A machine learning approach to identifying delirium from electronic health records. Issue 2 (25th May 2022)

• Record Type: Journal Article
• Authors: Kim, Jae Hyun; Hua, May; Whittington, Robert A; Lee, Junghwan; Liu, Cong; Ta, Casey N; Marcantonio, Edward R; Goldberg, Terry E; Weng, Chunhua
• Is Part Of: JAMIA open. Volume 5:Issue 2(2022)
• Publication Date: 2022-05-25
• Subjects: delirium -- Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) -- electronic health records -- logistic regression -- machine learning model; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooac042
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: The identification of delirium in electronic health records (EHRs) remains difficult due to inadequate assessment or under-documentation. The purpose of this research is to present a classification model that identifies delirium using retrospective EHR data. Delirium was confirmed with the Confusion Assessment Method for the Intensive Care Unit. Age, sex, Elixhauser comorbidity index, drug exposures, and diagnoses were used as features. The model was developed based on the Columbia University Irving Medical Center EHR data and further validated with the Medical Information Mart for Intensive Care III dataset. Seventy-six patients from Surgical/Cardiothoracic ICU were included in the model. The logistic regression model achieved the best performance in identifying delirium; mean AUC of 0.874 ± 0.033. The mean positive predictive value of the logistic regression model was 0.80. The model promises to identify delirium cases with EHR data, thereby enable a sustainable infrastructure to build a retrospective cohort of delirium. Lay Summary: Delirium is a commonly observed complication in hospitalized patients, especially with intensive care. While signs and symptoms of delirium could be observed and well managed during the hospital stay, less is known about the long-term complication of delirium after discharge. In order to monitor the long-term sequelae of delirium, the correct identification of delirium patients is crucial. Currently, the retrospective identification of delirium patients is limited due to the under-coding of delirium diagnosis in electronic health records. We proposed a simple machine-learning model to retrospectively identify patients who experienced delirium during their intensive care unit stay. The model could be used to identify missed delirium cases and the establishment of a delirium cohort for long-term monitoring and surveillance.