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

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.

Fig. 1

Consort diagram. EMR, electronic medical records; MDD, major depressive disorder ; TRD, treatment resistant depression.

Fig. 2

Receiver operating characteristic curves for single visit models: (a) depressed; (b) well; (c) cross-validation results for single visit models, specificity fixed at 0.95 (with bootstrapped standard errors shown in parentheses). NLP, natural language processing; AUC, area under the receiver operating characteristic curve.