Format

Send to

Choose Destination
J Am Geriatr Soc. 2019 Oct 14. doi: 10.1111/jgs.16182. [Epub ahead of print]

Development and Validation of eRADAR: A Tool Using EHR Data to Detect Unrecognized Dementia.

Author information

1
Department of Psychiatry, University of California, San Francisco, San Francisco, California.
2
Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, California.
3
San Francisco Veterans Affairs Health Care System.
4
Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, Washington.
5
Kaiser Permanente Washington Health Research Institute.
6
Department of Medicine, Division of Geriatrics, University of California, San Francisco, San Francisco, California.
7
Department of Pharmacy, University of Washington, Seattle, Washington.
8
Department of Epidemiology, University of Washington, Seattle, Washington.

Abstract

OBJECTIVES:

Early recognition of dementia would allow patients and their families to receive care earlier in the disease process, potentially improving care management and patient outcomes, yet nearly half of patients with dementia are undiagnosed. Our aim was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia (EHR Risk of Alzheimer's and Dementia Assessment Rule [eRADAR]).

DESIGN:

Retrospective cohort study.

SETTING:

Kaiser Permanente Washington (KPWA), an integrated healthcare delivery system.

PARTICIPANTS:

A total of 16 665 visits among 4330 participants in the Adult Changes in Thought (ACT) study, who undergo a comprehensive process to detect and diagnose dementia every 2 years and have linked KPWA EHR data, divided into development (70%) and validation (30%) samples.

MEASUREMENTS:

EHR predictors included demographics, medical diagnoses, vital signs, healthcare utilization, and medications within the previous 2 years. Unrecognized dementia was defined as detection in ACT before documentation in the KPWA EHR (ie, lack of dementia or memory loss diagnosis codes or dementia medication fills).

RESULTS:

Overall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 498 (49%) were unrecognized in the KPWA EHR. The final 31-predictor model included markers of dementia-related symptoms (eg, psychosis diagnoses, antidepressant fills), healthcare utilization pattern (eg, emergency department visits), and dementia risk factors (eg, cerebrovascular disease, diabetes). Discrimination was good in the development (C statistic = .78; 95% confidence interval [CI] = .76-.81) and validation (C statistic = .81; 95% CI = .78-.84) samples, and calibration was good based on plots of predicted vs observed risk. If patients with scores in the top 5% were flagged for additional evaluation, we estimate that 1 in 6 would have dementia.

CONCLUSION:

The eRADAR tool uses existing EHR data to detect patients with good accuracy who may have unrecognized dementia.

KEYWORDS:

decision support techniques; dementia; early diagnosis

PMID:
31612463
DOI:
10.1111/jgs.16182

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center