Citation

McCoy TH, Pellegrini AM, Perlis RH. Depress. Anxiety 2019; 36(5): 392-399.

Affiliation

Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.

Copyright

(Copyright © 2019, John Wiley and Sons)

DOI

10.1002/da.22882

PMID

30710497

Abstract

BACKGROUND: Identification of individuals at increased risk for suicide is an important public health priority, but the extent to which considering clinical phenomenology improves prediction of longer term outcomes remains understudied. Hospital discharge provides an opportunity to stratify risk using readily available clinical records and details.

METHODS: We applied a validated natural language processing tool to generate estimated Research Domain Criteria (RDoC) scores for a cohort of 444,317 individuals drawn from 815,457 hospital discharges between 2005 and 2013. We used survival analysis to examine the association of this risk with suicide and accidental death, adjusted for sociodemographic features.

RESULTS: In adjusted models, symptoms in each of the five domains contributed to incremental risk (log rank P < 0.001), with greatest increase observed with positive valence. The contribution of each domain to risk was time dependent.

CONCLUSIONS: RDoC symptom scores parsed from clinical documentation are associated with suicide and illustrates that multiple domains contribute to risk in a time-varying fashion.

© 2019 Wiley Periodicals, Inc.

Language: en

Keywords

Research Domain Criteria; accidental death; electronic health records; natural language processing; suicide; survival analysis