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Gen Hosp Psychiatry. 2017 May;46:1-6. doi: 10.1016/j.genhosppsych.2017.01.006. Epub 2017 Jan 26.

Characterizing and predicting rates of delirium across general hospital settings.

Author information

1
Center for Quantitative Health, Division of Clinical Research, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, United States; Avery D. Weisman Psychiatry Consultation Service, Massachusetts General Hospital, Warren Building 6th Floor, 55 Fruit St, Boston, MA 02114, United States. Electronic address: thmccoy@partners.org.
2
Center for Quantitative Health, Division of Clinical Research, Massachusetts General Hospital, Simches Research Building 6th Floor, 185 Cambridge St, Boston, MA 20114, United States.

Abstract

OBJECTIVE:

To better understand variation in reported rates of delirium, this study characterized delirium occurrence rate by department of service and primary admitting diagnosis.

METHOD:

Nine consecutive years (2005-2013) of general hospital admissions (N=831,348) were identified across two academic medical centers using electronic health records. The primary admitting diagnosis and the treating clinical department were used to calculate occurrence rates of a previously published delirium definition composed of billing codes and natural language processing of discharge summaries.

RESULTS:

Delirium rates varied significantly across both admitting diagnosis group (X210=12786, p<0.001) and department of care (X26=12106, p<0.001). In both cases obstetrical admissions showed the lowest incidences of delirium (86/109764; 0.08%) and neurological admissions the greatest (2851/25450; 11.2%). Although the rate of delirium varied across the two hospitals the relative rates within departments (r=0.96, p<0.001) and diagnostic categories (r=0.98, p<0.001) were consistent across the two institutions.

CONCLUSIONS:

The frequency of delirium varies significantly across admitting diagnosis and hospital department. Both admitting diagnosis and department of care are even stronger predictors of risk than age; as such, simple risk stratification may offer avenues for targeted prevention and treatment efforts.

KEYWORDS:

Acute confusional state; Delirium; Electronic health record; Epidemiology

[Indexed for MEDLINE]

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