Unplanned intensive care unit admissions in trauma patients: A critical appraisal

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.

Key Words: Trauma centers; Intensive care units; APACHE; Patient readmission; Machine learning

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.

INTRODUCTION

Patients admitted to the general ward and requiring subsequent transfer to the intensive care unit (ICU) are designated as unplanned ICU (UP-ICU) admissions. Patients requiring UP-ICU admission experience substantially poorer outcomes[1-4]. UP-ICU admissions serve as a valuable metric for improving the performance of healthcare facilities and are also useful for benchmarking[5]. UP-ICU admission, also known as “bounce back” in common clinical parlance, has been extensively studied. UP-ICU admission is associated with longer hospital stay and worse outcome. Researchers have also identified several predictors for ICU admission among trauma patients. These include the severity of the initial injury, associated comorbidities, age of the patient, and seriousness of the illness[5-7]. Cardiac and respiratory system disturbances at the time of discharge from the ICU, discharge during the night shift, limited ICU beds, and the lack of standard discharge protocols have also been reported to contribute to ICU readmission[8-10].

PREDICTIVE MODELS FOR ICU ADMISSION AND OUTCOMES

We used various predictive models to facilitate ICU admission and predict outcomes. Initially, the scores of these models were not specific to the type of systemic pathology being treated. The initial predictive models used included the Glasgow Coma Scale (GCS) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Modified Early Warning Score (MEWS), and Predisposition, Infection, Response, and Organ Dysfunction score[1,11-14]. Khari et al[11] from Iran compared the GCS score with eight physiologic scoring systems for predicting outcomes of trauma patients in the ICU. They found that GCS was a more effective modality for predicting mortality and poor outcomes in trauma patients. The drawbacks of these scores include their specific design for the trauma population and their cumbersome application in clinical settings[1,15,16].

COMPARISON OF TRAUMA, SPECIFIC SCORES, AND PHYSIOLOGICAL SCORES

In recent decades, multiple scores for assessing the severity in patients with trauma have been developed. These scores can be broadly classified into three categories: Anatomical [Injury Severity Score (ISS) and New Injury Severity Score (NISS)], physiological [Revised Trauma Score (RTS), Mortality Probability Model II at 24 hours, and APACHE II], and mixed scoring systems such as Trauma and Injury Severity Score, which combine physiological scores (RTS) based on the anatomical ISS and the mechanism of injury (penetration vs blunt). Researchers have developed statistical models that can enhance predictive accuracy. Physiological models have been observed to outperform anatomical models. In a study in Victoria, Australia, physiological scores such as APACHE III and ANZROD (a customized APACHE III Score for the Australian and New Zealand Population) were reported to be better at predicting hospital mortality than trauma-specific scores such as ISS, NISS, and RTS in trauma patients who were alive after 24 hours in the ICU[17]. A meta-analysis comparing the efficacy of ISS and NISS in predicting mortality did not reveal significant differences in their predictive ability[18].

STABILITY AND WORKLOAD INDEX FOR THE TRANSFER SCORE

Farmer et al[19] developed the stability and workload index for the transfer (SWIFT) score to quantify the suitability of patient transfer from critical care setups. The elements of the SWIFT score included ICU length of stay, days of discharge, GCS score, nursing demand for complex respiratory care, the Pa/FiO₂ ratio, and the ICU admissions source. The authors initially suggested that the SWIFT score showed an acceptable correlation with ICU readmission[19]. Subsequently, Gajic et al[20] validated the SWIFT score for the prediction of ICU readmission. The result of their study was equivocal, i.e., the authors were not sure whether the SWIFT score for discharge had beneficial effects in reducing ICU readmission or improving outcomes. A more extensive study of the SWIFT score subsequently completely debunked its efficacy[21].

ROLE OF MACHINE LEARNING

Researchers have explored machine learning in various critical care patient subsets, particularly patients requiring surgery, to predict ICU readmission[22]. Various machine learning models can predict mortality and unplanned ICU admission for patients with trauma[23-25]. Multiple deterrents exist for the more widespread use of machine learning models to predict mortality and prevent ICU readmission for trauma patients. These include the lack of integration of such models with existing EHRs and the multiple data entry requirements in third-party web applications[23,26]. Mou et al[23] investigated machine learning in conjunction with EHRs to predict mortality and unplanned ICU readmissions in trauma patients. They developed the Epic Termination Deterioration Index (EDI) using 125 objective parameters of the EHR and examined its efficacy. The authors discovered that the EDI was effective in forecasting in-patient mortality, such as NISS and AISS, and could also predict UP-ICU admissions. Multiple studies have investigated the role of machine learning in various subspecialties among patients requiring critical care. The patient subsets investigated included those with sepsis and those undergoing cardiac surgery[27-31]. Desautels et al[32] used a machine learning tool known as transfer learning to predict UP-ICU readmission for the first time in a tertiary care center in the United Kingdom. They found that the proposed model outperformed the SWIFT score. The authors demonstrated that the transfer learning algorithm could achieve excellent discrimination beyond that of treating clinicians or the SWIFT score[32]. Placido, based in Denmark, developed a deep learning model using recurrent neural networks and embedded text from various datasets to predict the risk of composite outcomes such as UP-ICU transfers and in-hospital death. They showed that the model was a viable tool for detecting patients at higher risk of clinical deterioration. The authors recommended that a proper prospective evaluation is required to establish the real-world efficacy of these models[33]. Zander et al[34] explored the use of machine learning to predict UP-ICU admission in nonoperative patients admitted in non-ICU locations as recently as 2024. They showed that machine learning demonstrated superior efficacy in comparison to previous attempts in predicting UP-ICU admission in trauma patients. The study also identified unique predictors for these unforeseen admissions.

OUTREACH PROGRAMS

In certain countries (such as the United Kingdom and Australia), patient-at-risk teams and follow-up services, commonly known as critical care outreach programs, have been established. There is evidence indicating the effectiveness of these teams; however, the increased costs associated with establishing and managing these teams have necessitated the need for evidence to justify their existence. The results regarding the efficacy of these outreach programs have been mixed[35-37].

ROLE OF TELEMEDICINE

Numerous studies have explored the efficacy of telemedicine in monitoring ICUs from offsite areas[38]. However, the results have not always been encouraging[38]. The coronavirus disease 2019 (COVID-19) pandemic, wars, and natural disasters have resulted in the widespread adoption of telemedicine in critical care in the last 1.5 decades. The three most important components of telemedicine are appropriate connectivity, software, and hardware. Regulatory deficiencies, poor compensation, infrastructure constraints, and cybersecurity concerns are the major risks for the implementation of telemedicine in critical care units[39]. Lewis et al[40] conducted a systematic review on the use of telemedicine in the management of acute phase injuries. They concluded that the barriers outlined above have limited the use of telemedicine in patients with trauma, especially in critical care setups. Lazzara et al[41] conducted a study in 2015 to evaluate the influence of telemedical robots on trauma ICU clinical teamwork during patient rounds. The study demonstrated that telemedicine increased task-based communication and did not negatively affect the efficacy of the teams involved in clinical rounds.

RESOURCES AND STRATEGIES FOR MITIGATING UNPLANNED ICU ADMISSIONS

Establishing a dedicated trauma care unit and adding specialized nursing care have also been shown to improve the quality and reduce the cost of care at Level I trauma centers[42]. Respiratory perturbations, such as insufficiency with acute respiratory failure and increased oxygen requirements, are common reasons for unplanned ICU admission. Medical errors, including triage errors, are also causes of such ICU admissions[43-49]. Several scoring systems, as well as effective triage systems, can help prevent sudden clinical deterioration and unscheduled ICU readmission. The tendency to transfer out ICU patients based on physician discretion has a detrimental effect on clinical outcomes and leads to unwanted ICU readmissions[20-24]. A comprehensive review of the chart of patients transferred to the ICU as well as the adoption of evidence-based protocols for patient management in the ICU and in the decision paradigm for transfer is useful in improving patient outcomes and avoiding catastrophic transfers out of the ICU[50-52].

PREHOSPITAL INTERVENTIONS: EARLY IDENTIFICATION AND STABILIZATION

The management of polytrauma requires substantial care, necessitating the involvement of multidisciplinary teams and specialized competence to effectively manage patients. The goal of such patient care includes the following: Attainment of maximum functional recovery; Identification and management of life-threatening injuries; Prevention of exacerbations of existing injuries; Prevention of further organ damage.

These principles as well as the formation of established trauma centers have been shown to be crucial in providing effective treatment during the “golden hour” following falling trauma. They also play a beneficial role in the comprehensive triage of, trauma patients and subsequent care, thereby preventing unnecessary ICU admissions[53-55].

BOUNCE BACKS VS UPGRADES COMPARISONS

Fokin et al[56] suggested that although upgrades (from floor to ICU) were more frequent than bounce backs (discharge from ICU and readmission), bounce backs were associated with worse outcomes, particularly longer ICU and hospital stays and higher mortality, especially among geriatric patients. Thoracic and traumatic brain injuries were common in both groups[56]. Early detection and preventive treatment within the first 72 hours of hospitalization are crucial for improving outcomes[56]. In another study, Fakhry et al[6] analyzed unplanned ICU readmissions (bounce back) in trauma patients. A bounce-back rate of 4.5% was observed, with 19.3% mortality. Male sex, low GCS score (< 9), discharge during the day shift, and comorbidities predicted readmission[6]. Early intervention for high-risk patients has been suggested.

DISCUSSION

The evolution of predictive models for ICU admission and patient outcomes has been pivotal in optimizing care for critically ill patients. Traditional models such as the GCS, APACHE II, and MEWS have laid the groundwork for assessing patient severity and predicting outcomes. However, these scores have limitations, particularly in terms of their specificity to trauma and operational efficiency in clinical settings. Khari et al[11] have highlighted the superior efficacy of GCS in predicting mortality among trauma patients compared with other physiological scoring systems; however, the lack of adaptability of these models limits their widespread application[1,15].

The categorization of injury severity scores into anatomical, physiological, and mixed systems indicates a dynamic approach for assessing trauma patients. Recent studies, including one from Victoria, Australia, have shown that physiological scoring systems outperform anatomical ones, emphasizing the need for continuous refinement of these models[17]. Interestingly, a meta-analysis indicated no significant differences between ISS and NISS in predicting mortality, raising questions on the reliance on specific systems without considering their context and patient demographics[18].

The introduction of the SWIFT score aimed to enhance transfer decision-making for ICU patients; however, subsequent validation studies have yielded conflicting results regarding its effectiveness in reducing ICU readmissions[19-21]. This reflects the broader challenge of critically appraising and validating new scoring systems in a complex clinical environment.

Machine learning is a promising frontier in predictive modeling; its application in identifying risk factors for unplanned ICU admissions and mortality is gaining traction[22,23]. However, the challenges associated with integrating these advanced models into existing EHR systems continue to inhibit their adoption in clinical practice. Mou et al[23] demonstrated the potential of machine learning algorithms, specifically the EDI, to improve mortality predictions significantly. In addition, the innovative transfer learning approach proposed by Desautels et al[32] has shown remarkable potential, outperforming traditional scoring methods, highlighting the capacity of machine learning to transform predictive analytics in critical care.

Telemedicine’s role in enhancing ICU care—especially in the wake of the COVID-19 pandemic—shows both potential benefits and limitations. Studies have indicated mixed results concerning its efficacy, and significant barriers such as inadequate infrastructure and regulatory concerns remain to be addressed[38-40]. Nevertheless, positive results, such as those from Lazzara et al[41] regarding teamwork dynamics during patient rounds, indicate the value of telemedicine in enhancing clinical communication.

The exploration of prehospital interventions, focusing on the early identification and management of trauma cases, highlights the critical need for swift action in emergency settings. Techniques that optimize triage and care during the “golden hour” can drastically alter patient outcomes and potentially decrease unnecessary ICU admissions[53-55].

Lastly, the differentiation between “bounce backs” (readmissions to ICU) and “upgrades” (transfers from general wards to ICU) emphasizes the complexity of patient transitions and their associated outcomes. The findings of Fokin et al[56] regarding worse outcomes associated with bounce backs highlight the need for meticulous discharge planning and ongoing patient monitoring to mitigate associated risks.

CONCLUSION

UP-ICU admissions in patients with trauma are associated with increased mortality, length of hospital stay, and costs[1,5]. These admissions occur in 1.2%–2.15% of trauma patients initially admitted to non-ICU locations[1,57]. Common causes include respiratory distress, neurological decline, and cardiovascular issues[58]. Risk factors for unplanned ICU admissions include older age, comorbidities like congestive heart failure and chronic obstructive pulmonary disease, and significant injuries to the thorax, spine, and lower extremities[57]. Predictive tools, such as the CRASH score, have been developed to identify patients at risk of UP-ICU admission[1]. Many unplanned transfers occur within 48 hours of admission or ICU discharge, suggesting undertriage as a leading cause[58]. These findings highlight opportunities for targeted interventions to improve patient outcomes and resource use.