Based on evidence to explore and analyze clinical pathway risk factors by the different prediction methods in trauma patients

1 Introduction

The development of multidisciplinary trauma treatment teams in China has been a relatively recent endeavor, marked by the absence of comprehensive management protocols and standardized procedures. This gap has contributed to a significantly higher mortality rate among trauma patients in China compared to other countries, and the overall level of trauma treatment remains lower than that of developed nations (1). Recognizing the urgent need to address these challenges, the National Health Commission took a pivotal step in 2018 to mandate the establishment of multidisciplinary trauma treatment teams within trauma centers, aiming to elevate the standard of trauma care across the country (2). Building on this foundation, the National Health Commission continued to refine the framework for trauma care to further standardize the processes and protocols for trauma team activation and operation (3). These efforts reflect a growing recognition of the importance of multidisciplinary collaboration in improving patient outcomes and reducing mortality rates.

These measures has further underscored the need for a more robust and refined regional emergency response system. As part of this strategy, there is an increasing emphasis on enhancing high-quality emergency response systems and disaster rescue capabilities. This includes the development of more sophisticated regional emergency networks that can respond effectively to trauma cases, particularly in the context of large-scale disasters or mass casualty incidents (4). The integration of modern multidisciplinary collaboration and the advancement of hybrid surgical techniques have also raised the bar for trauma treatment management, requiring healthcare providers to adopt more innovative and coordinated approaches to patient care.

Based on literature (5–13), pre-hospital transport is a crucial pathway for ensuring that trauma patients reach medical facilities promptly and receive rapid treatment. By transporting patients to the nearest trauma center, accurately assessing the severity of the patient's condition, quickly completing related examinations, and obtaining information on the trauma sites, systemic emergency treatment measures can be provided to improve patient clinical outcomes. For example, GCS < 15 indicates possible traumatic brain injury; ISS ≥ 16 is associated with an increase in mortality rate; Pre-hospital transportation > 30 min is associated with increased mortality rate; Pelvic/thoracic X-ray > 60 min is associated with delayed surgical risk and missed diagnosis, et al.

Moreover, the establishment of trauma centers and the standardization of trauma team protocols have facilitated the development of a more systematic approach to trauma care (14). This includes the implementation of evidence-based practices, the use of advanced diagnostic and therapeutic technologies, and the adoption of quality improvement initiatives to continuously enhance the effectiveness of trauma treatment. The integration of data-driven decision-making and the use of trauma registries have also played a key role in monitoring outcomes and identifying areas for improvement. However, compared to foreign trauma rescue, there are still shortcomings in China, especially existing the unclear point in decision-making for clinical pathways of trauma patients, such as risk factors, process optimization, timeliness, and decision-making weight.

As China continues to advance its trauma care capabilities, there is a growing recognition of the importance of international collaboration and knowledge exchange. By learning from the experiences of developed countries and adapting best practices to the local context, China is working to bridge the gap in trauma care and achieve parity with global standards. This includes fostering partnerships with international trauma care organizations, participating in global trauma research initiatives, and investing in the training and education of healthcare professionals to build a more skilled and knowledgeable workforce.

Above all, the research focused on two key aspects in the treatment of critically ill trauma patients: timeliness and comprehensiveness. By analyzing the treatment process of critically ill trauma patients, recording key indicator times, and conducting a multifactor analysis based on patient outcomes, we used predictive models to identify influential factors. Our goal was to explore the challenges in trauma treatment under China's medical landscape and propose countermeasures and recommendations, providing theoretical and empirical support for emergency trauma management. The findings are reported as follows:

2 Research subjects and methods

2.1 Research subjects

This study was submitted to, and approved by the local ethics committee, Shanghai East Hospital Ethics Committee, the number was 2025YS-068.Patients admitted between January 1, 2024, and December 31, 2024, with traumatic injuries were included in the study. The collected data included diagnosis, arrival time, mode of arrival, vital signs, GCS (Glasgow Coma Scale) score, ISS (Injury Severity Score), TI (Trauma Index) score, resuscitation time, examination time, surgical time, and final outcomes. Patients were divided into a survival group and a death group, with key clinical outcome factors analyzed. All the outcome based on statues of patients before being transformed to next department (e.g., operating room, ICU, et.al).

2.2 Definition criteria

Patients with trauma referred to the simultaneous or sequential occurrence of two or more severe injuries to different anatomical locations or organs caused by the same mechanical injury factor, with at least one injury being life-threatening or traumatic shock, at the same time, medical personnel initiated trauma treatment emergency procedures. The inclusion criteria were as follows: (1) age not less than 14 years old; (2) trauma patients; and (3) completed auxiliary examination data and clinical pathways of various trauma sites. The exclusion criteria were as follows: patients who died on the spot or came to hospital; pregnant; and lying-in women.

2.3 Sample size

The study aimed to analyze factors influencing patient clinical pathways to optimize emergency trauma treatment procedures by different predictive models. Based on national trauma patient indicators and literature research (Tables 1, 2), the range of variable were defined and 16 risk factors were selected. According to research methodology, each independent variable requires 5–10 patient cases (15), with an expected mortality rate of 1–2%. Allowing for a 10% dropout rate, the calculated required sample size was: (16 x 10/0.1)/0.9 = 1,778 cases. Data were sourced from the sub-system of National Trauma Alliance Database, which does not require dropout rate considerations, collecting a total of 1,736 valid cases, meeting sample size requirements.

Table 1. General information between the survival and deceased trauma groups (n = 1,736).

Table 2. Variable threshold setting based on relevant literature.

2.4 Statistical methods

SPSS and R software were used for statistical analysis (16, 17). Non-normally distributed continuous variables were expressed as medians and interquartile ranges, while categorical variables were presented as frequencies and percentages. For continuous variables with missing values, mean imputation was performed stratified by outcome group (death/survival) to avoid bias caused by distribution differences between groups. Categorical variables were not imputed, as missing values might imply clinical meaning, and imputation could introduce artificial bias. Comparisons were performed using the Chi-square test (X²) or Fisher's exact test. Univariate analysis was used to evaluate the significance of each variable. Significant variables were included in a Logistic regression model for multivariate analysis to construct a predictive model and identify risk factors affecting trauma patient outcomes. Variables were also selected from high-dimensional data using Lasso regression (L1 regularization) to mitigate overfitting and enhance the model's generalization ability. Output results using the Random Forest package in R Studio Nc MSE. The terminal outcomes (death) served as the final predictive variables.

3 Results

3.1 General information

A total of 1,736 valid cases were collected, including 1,634 cases in the survival group (mean age: 60.52 ± 13.02 years) and 102 cases in the death group (mean age: 61.24 ± 12.56 years). Critically ill trauma patients accounted for 81.6% in the survival group and 80.4% in the deceased group. Initial vital signs measuring during triage in the emergency department showed no significant statistical difference between the two groups, including HR, P, Bp, SpO₂, Gender, and age (P > 0.05) (Table 1).

3.2 Analysis of factors related to clinical outcomes in emergency trauma patients

From Table 1, results indicated statistically significant differences between the survival and death groups regarding GCS scores, ISS scores, TI score FAST (Focused Assessment with Sonography for Trauma) completion time, chest X-ray completion time, and pelvic X-ray completion time (P < 0.001). However, due to the use of the revised trauma score (RTS) in clinical practice, which is more complex to calculate, some still use the simplified TI (trauma score) of the old version. Here, we used the latter. Although the two sets of data were compared with P < 0.05, the mean values of both groups were less than 9, which meaned that both were minor injuries and did not reach moderate or above severity. Therefore, they were excluded from the final model. Additionally, based on literature (Table 2), according to the injury detection mechanism, the severity of the condition requires the activation of the trauma team, so severity assessment was included in the regression model. CT scans are a crucial early diagnostic tool for internal organ and skeletal injuries, so CT examination time was also included in the regression model. Pre-hospital emergency transportation speed is the time from the triage nurse receiving the ambulance forecast call to the ambulance arriving at the hospital. It is critical in determining whether trauma patients receive timely and effective treatment, making it a key indicator for evaluating the efficiency of emergency care pathways, so pre-hospital transportation time was also included in the regression model. Finally, variables were brought into the final regression model including severeity assessment, GCS, ISS, pre-hospital transportation time, and examination time (X-ray, CT, FAST).

3.3 Feature selection using LASSO regression model

Using LASSO regression to select risk factors, variables with non-zero coefficients were deemed significant (18). The model achieved the minimum target parameter mean at λ = 0.117 and the simplest model within one standard deviation of the minimum value at λ = 0.689 [Log(λ) = −0.372]. At this point, seven predictive variables were included in the model (Figure 1).

3.4 Importance of variables using random forest model

A Random Forest model was used to analyze the importance of these selected factors. Output results using the random forest package in R Studio Inc MSE (Increase in Mean Squared Error) is the average decrease in accuracy %. The larger the MSE, the higher the importance of variables in influencing factors (19).The results showed that the most influential factors, ranked by importance, were: severity assessment, GCS score, blood pressure, ISS score, CT examination time, multiple injuries, pre-hospital transportation time, and completion of relevant examinations (Figure 2).

3.5 Multivariate analysis using logistic regression model

The above statistically different factors were used as independent variables, and the inclusion criteria for the relevant pathway times were set and assigned values based on literature evidence (Table 2) and clinical practice data (Table 1). The detailed assignment information was referred in Table 3. Logistic regression analysis identified the main factors influencing trauma patient clinical outcomes were severity (Emergency or Higher), GCS Score (< 15)ISS Score (≥16), Pre-hospital Transportation Time (>30 min), Pelvic X-ray Completion Time (>60 min), Chest X-ray Completion Time (>60 min), FAST Ultrasound Completion Time (>50 min), CT Scan Completion Time (>90 min) (Table 4). The results were consistent with the literature evidence.

Table 3. Variable assignments.

Table 4. Multivariate logistic regression analysis.

Pre-hospital transportation exceeding 30 min increased mortality risk by 1.83 times (Table 3). The extension of examination time increased the risk of death, which may be related to the determination of surgical plans based on examination results. The earlier the examination results of trauma patients (such as bleeding sites) could be obtained, the earlier surgical intervention can be carried out, thereby reducing the risk of death. Additionally, non-emergency trauma patients had a significantly higher mortality risk due to hidden symptoms, particularly in internal bleeding cases. The finding emphasized the need for early correct identification of internal injuries to improve survival rates (20).Therefore, these trauma scoring tools have important significance and role in effectively identifying critically ill trauma patients.

4 Discussion

4.1 Application of multiple predictive models

Based on a review of relevant literature and national trauma quality control indicators, this study incorporated key risk factors into predictive models. By comparing the predictive results of RF and LG models for the risk factors of emergency pathways in trauma patients, clinical medical staff can more intuitively perceive the mechanism of influencing factors in daily clinical work, thereby avoiding or reducing the impact of such factors on emergency procedures, which could help them make clinical decisions, optimize emergency procedures, and reduce the mortality rate of trauma patients. Hence, the aim was to identify factors influencing emergency trauma care pathways and reduce risks associated with treatment delays for critically ill patients. The findings were integrated into clinical practice by setting quality control indicators and defining time thresholds for essential diagnostic and treatment steps. This approach optimizes emergency trauma management, ensuring standardization and scientific rigor.

4.2 Difference of key predictive factors

While prior research had largely focused on medical factors such as pharmacological interventions and blood test results, there was limited literature on clinical nursing management and trauma care pathways (21). The study addressed this gap, making significant contributions by transforming abstract nursing management concepts into concrete, data-driven models. The research results indicated that both predictive models found injury assessment and examination timeliness play an important role in patient outcomes. The difference was that RF listed heart rate, respiration, and blood pressure as the main factors, but there was no statistical difference in LG (P > 0.05), which may be related to the differences in screening indicators between the two models. However, these indicators (HR, R, Bp) were also the most fundamental methods for evaluating whether a patient's vital signs were stable. Similarly, patients with multiple injuries may have more complex conditions than those with single injury, who may require multidisciplinary collaboration to determine the final plan. The integration of objective data into predictive models enhanced scientific validity, while optimizing clinical pathways based on these findings provided a practical framework for real-world application. By converting risk factors into a visualized early warning system, the study ensured that potential hazards could be identified and mitigated before they impacted patient outcomes. Therefore, effective evaluation and rapid examination identification are essential for providing correct clinical decisions to improve the survival rate of trauma patients (22). They are key control elements for the effective operation of the entire trauma process.

4.3 The importance of trauma assessment and diagnosis

Findings from multiple predictive models indicated that accurate trauma assessment played a crucial role in determining patient outcomes, aligning with results from international studies (23). RF analyzed highlight that early and precise evaluation of trauma severity was essential for effective emergency intervention, including triage mechanism and trauma scoring tools (GCS, ISS). LG further confirmed that trauma patients, except those classified as non-emergency cases, exhibit a significantly higher risk of mortality. There were differences in injury assessment and trauma scoring tools between two groups of patients. Various trauma severity scoring systems, such as GCS, ISS, are widely used in emergency medicine to assess patient condition. The results of the study reinforced the critical role of trauma assessment tools in guiding clinical decision-making and treatment prioritization. Given the sudden onset, hidden nature, and rapid progression of trauma-related conditions, emergency teams must be able to quickly and accurately assess trauma severity to determine the appropriate course of action.

4.4 The necessity of completing key diagnostic examinations

Trauma care is highly time-sensitive, requiring rapid identification of bleeding sites and injury severity to guide emergency interventions. The timely completion of critical diagnostic tests—including FAST ultrasound, CT scans, chest X-rays, and pelvic X-rays—is essential for accurate treatment planning. These diagnostic tools act as crucial links connecting emergency, surgical, and intensive care teams (6). In clinical practice, trauma patients often present subtle or delayed symptoms, making early identification challenging. This is particularly true for patients with internal bleeding, who may not exhibit obvious signs in the initial stages. However, as time progresses, their condition can rapidly deteriorate, leading to sudden clinical worsening and increased mortality risk. The results are consistent with previous international research (24).

Literature found that delays in completing key examinations were significantly associated with higher mortality rates. Prolonged diagnostic times can result in missed opportunities for timely surgical intervention, placing patients at greater risk of deterioration (25) (Table 2).

However, despite the recognized importance of imaging studies, the study also identified considerable delays in their completion. Clinically, these delays can often be attributed to unstable vital signs in severely injured patients, which require immediate resuscitation efforts before imaging can be performed. For instance, patients with severe hemorrhagic shock may need fluid resuscitation and airway management before being transported for imaging. In such cases, balancing stabilization efforts with the urgency of diagnostic evaluations is a critical challenge.

Additionally, in the RF model, the importance ranking of relevant examinations was relatively low, which may be related to the different examination methods required for different trauma sites and types. Hence, the study raised questions about the necessity of CT scans in all trauma cases. Consultations with trauma specialists revealed that FAST ultrasound may be sufficient for detecting internal bleeding, allowing for immediate surgical intervention without requiring patient transport. While CT scans provide detailed insights into internal organ and skeletal injuries, moving unstable trauma patients to a CT scanner may pose additional risks—particularly for those with suspected abdominal bleeding.

4.5 The impact of pre-hospital emergency transport time

The study found that pre-hospital transport times exceeding 30 min were associated with a 1.83-fold increase in mortality risk, a finding that aligns with previous international research (26). Pre-hospital transport time is a key determinant of patient survival, as delays in reaching a trauma center can significantly impact emergency interventions (27, 28).

The analysis revealed that the average emergency transport time was approximately 30 min, suggesting that improving pre-hospital response efficiency is a critical area for intervention (29). Several factors contribute to prolonged transport times, including: Severe trauma cases requiring on-site stabilization before transport, such as multi-trauma or internal bleeding cases. Urban traffic congestion, particularly during peak hours, which delays ambulance response. A limited number of trauma centers, requiring some patients to be transported over long distances. Given these challenges, the study highlights the urgent need for expanding trauma center capacity and optimizing emergency transport systems.

4.6 Recommendations and future directions

4.6.1 Increase triage and disease assessment training

Research showed that disease diagnosis and injury assessment played a key role in the subsequent multidisciplinary treatment process for medical personnel, including laboratory tests, imaging examinations, trauma care, blood transfusion, and surgical plans, et al. Therefore, the use of assessment tools and the ability of triage nurses to classify injuries could both affect patient outcomes. Regular training and case discussions should be conducted to retrospectively analyze special cases and gain experience and lessons from them (30).

4.6.2 Optimizing trauma care pathways through data-driven decision-making

By integrating predictive models into emergency management, hospitals can develop predictive algorithms that flag high-risk patients in real-time, ensuring proactive interventions and reduced treatment delays. The main factors affecting the path can be identified by LR, at the same time, the degree of impact of each factor on the path can be intuitively understood by RF, thereby simplifying the process and providing scientific evidence and reference for clinical emergency decision-making.

4.6.3 Strengthening regional trauma systems

Expanding trauma centers and improving hospital network integration will enable faster patient transfers, ensuring that critically ill trauma patients receive care at specialized facilities equipped to handle severe cases. One potential solution is the implementation of real-time traffic monitoring and GPS-based ambulance routing, allowing emergency teams to bypass congested areas. Additionally, public awareness campaigns encouraging drivers to yield to emergency vehicles can help reduce transport delays.

5 Conclusion

The findings of this study emphasize the importance of timely trauma assessment, efficient emergency workflows, and structured trauma care protocols in improving patient outcomes. By leveraging data-driven models and clinical expertise, future trauma management strategies can further reduce treatment delays and optimize emergency response systems.

6 Limitation

The study had several limitations. First, the predictive model lacked external validation, and its generalizability to other trauma systems and populations required further confirmation. Second, although key confounders were controlled for, residual confounding from unmeasured factors (e.g., detailed pre-hospital care) may affect the accuracy of the results. Third, the single-center design limited the generalizability of the findings to other regions or different levels of trauma care facilities. Additionally, the heterogeneity of included trauma mechanisms may obscure the impact of time thresholds in specific injury subgroups. Finally, the focus on short-term clinical outcomes precluded assessment of the long-term functional and quality-of-life consequences of treatment delays. Future research should involve multi-center prospective external validation and include comprehensive long-term follow-up data to further refine time-based quality indicators in trauma care.

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Footnotes

Edited by: Muhammad Waseem, Lincoln Medical Center, United States

Reviewed by: Jabeen Fayyaz, University of Toronto, Canada

Bassem Nashaat Beshey, Alexandria Faculty of Medicine, Egypt