The majority of severe trauma patients require treatment in the intensive care unit (ICU). Due to improvements in clinical treatment and care in recent years, the mortality rate of these patients has decreased (1, 2). However, surviving patients often experience prolonged stays in the ICU and enter a state of Chronic Critical Illness (CCI) (1, 3). In 2012, Gentile et al. coined the term Post-Intensive Care Syndrome (PIICS) and defined its clinical determinants as Persistent Inflammation, Immunosuppression, and Catabolism Syndrome. These determinants include prolonged hospitalization (>14 days), inflammation (C-reactive protein levels >150 μg/dL), immune suppression (lymphocyte count <800/μL), and catabolism (weight loss >10% during hospitalization or BMI <18.5 kg/m²) (1, 4). PIICS is characterized by prolonged dysregulation of the inflammatory response, immune dysfunction, and catabolic state, resulting in a range of adverse outcomes, including infection, organ dysfunction, and impaired wound healing (2, 5). Early identification and prediction of PIICS in trauma patients are crucial for optimizing patient management and improving long-term prognosis.

The development and validation of predictive models specifically designed for trauma patients can assist healthcare professionals in identifying high-risk individuals and preventing PIICS-related complications (6). Predictive models can integrate clinical and demographic variables, provide early risk stratification, and facilitate targeted interventions. Currently, there is a primary focus on predictive models for PIICS in critically ill patients in different clinical settings, such as sepsis and major surgeries (6, 7). However, trauma patients present unique challenges and characteristics (5). The pathological mechanisms underlying the development of PIICS in trauma patients are not yet clear, and factors such as the severity of the injury, anatomical location, and surgical interventions may significantly influence the risk and trajectory of PIICS (5, 8–10). Therefore, there is a need to establish a robust dataset encompassing diverse demographic characteristics, injury severity, clinical variables, and biomarker measurements, and employ advanced statistical techniques and machine learning algorithms to derive predictive models with good discriminative and calibration abilities, enhancing their applicability in clinical practice.

This study aims to develop and validate a predictive model specifically for severe trauma patients to predict PIICS. The model will incorporate clinical and injury-related variables to provide physicians with a reliable tool for assessing the risk of PIICS in individual trauma patients. By identifying high-risk patients early on, healthcare professionals can implement targeted interventions to modulate dysregulated inflammatory responses and alleviate the occurrence and progression of PIICS-related complications. This effective prediction approach can be utilized to reduce the risk of PIICS-related complications and improve patient recovery and long-term health.

A prospective survey was conducted from October 2020 to September 2022 to collect data from severe trauma patients aged 18 and above admitted to the Trauma Intensive Care Unit (ICU) of Tongji Hospital, Huazhong University of Science and Technology School of Medicine. Clinical data within 24 h of admission were assessed and recorded, including age, gender, mechanism of injury (MOI), body mass index (BMI), Injury Severity Score (ISS), Nutritional Risk Screening 2002 (NRS 2002), Sequential Organ Failure Assessment (SOFA) score, and Acute Physiology and Chronic Health Evaluation II score (APACHE II score). Laboratory examinations at admission included hemoglobin (g/L), lymphocyte count (*10⁹/L), albumin (g/L), and lactate (mmol/L). The BMI was calculated as weight divided by height squared (kg/m²). 14 days after admission, the relevant indicators of PIICS were recorded and evaluated. Specific indicators include inflammation (C-reactive protein level), immune suppression (lymphocyte count), and catabolism (weight loss during hospitalization or BMI). This research plan has been approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (approval number: TJ-IRB20230214). According to the guidelines, the study satisfied the conditions to waive the requirement for informed consent from individual participants. Therefore, informed consent was waived by Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. All procedures were carried out following relevant guidelines and regulations.

In our study, we employed rigorous methods to develop and validate the nomogram, and obtaining a series of findings. (a) We retrospectively collected a large amount of data from severely traumatized patients and identified potential risk factors associated with the development of PIICS. These factors included demographic characteristics, injury severity scores, and other relevant clinical parameters. (b) Through multivariate analysis, we identified the most significant predictors and incorporated them into the nomogram. (c)The LASSO regression combined with 10-fold cross-validation was used to select four risk factors, including NRS2002, ISS, APACHE II, and SOFA, to construct a logistic model for predicting the risk of PIICS in severely traumatized patients. These findings provide an effective way to screen out PIICS early in our clinical work and may have a positive impact on patient care.

Various scoring systems with potential for predicting poor outcomes in severe trauma have been explored. NRS2002 is a tool for assessing nutritional risk in patients, considering factors such as nutritional intake, weight changes, and illness status to evaluate the level of nutritional risk (11). It has been reported to have predictive value in complications of severe trauma and is highly correlated with increased length of hospital stay (LOS) (12). ISS is a scoring system that assesses the severity of trauma based on the location and severity of injuries and is the most commonly used prognostic score in clinically severe multiple trauma patients (13, 14). APACHE II considers physiological indicators (e.g., blood pressure, respiratory rate, body temperature) and illness status (e.g., chronic diseases, age) to determine patient severity and prognosis (15) and has good performance in assessing in-hospital mortality in emergency trauma patients (16). The SOFA scoring system is used to assess the severity of multiorgan dysfunction in critically ill patients and has better performance in predicting mortality in both non-trauma and trauma patients (17, 18). However, individual scoring systems have limitations in predicting the complex complication of PIICS. Developing an early predictive model for PIICS is an important step in the prevention and management of complications in severe trauma (19). Therefore, an increasing number of studies have focused on exploring predictive factors for the occurrence of PIICS.

Different models have been developed for predicting fatigue syndrome and poor outcomes in elderly trauma patients (22), which have demonstrated good predictive and evaluative capabilities in trauma patients. By combining multiple variables and their respective weights, the nomogram provides a visualized model for risk prediction (23, 24), enabling clinicians to make effective predictions of the probability of PIICS occurrence in individual patients. This can aid in identifying high-risk patients and implementing targeted interventions, such as immunomodulatory therapy or nutritional support (25), to mitigate the progression of PIICS and its associated complications.

Compared to traditional prediction models or scoring systems, this model offers several advantages. Firstly, it incorporates a wide range of variables that capture the complexity of trauma patients developing PIICS. This comprehensive approach enhances the accuracy of risk prediction. Secondly, it provides a practical tool for clinicians to conduct real-time risk assessments in clinical practice. By inputting a patient’s clinical data into the model, an immediate estimation of the likelihood of PIICS development can be obtained, enabling early intervention.

Although our study has strengths, there are also limitations to consider. Firstly, the retrospective design introduces inherent biases and potential confounding factors. Prospective validation in well-designed cohorts would be valuable to confirm the generalizability of the nomogram. Secondly, our nomogram was developed and validated in a specific population of severely traumatized individuals, and its performance needs to be evaluated in other patient populations or healthcare settings. Furthermore, further validation in different centers or countries is required to ensure its applicability.

In conclusion, the prognostic model developed in this study demonstrates good accuracy and discriminative ability. Developing and validating a predictive model specifically for PIICS in trauma patients is a crucial step toward personalized and proactive management of this complex syndrome. By utilizing existing data and analytical techniques, improving the prediction of risk stratification for severe trauma patients during hospitalization provides valuable insights for clinicians.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by this research plan has been approved by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (approval number: TJ-IRB20230214). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

LX, ZK, DW, YL, and CW undertook the research, LX and YW wrote the main manuscript text and prepared figures. ZL, XB, and YW revised the article critically for important intellectual content and final approval of the version to be submitted. All authors contributed to the design of the study and the writing of the manuscript and reviewed the manuscript.

This study was supported by grants from the National Natural Science Foundation of China (Grant No. 82002101).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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