Ascertainment of Delirium Status Using Natural Language Processing From Electronic Health Records. Issue 3 (30th October 2020)

• Record Type: Journal Article
• Authors: Fu, Sunyang; Lopes, Guilherme S; Pagali, Sandeep R; Thorsteinsdottir, Bjoerg; LeBrasseur, Nathan K; Wen, Andrew; Liu, Hongfang; Rocca, Walter A; Olson, Janet E; St. Sauver, Jennifer; Sohn, Sunghwan
• Editors: Newman, Anne B
• Is Part Of: Journals of gerontology. Volume 77:Issue 3(2022)
• Page Start: 524
• Page End: 530
• Publication Date: 2020-10-30
• Subjects: Confusion assessment method -- Delirium -- Electronic health records -- Natural language processing; Geriatrics -- Periodicals; Gerontology -- Periodicals; 618.97
• DOI: 10.1093/gerona/glaa275
• Languages: English
• ISSNs: 1079-5006

Abstract

Abstract: Background: Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs. Methods: This study used a randomly selected cohort from the population-based Mayo Clinic Biobank ( N = 300, age ≥65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium using clinical notes in EHRs. Two NLP algorithms were developed based on CAM criteria: one based on the original CAM (NLP-CAM; delirium vs no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium). The sensitivity, specificity, and accuracy were used for concordance in delirium status between NLP algorithms and manual chart review as the gold standard. The prevalence of delirium cases was examined using International Classification of Diseases, 9th Revision (ICD-9), NLP-CAM, and NLP-mCAM. Results: NLP-CAM demonstrated a sensitivity, specificity, and accuracy of 0.919, 1.000, and 0.967, respectively. NLP-mCAM demonstrated sensitivity, specificity, and accuracy of 0.827, 0.913, and 0.827, respectively. The prevalence analysis of delirium showed that the NLP-CAM algorithm identified 12 651 (9.4%) delirium patients, the NLP-mCAM algorithm identified 20 611 (15.3%) definite delirium cases, and 10 762 (8.0%) possible cases. Conclusions: NLP algorithms based on the standardized evidence-based CAM framework demonstrated high performance in delineating delirium status in an expeditious and cost-effective manner.