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.