Hostname: page-component-7c8c6479df-r7xzm Total loading time: 0 Render date: 2024-03-26T14:14:32.978Z Has data issue: false hasContentIssue false

Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model

Published online by Cambridge University Press:  20 June 2011

R. H. Perlis*
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
D. V. Iosifescu
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Mood and Anxiety Disorders Program, Department of Psychiatry, Mount Sinai Hospital, New York, NY, USA
V. M. Castro
Affiliation:
Partners Research Computing, Partners HealthCare System, Boston, MA, USA
S. N. Murphy
Affiliation:
Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
V. S. Gainer
Affiliation:
Partners Research Computing, Partners HealthCare System, Boston, MA, USA
J. Minnier
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
T. Cai
Affiliation:
Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
S. Goryachev
Affiliation:
Partners Research Computing, Partners HealthCare System, Boston, MA, USA
Q. Zeng
Affiliation:
Department of Radiology, Brigham & Women's Hospital, Boston, MA, USA
P. J. Gallagher
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
M. Fava
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
J. B. Weilburg
Affiliation:
Depression Clinic and Research Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
S. E. Churchill
Affiliation:
Information Systems, Partners HealthCare System, Boston, MA, USA
I. S. Kohane
Affiliation:
Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
J. W. Smoller
Affiliation:
Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
*
*Address for correspondence: Dr R. H. Perlis, Simches Research Building, 185 Cambridge St, 6th Floor, Boston, MA 02114, USA (Email: rperlis@partners.org)

Abstract

Background

Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome.

Method

Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard.

Results

Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85–0.88 v. 0.54–0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001).

Conclusions

The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bates, DW, Evans, RS, Murff, H, Stetson, PD, Pizziferri, L, Hripcsak, G (2003). Detecting adverse events using information technology. Journal of the American Medical Informatics Association 10, 115128.CrossRefGoogle ScholarPubMed
Bunea, F, She, Y, Ombao, H, Gongvatana, A, Devlin, K, Cohen, R (2011). Penalized least squares regression methods and applications to neuroimaging. Neuroimage 55, 15191527.CrossRefGoogle Scholar
Charlson, M, Szatrowski, TP, Peterson, J, Gold, J (1994). Validation of a combined comorbidity index. Journal of Clinical Epidemiology 47, 12451251.CrossRefGoogle ScholarPubMed
Charlson, ME, Pompei, P, Ales, KL, MacKenzie, CR (1987). A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. Journal of Chronic Diseases 40, 373383.CrossRefGoogle ScholarPubMed
Effler, P, Ching-Lee, M, Bogard, A, Ieong, MC, Nekomoto, T, Jernigan, D (1999). Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. Journal of the American Medical Association 282, 18451850.CrossRefGoogle ScholarPubMed
Fava, M, Rush, AJ (2006). Current status of augmentation and combination treatments for major depressive disorder: a literature review and a proposal for a novel approach to improve practice. Psychotherapy and Psychosomatics 75, 139153.CrossRefGoogle Scholar
Ferruci, D, Brown, E, Chu-Carroll, J, Fan, J, Gondek, D, Kalyanpur, A, Lally, A, Murdock, JW, Nyberg, E, Prager, J, Schlaefer, N, Welty, C (2010). Building Watson: an overview of the DeepQA project. Artificial Intelligence 5979.Google Scholar
Fournier, JC, DeRubeis, RJ, Hollon, SD, Dimidjian, S, Amsterdam, JD, Shelton, RC, Fawcett, J (2010). Antidepressant drug effects and depression severity: a patient-level meta-analysis. Journal of the American Medical Association 303, 4753.CrossRefGoogle ScholarPubMed
Frank, E, Prien, RF, Jarrett, RB, Keller, MB, Kupfer, DJ, Lavori, PW, Rush, AJ, Weissman, MM (1991). Conceptualization and rationale for consensus definitions of terms in major depressive disorder. Remission, recovery, relapse, and recurrence. Archives of General Psychiatry 48, 851855.CrossRefGoogle ScholarPubMed
Garfield, DA, Rapp, C, Evens, M (1992). Natural language processing in psychiatry. Artificial intelligence technology and psychopathology. Journal of Nervous and Mental Disease 180, 227237.CrossRefGoogle ScholarPubMed
Gibson, TB, Jing, Y, Smith Carls, G, Kim, E, Bagalman, JE, Burton, WN, Tran, QV, Pikalov, A, Goetzel, RZ (2010). Cost burden of treatment resistance in patients with depression. American Journal of Managed Care 16, 370377.Google ScholarPubMed
Guy, W (1976). ECDEU Assessment Manual for Psychopharmacology: US Dept Health Education and Welfare publication (ADM), 76–338, pp. 218222. National Institute of Mental Health: Rockville, MD.Google Scholar
Jakobsen, K, Hansen, T, Dam, H, Larsen, E, Gether, U, Werge, T (2008). Reliability of clinical ICD-10 diagnoses among electroconvulsive therapy patients with chronic affective disorders. European Journal of Psychiatry 22, 167172.Google Scholar
Klompas, M, Haney, G, Church, D, Lazarus, R, Hou, X, Platt, R (2008). Automated identification of acute hepatitis B using electronic medical record data to facilitate public health surveillance. PLoS One 3, e2626.CrossRefGoogle ScholarPubMed
Kroenke, K, Spitzer, RL, Williams, JB (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 16, 606613.CrossRefGoogle ScholarPubMed
Lazarus, R, Klompas, M, Campion, FX, McNabb, SJ, Hou, X, Daniel, J, Haney, G, DeMaria, A, Lenert, L, Platt, R (2009). Electronic support for public health: validated case finding and reporting for notifiable diseases using electronic medical data. Journal of the American Medical Informatics Association 16, 1824.CrossRefGoogle ScholarPubMed
Levin, MA, Krol, M, Doshi, AM, Reich, DL (2007). Extraction and mapping of drug names from free text to a standardized nomenclature. AMIA Annual Symposium Proceedings, pp. 438442.Google Scholar
Meystre, S, Haug, P (2006 a). Improving the sensitivity of the problem list in an intensive care unit by using natural language processing. AMIA Annual Symposium Proceedings, pp. 554558.Google Scholar
Meystre, S, Haug, PJ (2006 b). Natural language processing to extract medical problems from electronic clinical documents: performance evaluation. Journal of Biomedical Informatics 39, 589599.CrossRefGoogle ScholarPubMed
Murphy, SN, Mendis, ME, Hackett, K, Kuttan, R, Pan, W, Phillips, L, Gainer, VS, Berkowicz, D, Glaser, J, Kohane, IS, Chueh, H (2007). Architecture of the open-source clinical research chart from informatics for integrating biology and the bedside. AMIA Annual Symposium Proceedings, pp. 548552.Google Scholar
Nierenberg, AA, Husain, MM, Trivedi, MH, Fava, M, Warden, D, Wisniewski, SR, Miyahara, S, Rush, AJ (2010). Residual symptoms after remission of major depressive disorder with citalopram and risk of relapse: a STAR*D report. Psychological Medicine 40, 4150.CrossRefGoogle ScholarPubMed
Papakostas, GI, Petersen, T, Pava, J, Masson, E, Worthington, JJ 3rd, Alpert, JE, Fava, M, Nierenberg, AA (2003). Hopelessness and suicidal ideation in outpatients with treatment-resistant depression: prevalence and impact on treatment outcome. Journal of Nervous and Mental Disease 191, 444449.CrossRefGoogle ScholarPubMed
Penz, JF, Wilcox, AB, Hurdle, JF (2007). Automated identification of adverse events related to central venous catheters. Journal of Biomedical Informatics 40, 174182.CrossRefGoogle ScholarPubMed
Pestian, JP, Matykiewicz, P, Grupp-Phelan, J, Lavanier, SA, Combs, J, Kowatch, R (2008). Using natural language processing to classify suicide notes. Annual Symposium Proceedings of the American Medical Informatics Association, 6 November 2008. Abstract 1091.Google Scholar
Rush, AJ, Kraemer, HC, Sackeim, HA, Fava, M, Trivedi, MH, Frank, E, Ninan, PT, Thase, ME, Gelenberg, AJ, Kupfer, DJ, Regier, DA, Rosenbaum, JF, Ray, O, Schatzberg, AF (2006). Report by the ACNP Task Force on response and remission in major depressive disorder. Neuropsychopharmacology 31, 18411853.CrossRefGoogle ScholarPubMed
Rush, AJ, Thase, ME, Dube, S (2003 a). Research issues in the study of difficult-to-treat depression. Biological Psychiatry 53, 743753.CrossRefGoogle Scholar
Rush, AJ, Trivedi, MH, Ibrahim, HM, Carmody, TJ, Arnow, B, Klein, DN, Markowitz, JC, Ninan, PT, Kornstein, S, Manber, R, Thase, ME, Kocsis, JH, Keller, MB (2003 b). The 16-Item Quick Inventory of Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biological Psychiatry 54, 573583.CrossRefGoogle ScholarPubMed
Simon, GE, Perlis, RH (2010). Personalized medicine for depression: can we match patients with treatments? American Journal of Psychiatry 167, 14451455.CrossRefGoogle ScholarPubMed
Solti, I, Aaronson, B, Fletcher, G, Solti, M, Gennari, JH, Cooper, M, Payne, T (2008). Building an automated problem list based on natural language processing: lessons learned in the early phase of development. AMIA Annual Symposium Proceedings 687691.Google Scholar
Trivedi, MH, Fava, M, Wisniewski, SR, Thase, ME, Quitkin, F, Warden, D, Ritz, L, Nierenberg, AA, Lebowitz, BD, Biggs, MM, Luther, JF, Shores-Wilson, K, Rush, AJ (2006). Medication augmentation after the failure of SSRIs for depression. New England Journal of Medicine 354, 12431252.CrossRefGoogle ScholarPubMed
Trivedi, MH, Rush, AJ, Ibrahim, HM, Carmody, TJ, Biggs, MM, Suppes, T, Crismon, ML, Shores-Wilson, K, Toprac, MG, Dennehy, EB, Witte, B, Kashner, TM (2004). The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation. Psychological Medicine 34, 7382.CrossRefGoogle ScholarPubMed
Turchin, A, Morin, L, Semere, LG, Kashyap, V, Palchuk, MB, Shubina, M, Chang, F, Li, Q (2006). Comparative evaluation of accuracy of extraction of medication information from narrative physician notes by commercial and academic natural language processing software packages. AMIA Annual Symposium Proceedings 789793.Google Scholar
Zeng, QT, Goryachev, S, Weiss, S, Sordo, M, Murphy, SN, Lazarus, R (2006). Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Medical Informatics and Decision Making 6, 30.CrossRefGoogle ScholarPubMed
Zou, H (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 14181429.CrossRefGoogle Scholar
Supplementary material: File

Perlis Supplementary Material

Perlis Supplementary Material

Download Perlis Supplementary Material(File)
File 1.5 MB