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Characterizing DSM-5 and ICD-11 personality disorder features in psychiatric inpatients at scale using electronic health records

Published online by Cambridge University Press:  23 September 2019

Sergio A. Barroilhet Open the ORCID record for Sergio A. Barroilhet [Opens in a new window] ,
Amelia M. Pellegrini Open the ORCID record for Amelia M. Pellegrini [Opens in a new window] ,
Thomas H. McCoy Open the ORCID record for Thomas H. McCoy [Opens in a new window]  and
Roy H. Perlis Open the ORCID record for Roy H. Perlis [Opens in a new window]
Sergio A. Barroilhet
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA Department of Psychiatry, Tufts University School of Medicine, Boston, MA, USA University Psychiatric Clinic, University of Chile Clinical Hospital, Santiago, Chile
Amelia M. Pellegrini
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Thomas H. McCoy
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
Roy H. Perlis*
Affiliation:
Center for Quantitative Health, Division of Clinical Research and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
*
Author for correspondence: Roy H. Perlis, E-mail: rperlis@partners.org
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Abstract

Background

Investigation of personality traits and pathology in large, generalizable clinical cohorts has been hindered by inconsistent assessment and failure to consider a range of personality disorders (PDs) simultaneously.

Methods

We applied natural language processing (NLP) of electronic health record notes to characterize a psychiatric inpatient cohort. A set of terms reflecting personality trait domains were derived, expanded, and then refined based on expert consensus. Latent Dirichlet allocation was used to score notes to estimate the extent to which any given note reflected PD topics. Regression models were used to examine the relationship of these estimates with sociodemographic features and length of stay.

Results

Among 3623 patients with 4702 admissions, being male, non-white, having a low burden of medical comorbidity, being admitted through the emergency department, and having public insurance were independently associated with greater levels of disinhibition, detachment, and psychoticism. Being female, white, and having private insurance were independently associated with greater levels of negative affectivity. The presence of disinhibition, psychoticism, and negative affectivity were each significantly associated with a longer stay, while detachment was associated with a shorter stay.

Conclusions

Personality features can be systematically and scalably measured using NLP in the inpatient setting, and some of these features associate with length of stay. Developing treatment strategies for patients scoring high in certain personality dimensions may facilitate more efficient, targeted interventions, and may help reduce the impact of personality features on mental health service utilization.

Keywords

Electronic health recordlength of staymachine learningnatural language processingpersonality disorder
Type
Original Articles
Information
Psychological Medicine , Volume 50 , Issue 13 , October 2020 , pp. 2221 - 2229
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Copyright
Copyright © Cambridge University Press 2019

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References

Afshar, M, Phillips, A, Karnik, N, Mueller, J, To, D, Gonzalez, R, Price, R, Cooper, R, Joyce, C and Dligach, D (2019) Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation. Journal of the American Medical Informatics Association: JAMIA 26, 254–261.CrossRefGoogle Scholar
Al-Halabí, S, Herrero, R, Saiz, PA, Garcia-Portilla, MP, Corcoran, P, Teresa Bascaran, M, Errasti, JM, Lemos, S and Bobes, J (2010) Sociodemographic factors associated with personality traits assessed through the TCI. Personality and Individual Differences 48, 809–814.CrossRefGoogle Scholar
Althoff, T, Clark, K and Leskovec, J (2016) Large-scale analysis of counseling conversations: an application of natural language processing to mental health. Transactions of the Association for Computational Linguistics 4, 463–476.CrossRefGoogle Scholar
American Psychiatric Association (2013) Diagnostic and Statistical Manual of Mental Disorders (DSM-5®), 5th Edn. Washington, DC: American Psychiatric Association.Google Scholar
Ashton, MC, Lee, K, Perugini, M, Szarota, P, de Vries, RE, Di Blas, L, Boies, K and De Raad, B (2004) A six-factor structure of personality-descriptive adjectives: solutions from psycholexical studies in seven languages. Journal of Personality and Social Psychology 86, 356–366.CrossRefGoogle Scholar
Ashton, MC, Lee, K, de Vries, RE, Hendrickse, J and Born, MPH (2012) The maladaptive personality traits of the personality inventory for DSM-5 (PID-5) in relation to the HEXACO personality factors and schizotypy/dissociation. Journal of Personality Disorders 26, 641–659.CrossRefGoogle Scholar
Bach, B and First, MB (2018) Application of the ICD-11 classification of personality disorders. BMC Psychiatry 18, 351.CrossRefGoogle Scholar
Bach, B, Sellbom, M and Simonsen, E (2018a) Personality inventory for DSM-5 (PID-5) in clinical versus nonclinical individuals: generalizability of psychometric features. Assessment 25, 815–825.CrossRefGoogle Scholar
Bach, B, Sellbom, M, Skjernov, M and Simonsen, E (2018b) ICD-11 and DSM-5 personality trait domains capture categorical personality disorders: finding a common ground. Australian & New Zealand Journal of Psychiatry 52, 425–434.CrossRefGoogle Scholar
Beckwith, H, Moran, PF and Reilly, J (2014) Personality disorder prevalence in psychiatric outpatients: a systematic literature review. Personality and Mental Health 8, 91–101.CrossRefGoogle Scholar
Birnie, KI, Stewart, R and Kolliakou, A (2018) Recorded atypical hallucinations in psychotic and affective disorders and associations with non-benzodiazepine hypnotic use: the South London and Maudsley Case Register. BMJ Open 8, e025216.CrossRefGoogle Scholar
Bjelland, I, Lie, SA, Dahl, AA, Mykletun, A, Stordal, E and Kraemer, HC (2009) A dimensional versus a categorical approach to diagnosis: anxiety and depression in the HUNT 2 study. International Journal of Methods in Psychiatric Research 18, 128–137.CrossRefGoogle Scholar
Blei, DM (2012) Probabilistic topic models. Communications of the ACM 55, 77–84.CrossRefGoogle Scholar
Blei, DM, Ng, AY and Jordan, MI (2003) Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022.Google Scholar
Can, D, Marín, RA, Georgiou, PG, Imel, ZE, Atkins, DC and Narayanan, SS (2016) ‘It sounds like…’: a natural language processing approach to detecting counselor reflections in motivational interviewing. Journal of Counseling Psychology 63, 343–350.CrossRefGoogle Scholar
Compton, MT, Craw, J and Rudisch, BE (2006) Determinants of inpatient psychiatric length of stay in an urban county hospital. The Psychiatric Quarterly 77, 173–188.CrossRefGoogle Scholar
Dictionary.com, LLC (2019) Thesaurus.com. Long Beach, CA: Lexico Publishing Group.Google Scholar
Fok, ML, Stewart, R, Hayes, RD and Moran, P (2014) The impact of co-morbid personality disorder on use of psychiatric services and involuntary hospitalization in people with severe mental illness. Social Psychiatry and Psychiatric Epidemiology 49, 1631–1640.CrossRefGoogle Scholar
Grilo, CM, McGlashan, TH, Quinlan, DM, Walker, ML, Greenfeld, D and Edell, WS (1998) Frequency of personality disorders in Two Age cohorts of psychiatric inpatients. American Journal of Psychiatry 155, 140–142.CrossRefGoogle Scholar
Grün, B and Hornik, K (2018). Topicmodels, v0.2-8. Available at https://cran.rproject.org/web/packages/topicmodels/index.html.Google Scholar
Habermeyer, B, De Gennaro, H, Frizi, RC, Roser, P and Stulz, N (2018) Factors associated with length of stay in a Swiss mental hospital. The Psychiatric Quarterly 89, 667–674.CrossRefGoogle Scholar
Haslam, N, Holland, E and Kuppens, P (2012) Categories versus dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychological Medicine 42, 903–920.CrossRefGoogle Scholar
Heslin, KC, Elixhauser, A and Steiner, CA (2015). Hospitalizations Involving Mental and Substance Use Disorders Among Adults, 2012. HCUP Statistical Brief #191. Rockville, MD: Agency for Healthcare Research and Quality.Google Scholar
Huprich, SK (2018) Personality pathology in primary care: ongoing needs for detection and intervention. Journal of Clinical Psychology in Medical Settings 25, 43–54.CrossRefGoogle Scholar
Jacobs, R, Gutacker, N, Mason, A, Goddard, M, Gravelle, H, Kendrick, T and Gilbody, S (2015) Determinants of hospital length of stay for people with serious mental illness in England and implications for payment systems: a regression analysis. BMC Health Services Research 15, 439.CrossRefGoogle Scholar
Jiménez, RE, Lam, RM, Marot, M and Delgado, A (2004) Observed-predicted length of stay for an acute psychiatric department, as an indicator of inpatient care inefficiencies. Retrospective case-series study. BMC Health Services Research 4, 4.CrossRefGoogle Scholar
Keown, P, Holloway, F and Kuipers, E (2005) The impact of severe mental illness, co-morbid personality disorders and demographic factors on psychiatric bed use. Social Psychiatry and Psychiatric Epidemiology 40, 42–49.CrossRefGoogle Scholar
Kjelsås, E and Augestad, LB (2004) Gender, eating behavior, and personality characteristics in physically active students. Scandinavian Journal of Medicine & Science in Sports 14, 258–268.CrossRefGoogle Scholar
Kotov, R, Krueger, RF, Watson, D, Achenbach, TM, Althoff, RR, Bagby, RM, Brown, TA, Carpenter, WT, Caspi, A, Clark, LA, Eaton, NR, Forbes, MK, Forbush, KT, Goldberg, D, Hasin, D, Hyman, SE, Ivanova, MY, Lynam, DR, Markon, K, Miller, JD, Moffitt, TE, Morey, LC, Mullins-Sweatt, SN, Ormel, J, Patrick, CJ, Regier, DA, Rescorla, L, Ruggero, CJ, Samuel, DB, Sellbom, M, Simms, LJ, Skodol, AE, Slade, T, South, SC, Tackett, JL, Waldman, ID, Waszczuk, MA, Widiger, TA, Wright, AGC and Zimmerman, M (2017) The Hierarchical Taxonomy of Psychopathology (HiTOP): a dimensional alternative to traditional nosologies. Journal of Abnormal Psychology 126, 454–477.CrossRefGoogle Scholar
Krismayer, T, Schedl, M, Knees, P and Rabiser, R (2019) Predicting user demographics from music listening information. Multimedia Tools and Applications 78, 2897–2920.CrossRefGoogle Scholar
Krueger, RF, Derringer, J, Markon, KE, Watson, D and Skodol, AE (2012) Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychological Medicine 42, 1879–1890.CrossRefGoogle Scholar
Leontieva, L and Gregory, R (2013) Characteristics of patients with borderline personality disorder in a state psychiatric hospital. Journal of Personality Disorders 27, 222–232.CrossRefGoogle Scholar
Lugo, V, de Oliveira, SES, Hessel, CR, Monteiro, RT, Pasche, NL, Pavan, G, Motta, LS, Pacheco, MA and Spanemberg, L (2019) Evaluation of DSM-5 and ICD-11 personality traits using the Personality Inventory for DSM-5 (PID-5) in a Brazilian sample of psychiatric inpatients. Personality and Mental Health 13, 24–39.CrossRefGoogle Scholar
Lynn, R and Martin, T (1997) Gender differences in extraversion, neuroticism, and psychoticism in 37 nations. The Journal of Social Psychology 137, 369–373.CrossRefGoogle Scholar
Manning, CD and Schiitze, H (1999) Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press.Google Scholar
McCallum, AK (2002). ‘MALLET: A Machine Learning for Language Toolkit’. Available at http://mallet.cs.umass.edu.Google Scholar
McCoy, TH Jr, Castro, VM, Roberson, AM, Snapper, LA and Perlis, RH (2016) Improving prediction of suicide and accidental death after discharge from general hospitals with natural language processing. JAMA Psychiatry 73, 1064–1071.CrossRefGoogle Scholar
McCoy, TH, Castro, VM, Snapper, LA, Hart, KH, Januzzi, JL, Huffman, JC and Perlis, RH (2017) Polygenic loading for major depression is associated with specific medical comorbidity. Translational Psychiatry 7, e1238.CrossRefGoogle Scholar
McCoy, TH, Yu, S, Hart, KL, Castro, VM, Brown, HE, Rosenquist, JN, Doyle, AE, Vuijk, PJ, Cai, T and Perlis, RH (2018) High throughput phenotyping for dimensional psychopathology in electronic health records. Biological Psychiatry 83, 997–1004.CrossRefGoogle Scholar
McLay, RN, Daylo, A and Hammer, PS (2005) Predictors of length of stay in a psychiatric ward serving active duty military and civilian patients. Military Medicine 170, 219–222.CrossRefGoogle Scholar
Morey, LC, Shea, MT, Markowitz, JC, Stout, RL, Hopwood, CJ, Gunderson, JG, Grilo, CM, McGlashan, TH, Yen, S, Sanislow, CA and Skodol, AE (2010) State effects of Major depression on the assessment of personality and personality disorder. The American Journal of Psychiatry 167, 528–535.CrossRefGoogle Scholar
Murphy, SN, Weber, G, Mendis, M, Gainer, V, Chueh, HC, Churchill, S and Kohane, I (2010) Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association: JAMIA 17, 124–130.CrossRefGoogle Scholar
Newman, L, Harris, V, Evans, LJ and Beck, A (2018) Factors associated with length of stay in psychiatric inpatient services in London, UK. The Psychiatric Quarterly 89, 33–43.CrossRefGoogle Scholar
Newton-Howes, G, Tyrer, P, Anagnostakis, K, Cooper, S, Bowden-Jones, O and Weaver, T, COSMIC study team (2010) The prevalence of personality disorder, its comorbidity with mental state disorders, and its clinical significance in community mental health teams. Social Psychiatry and Psychiatric Epidemiology 45, 453–460.CrossRefGoogle Scholar
Pauselli, L, Verdolini, N, Bernardini, F, Compton, MT and Quartesan, R (2017) Predictors of length of stay in an inpatient psychiatric unit of a general hospital in Perugia, Italy. The Psychiatric Quarterly 88, 129–140.CrossRefGoogle Scholar
Piccinelli, M, Bortolaso, P, Bolla, E and Cioffi, I (2016) Typologies of psychiatric admissions and length of inpatient stay in Italy. International Journal of Psychiatry in Clinical Practice 20, 116–120.CrossRefGoogle Scholar
Quirk, SE, Berk, M, Chanen, AM, Koivumaa-Honkanen, H, Brennan-Olsen, SL, Pasco, JA and Williams, LJ (2016) Population prevalence of personality disorder and associations with physical health comorbidities and health care service utilization: a review. Personality Disorders: Theory, Research, and Treatment 7, 136–146.CrossRefGoogle Scholar
Řehůřek, R and Sojka, P (2010) Software framework for topic modelling with large corpora. In Proceedings of LREC 2010 Workshop New Challenges for NLP Frameworks. p. 46–50, 5 pp. ISBN 2-9517408-6-7.Google Scholar
Sevilla-Llewellyn-Jones, J, Cano-Domínguez, P, de-Luis-Matilla, A, Peñuelas-Calvo, I, Espina-Eizaguirre, A, Moreno-Kustner, B and Ochoa, S (2017) Personality traits and psychotic symptoms in recent onset of psychosis patients. Comprehensive Psychiatry 74, 109–117.CrossRefGoogle Scholar
Skodol, AE (2012) Personality disorders in DSM-5. Annual Review of Clinical Psychology 8, 317–344.CrossRefGoogle Scholar
Skodol, AE (2018) Can personality disorders be redefined in personality trait terms? American Journal of Psychiatry 175, 590–592.CrossRefGoogle Scholar
Soeteman, DI, Hakkaart-Van Roijen, L, Verheul, R and Van Busschbach, J (2008) The economic burden of personality disorders in mental health care. Journal of Clinical Psychiatry 69, 259–265.CrossRefGoogle Scholar
Stevenson, J, Datyner, A, Boyce, P and Brodaty, H (2011) The effect of age on prevalence, type and diagnosis of personality disorder in psychiatric inpatients. International Journal of Geriatric Psychiatry 26, 981–987.CrossRefGoogle Scholar
Twomey, CD, Baldwin, DS, Hopfe, M and Cieza, A (2015) A systematic review of the predictors of health service utilisation by adults with mental disorders in the UK. BMJ Open 5, e007575.CrossRefGoogle Scholar
Tyrer, P and Simmonds, S (2003) Treatment models for those with severe mental illness and comorbid personality disorder. The British Journal of Psychiatry. Supplement 44, S15–S18.CrossRefGoogle Scholar
Tyrer, P, Reed, GM and Crawford, MJ (2015) Classification, assessment, prevalence, and effect of personality disorder. The Lancet 385, 717–726.CrossRefGoogle Scholar
von Gunten, A, Pocnet, C and Rossier, J (2009) The impact of personality characteristics on the clinical expression in neurodegenerative disorders – a review. Brain Research Bulletin 80, 179–191.CrossRefGoogle Scholar
Widiger, TA (2011) Personality and psychopathology. World Psychiatry 10, 103–106.CrossRefGoogle Scholar
Wright, AGC and Simms, LJ (2015) A metastructural model of mental disorders and pathological personality traits. Psychological Medicine 45, 2309–2319.CrossRefGoogle Scholar
Yim, W-W, Yetisgen, M, Harris, WP and Kwan, SW (2016) Natural language processing in oncology: a review. JAMA Oncology 2, 797–804.CrossRefGoogle Scholar
Yu, S, Kumamaru, KK, George, E, Dunne, RM, Bedayat, A, Neykov, M, Hunsaker, AR, Dill, KE, Cai, T and Rybicki, FJ (2014) Classification of CT pulmonary angiography reports by presence, chronicity, and location of pulmonary embolism with natural language processing. Journal of Biomedical Informatics 52, 386–393.CrossRefGoogle Scholar
Zimmerman, M (2016) Improving the recognition of borderline personality disorder in a bipolar world. Journal of Personality Disorders 30, 320–335.CrossRefGoogle Scholar
Zimmerman, M and Morgan, TA (2013) The relationship between bordersline personality disorder and bipolar disorder. Dialogues in Clinical Neuroscience 15, 155–169.Google Scholar
Zimmerman, M, Rothschild, L and Chelminski, I (2005) The prevalence of DSM-IV personality disorders in psychiatric outpatients. The American Journal of Psychiatry 162, 1911–1918.CrossRefGoogle Scholar
Zimmerman, M, Chelminski, I and Young, D (2008) The frequency of personality disorders in psychiatric patients. Psychiatric Clinics of North America 31, 405–420.CrossRefGoogle Scholar