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
Core Tip
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.
