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Predicting Unscheduled Emergency Department Revisits Leading to Acute Hospital Admissions Among Older Adults
Journal article   Open access   Peer reviewed

Predicting Unscheduled Emergency Department Revisits Leading to Acute Hospital Admissions Among Older Adults

Tony Rosen, Yufang Huang, Matthew McCarty, Michael Stern, Yiye Zhang, Daniel Barchi, Rahul Sharma, Peter Steel and Rose A DiMaria-Ghalili
Innovation in aging, v 5(Suppl 1), pp 582-582
17 Dec 2021
DOI: https://doi.org/10.1093/geroni/igab046.2233
url
https://academic.oup.com/innovateage/article-pdf/5/Supplement_1/582/43184242/igab046.2233.pdfView
Published, Version of Record (VoR) Open
url
https://doi.org/10.1093/geroni/igab046.2233View
Published, Version of Record (VoR) Open

Abstract

Abstracts AcademicSubjects SOC02600
Background: Unscheduled emergency department (ED) revisits leading to acute hospital admission (RVA) are tantamount to a failed discharge, associated with physician error, mis-prognosis, and inadequate care planning. Previous research has shown RVA to be associated with adverse outcomes such as ICU admissions, long hospitalizations and mortality. Given the limited impact of pre-existing screening tools for older adults, we developed and validated a machine learning model to predict individual patient risk of RVA within 72 hours and 9 days of index ED visits. Method: A machine learning model was applied to retrospective electronic health record (EHR) data of patients presenting to 2 geographically and demographically divergent urban EDs in 2019. 478 clinically meaningful EHR data variables were included: socio-demographics, ED and comorbidity diagnoses, therapeutics, laboratory test orders and test results, diagnostic imaging test orders, vital signs, and utilization and operational data. Multiple machine learning algorithms were constructed; models were compared against a pre-existing adult ED-RVA risk score as a baseline. Results: A total of 62,154 patients were included in the analysis, with 508 (0.82%) and 889 (1.4%) having 72-hour and 9-day RVA. The best-performing model, combining deep significance clustering (DICE) and regularized logistic regression, achieved AUC of 0.86 and 0.79 for 72-hour and 9-day ED-RVA for older adult patients, respectively, outperforming the pre-existing RVA risk score (0.704 and 0.694). Discussion: Machine learning models to screen for and predict older adults at high-risk for ED-RVA may be useful in directing interventions to reduce adverse events in older adults discharged from the ED.

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