The Epidemiology of ICU Readmissions Across Ten Health Systems
Year: 2025
Type: article
Study design: Observational Study
Abstract: ICU readmissions remain a critical concern, carrying increased morbidity, mortality, and cost. We examined the epidemiology of unplanned ICU readmissions across 19 hospitals in the Common Longitudinal ICU data Format (CLIF) Consortium from January 2020 to December 2021 and the MIMIC-IV database. The cohort included 185,241 adult ICU admissions, excluding postoperative or post-interventional procedure readmissions. Overall, 8.6% of ICU discharges were readmitted during the same hospitalization. Unplanned readmissions occurred in 1.9% of cases within 24 hours, 3.4% within 48 hours, and 4.5% within 72 hours of discharge. Readmitted patients experienced markedly higher in-hospital mortality (20.6% vs. 2.1%; p < 0.001). Compared with initial ICU stays, readmissions were associated with greater use of respiratory support (42.3% vs. 35.3%) and vasopressors (26.1% vs. 23.1%). Hospitals with stepdown units demonstrated comparable unplanned ICU readmission rates. These findings demonstrate that ICU readmissions remain common, are associated with poor outcomes, and require greater organ support. Improved characterization of high-risk subphenotypes is needed to inform safer discharge processes.
Source: Critical Care Explorations
Authors: Saki Amagai, Vaishvik Chaudhari, Kaveri Chhikara, Nicholas E. Ingraham, Chad H. Hochberg, Anna K. Barker, Chengsheng Mao, Alexander C. Ortiz, Gary E. Weissman, Benjamin E. Schmid, Megan Schwinne, Sivasubramanium V. Bhavani, Shan Guleria, Zewei Liao, Nikolay Markov, Patrick G. Lyons, Brenna Park-Egan, William F. Parker, Yuan Luo, Juan C. Rojas, Catherine A. Gao
Language: English
Subfield: Emergency Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,600
Awards: Mending a Broken Heart Allocation System with Machine Learning, G1676635069, G2578364121, G2984098479, High Performance Computing and Machine Learning Infrastructure for Oregon Life Sciences, G5110925203, Machine learning approaches to predict outcomes and complications in ICU patients, Development and Validation of Correction Equations to Enhance Quality of Infrared Technology, Cardiology and Pulmonary Clinical Research Training Program
OpenAlex ID: W4415700063
