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J Am Med Dir Assoc. 2016 Mar 1;17(3):244-8. doi: 10.1016/j.jamda.2015.10.020. Epub 2015 Dec 15.

Validation of a Delirium Risk Assessment Using Electronic Medical Record Information.

Author information

1
Center of Innovation in Long-term Services/Supports, Providence VA Medical Center, Providence, RI; Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA; Division of Aging, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA. Electronic address: James.Rudolph@va.gov.
2
Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA.
3
Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA; School of Nursing, Science and Health Professions, Regis College, Weston, MA.
4
Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA; Division of Aging, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA.
5
Delirium Patient Safety Center of Inquiry, VA Boston Healthcare System, Boston, MA; Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, MA.

Abstract

OBJECTIVE:

Identifying patients at risk for delirium allows prompt application of prevention, diagnostic, and treatment strategies; but is rarely done. Once delirium develops, patients are more likely to need posthospitalization skilled care. This study developed an a priori electronic prediction rule using independent risk factors identified in a National Center of Clinical Excellence meta-analysis and validated the ability to predict delirium in 2 cohorts.

DESIGN:

Retrospective analysis followed by prospective validation.

SETTING:

Tertiary VA Hospital in New England.

PARTICIPANTS:

A total of 27,625 medical records of hospitalized patients and 246 prospectively enrolled patients admitted to the hospital.

MEASUREMENTS:

The electronic delirium risk prediction rule was created using data obtained from the patient electronic medical record (EMR). The primary outcome, delirium, was identified 2 ways: (1) from the EMR (retrospective cohort) and (2) clinical assessment on enrollment and daily thereafter (prospective participants). We assessed discrimination of the delirium prediction rule with the C-statistic. Secondary outcomes were length of stay and discharge to rehabilitation.

RESULTS:

Retrospectively, delirium was identified in 8% of medical records (n = 2343); prospectively, delirium during hospitalization was present in 26% of participants (n = 64). In the retrospective cohort, medical record delirium was identified in 2%, 3%, 11%, and 38% of the low, intermediate, high, and very high-risk groups, respectively (C-statistic = 0.81; 95% confidence interval 0.80-0.82). Prospectively, the electronic prediction rule identified delirium in 15%, 18%, 31%, and 55% of these groups (C-statistic = 0.69; 95% confidence interval 0.61-0.77). Compared with low-risk patients, those at high- or very high delirium risk had increased length of stay (5.7 ± 5.6 vs 3.7 ± 2.7 days; P = .001) and higher rates of discharge to rehabilitation (8.9% vs 20.8%; P = .02).

CONCLUSIONS:

Automatic calculation of delirium risk using an EMR algorithm identifies patients at risk for delirium, which creates a critical opportunity for gaining clinical efficiencies and improving delirium identification, including those needing skilled care.

KEYWORDS:

Delirium; patient safety; patient-centered outcomes research; risk assessment

PMID:
26705000
DOI:
10.1016/j.jamda.2015.10.020
[Indexed for MEDLINE]

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