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

Background Trauma often involves multiple organs and systems, making it difficult for a single-discipline medical team to provide comprehensive care. Therefore, multidisciplinary teamwork is essential to improve the success rate of trauma treatment, reduce mortality, and enhance patient outcomes. Based on literature evidence, it is recommended that trauma patients complete the corresponding examinations, treatments, and nursing processes within the prescribed time, in order to improve the cure rate and survival rate. However, Trauma care encompasses multiple medical stages and pathways, including pre-hospital emergency care, emergency room rescue, surgery, intensive care, and rehabilitation. Thus, ensuring that each procedure proceeds smoothly, pathways remain unobstructed, the time of diagnostic and treatment is shortened as quickly as possible, resources are allocated efficiently, and critical patients receive priority care are key factors in improving clinical outcomes for patients. Therefore, this study combined the current situation with literature evidence, to explore and compare the risk factors in the emergency pathway of trauma patients by different predictive models. Objective To explore the multi-factor analysis of clinical emergency treatment pathways for emergency trauma patients based on their final outcomes, and to screen risk factors affecting the clinical outcomes of trauma patients using multiple predictive models. Methods Data were collected from emergency trauma patients treated at the hospital from January 1, 2024, to December 31, 2024, sourced from sub-system of the National Trauma Alliance Database, which divided into survival group and death group. Besides, setting indicator thresholds based on literature evidence and clinical status. Clinical emergency treatment factors affecting patient outcomes were analyzed by two predictive models. One was by LASSO regression to scan the factors and compared the factors by Radom Forest model. The other one was by Logistic regression. Results A total of 1,736 valid cases were collected. Among them, there were 1,634 cases in the survival group and 102 cases in the death group. Based on LASSO regression, Log(λ) = −0.372, and at this point, 7 predictor variables were included in the model. With the outcome of trauma patients as the dependent variable, the variables selected by the LASSO method were incorporated into the random forest model. The importance of variables from high to low was as follows: condition assessment, GCS score, blood pressure, ISS score, CT examination time, multiple site injuries, pre-hospital transport time, and completion of related examinations. Based on logistic regression, condition assessment, GCS score, ISS score, pre-hospital transport time, and time from arrival at the emergency department to completion of examinations were the main factors affecting the final outcome of patients ( P < 0.001). Conclusion Machine learning models can visualize the importance of risk factors in clinical pathways, enabling medical personnel to have a clearer understanding of emergency elements during the emergency process. Logistic regression could focus more on the construction of indicators, expressing clinical pathways through objective data. In addition, LASSO regression was more convenient and reduced bias compared to traditional regression methods in terms of screening variables. The ultimate goal was to assist clinical healthcare professionals in visualizing risks along pathways through evidence-based methods, thus establishing corresponding quality control indicators, which can optimize pathways to improve the quality and efficiency of trauma care.