuroimaging. 2018;3:223–230.