Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records

Yijun Shao,Debby W Tsuang,Stephen M Thielke,Andrew Shutes-David,Kathryn K Chen,Qing T Zeng

doi:10.1186/s12911-019-0846-4

Abstract

BackgroundDementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. New approaches are therefore necessary to facilitate the timely detection of dementia. This study seeks to identify cases of undiagnosed dementia by developing and validating a weakly supervised machine-learning approach that incorporates the analysis of both structured and unstructured electronic health record (EHR) data.MethodsA topic modeling approach that included latent Dirichlet allocation, stable topic extraction, and random sampling was applied to VHA EHRs. Topic features from unstructured data and features from structured data were compared between Veterans with (n = 1861) and without (n = 9305) ICD-9 dementia codes. A logistic regression model was used to develop dementia prediction scores, and manual reviews were conducted to validate the machine-learning results.ResultsA total of 853 features were identified (290 topics, 174 non-dementia ICD codes, 159 CPT codes, 59 medications, and 171 note types) for the development of logistic regression prediction scores. These scores were validated in a subset of Veterans without ICD-9 dementia codes (n = 120) by experts in dementia who performed manual record reviews and achieved a high level of inter-rater agreement. The manual reviews were used to develop a receiver of characteristic (ROC) curve with different thresholds for case detection, including a threshold of 0.061, which produced an optimal sensitivity (0.825) and specificity (0.832).ConclusionsDementia is underdiagnosed, and thus, ICD codes alone cannot serve as a gold standard for diagnosis. However, this study suggests that imperfect data (e.g., ICD codes in combination with other EHR features) can serve as a silver standard to develop a risk model, apply that model to patients without dementia codes, and then select a case-detection threshold. The study is one of the first to utilize both structured and unstructured EHRs to develop risk scores for the diagnosis of dementia.

Highlights

Dementia is underdiagnosed in both the general population and among Veterans
The controls were matched to dementia cases (5:1) on gender, age, and Charlson comorbidity index (CCI) [19] as a way to reduce the contributions of these variables to the differences that might be observed in structured and unstructured data between cases and controls
Our findings show that there are terms in notes and coded electronic health record (EHR) data that are more likely to be associated with dementia cases than controls, and our examination of these terms suggests a high rate of undiagnosed dementia in Veterans Health Administration (VHA)

Summary

Introduction

Dementia is underdiagnosed in both the general population and among Veterans This underdiagnosis decreases quality of life, reduces opportunities for interventions, and increases health-care costs. Dementia significantly decreases quality of life and increases inpatient service utilization [1, 2], outpatient mental health visits, and health-care costs, both in civilian contexts [3, 4] and within VHA [1]. Many of these consequences can be at least moderately reduced when dementia is identified earlier in the course of illness.

Methods

Results

Discussion

Conclusion