Swain A, Nag DS, Laik JK, Sahu S, Panchal M, Srirala S. Unplanned intensive care unit admissions in trauma patients: A critical appraisal. World J Crit Care Med 2025; 14(3): 105147 [DOI: 10.5492/wjccm.v14.i3.105147]
Corresponding Author of This Article
Deb Sanjay Nag, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, India. ds.nag@tatasteel.com
Research Domain of This Article
Anesthesiology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Amlan Swain, Deb Sanjay Nag, Seelora Sahu, Shivani Srirala, Department of Anaesthesiology, Tata Main Hospital, Jamshedpur 831001, India
Amlan Swain, Seelora Sahu, Department of Anaesthesiology, Manipal Tata Medical College, Jamshedpur 831017, India
Jayanta Kumar Laik, Department of Joint Replacement and Orthopedics, Tata Main Hospital, Jamshedpur 831001, India
Mrunalkant Panchal, Department of Surgery, Tata Main Hospital, Jamshedpur 831001, India
Author contributions: Swain A, Nag DS, Laik JK, Sahu S, Panchal M, and Shivani S contributed to this paper; Swain A, Nag DS, Laik JK, Panchal M designed the overall concept and outline of the manuscript; Laik JK, Sahu S, and Shivani S contributed to the discussion and design of the manuscript; Swain A, Nag DS, Laik JK, Sahu S, Panchal M, and Shivani S contributed to the writing and editing of the manuscript and review of literature.
Conflict-of-interest statement: All authors declare that they have no competing interests.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Deb Sanjay Nag, Department of Anaesthesiology, Tata Main Hospital, C Road West, Northern Town, Bistupur, Jamshedpur 831001, India. ds.nag@tatasteel.com
Received: January 13, 2025 Revised: April 7, 2025 Accepted: April 24, 2025 Published online: September 9, 2025 Processing time: 187 Days and 0.6 Hours
Abstract
Unplanned intensive care unit (ICU) admissions (UP-ICU) following initial general ward placement are associated with poor patient outcomes and represent a key quality indicator for healthcare facilities. Healthcare facilities have employed numerous predictive models, such as physiological scores (e.g., Acute Physiology and Chronic Health Evaluation II, Revised Trauma Score, and Mortality Probability Model II at 24 hours) and anatomical scores (Injury Severity Score and New Injury Severity Score), to identify high-risk patients. Although physiological scores frequently surpass anatomical scores in predicting mortality, their specificity for trauma patients is limited, and their clinical applicability may be limited. Initially proposed for ICU readmission prediction, the stability and workload index for the transfer score has demonstrated inconsistent validity. Machine learning offers a promising alternative. Several studies have shown that machine learning models, including those that use electronic health records (EHR) data, can more accurately predict trauma patients’ deaths and admissions to the ICU than traditional scoring systems. These models identify unique predictors that are not captured by existing methods. However, challenges remain, including integration with EHR systems and data entry complexities. Critical care outreach programs and telemedicine can help reduce UP-ICU admissions; however, their effectiveness remains unclear because of costs and implementation challenges, respectively. Strategies to reduce UP-ICU admissions include improving triage systems, implementing evidence-based protocols for ICU patient management, and prioritizing prehospital intervention and stabilization to optimize the “golden hour” of trauma care. To improve patient outcomes and reduce the burden of UP-ICU admissions, further studies are required to validate and implement these strategies and refine machine learning models.
Core Tip: Unplanned intensive care unit (ICU) admissions after trauma significantly worsen patient outcomes and increase healthcare costs. Existing predictive tools include Injury Severity Score, New Injury Severity Score (anatomical scoring) and Glasgow Coma Scale, an Acute Physiology and Chronic Health Evaluation III (physiological scoring) the as well as the stability and workload index for the transfer score. Machine learning offers a promising alternative particularly when combined with electronic health records. Reducing unplanned ICU admissions requires a multifaceted approach. Combining advanced predictive modeling with proactive management and interdisciplinary coordination is crucial for reducing the incidence and impact of unplanned ICU admissions in patients with trauma.
