Trauma-related disorders have emerged as a critical global public health challenge. According to World Health Organization statistics, the socioeconomic burden attributable to traumatic injuries has risen to become the second leading contributor to the global disease burden (Galbraith et al., 2023). As a distinct subtype of trauma, polytrauma is characterized by complex pathophysiology, multi-system complications, and prolonged hospitalization, necessitating multidisciplinary collaborative care throughout treatment (Sumann et al., 2020). While damage control resuscitation (DCR) protocols significantly improve hemodynamic stability and survival rates, we observe increased neurological complication rates in this surviving cohort–particularly in patients requiring ≥6 units of blood transfusion. This complication profile reflects emergent pathophysiological perturbations in severely injured patients who survive initial resuscitation, rather than a direct consequence of DCR strategy (Kruithof et al., 2020). Among these, delirium—a severe neuropsychiatric syndrome with substantial prognostic implications—has been reported to affect 24% of emergency polytrauma patients. This condition not only prolongs mechanical ventilation duration and increases unplanned extubation risks but also induces long-term cognitive impairment, severely compromising patients’ quality of life (Von Rueden et al., 2017).

Although the American Guidelines for Critical Care Medicine explicitly recommend incorporating delirium screening into routine ICU care protocols, significant diagnostic gaps persist in clinical practice (Safdar et al., 2024). Studies indicate that healthcare providers actively identify delirium in 15%–20% of cases (Tonna et al., 2021). This disparity between knowledge and implementation may stem from three interrelated challenges: (1) the heterogeneous clinical manifestations driven by delirium’s complex pathophysiological mechanisms; (2) the high expertise requirements for administering validated assessment tools like the Confusion Assessment Method for the ICU (CAM-ICU); and (3) the inadequacy of traditional risk factor analysis in addressing dynamically evolving clinical features of polytrauma patients. Current delirium prediction models predominantly focus on geriatric or elective postoperative populations, with scarce systematic investigations into personalized model development for emergency trauma cohorts. This knowledge gap substantially hinders evidence-based implementation of precision preventive strategies (Heinrich et al., 2022).

Emerging evidence highlights the unique value of machine learning (ML) in predicting critical illness outcomes (Li et al., 2024; Fan et al., 2023; Tobin et al., 2024). Gong et al. developed a predictive model achieving an AUC of 0.845 (95% CI: 0.831–0.859), demonstrating the clinical potential of risk stratification in delirium management (Gong et al., 2023). However, significant challenges arise when adapting such models to emergency polytrauma scenarios. Key limitations include: (1) omission of trauma-specific indicators such as Injury Severity Score (ISS); (2) insufficient capacity of linear regression methods to capture complex variable interactions; and (3) unresolved conflicts between rapid decision-making demands and model usability in emergency settings (Rostam Niakan Kalhori, 2022). These shortcomings underscore the urgent need for context-specific predictive tools.

Building upon this rationale, our study innovatively integrates three pivotal components: (1) comprehensive trauma care cycle data collection; (2) adaptive ML algorithms optimized for dynamic clinical environments; and (3) implementation of the SHapley Additive exPlanations (SHAP) framework for transparent interpretation of model decisions. By synergizing advanced information technologies with traditional clinical research paradigms, this multidisciplinary approach aims to provide an intelligent solution for delirium prevention and management in emergency polytrauma patients, ultimately advancing the quality of trauma care delivery.

Our study employed a multicenter retrospective design to develop an adaptive machine learning model based on an improved flood optimization algorithm (IFLA) for predicting delirium risk in emergency department (ED) patients with multiple trauma. Results demonstrated that the optimized IFLA model significantly outperformed traditional models (e.g., logistic regression and XGBoost) in key metrics including AUC and F1 scores. By integrating sine mapping initialization and Cauchy mutation perturbation strategies, the IFLA algorithm successfully overcame the local optimum trapping inherent in the conventional FLA, a finding corroborated by standard benchmark function tests. This innovative approach aligns with the algorithmic enhancements proposed by Gao et al. in COVID-19 prediction models (Gao et al., 2022). Through SHAP interpretability analysis, five critical predictors were identified: Glasgow Coma Scale (GCS) score, fibrin degradation products (FDP), lactate levels, body mass index (BMI), and Clinical Frailty Scale (CFS). Notably, GCS score exhibited the highest SHAP value contribution (26.8%). The real-time decision support system embedded in our model demonstrated favorable clinical acceptance during ED validation, indicating substantial translational potential.

Current delirium prediction research primarily focuses on medical or postoperative cohorts (Tobin et al., 2024; Gong et al., 2023; Rostam Niakan Kalhori, 2022; LaGrone et al., 2024; Chen et al., 2021; Shimura et al., 2017; Church et al., 2020; Ghasemi et al., 2024; Sharma and Raju, 2024; Gao et al., 2022; Liu et al., 2025; Shpakov et al., 2023; Saviano et al., 2023), with limited models specifically designed for trauma populations. Conventional linear regression approaches frequently exhibit inadequate predictive performance (AUC typically <0.85 (Matsuoka et al., 2021)) due to their limited capacity for modeling nonlinear relationships. Compared to rapid decision tree models developed in previous studies (Xie et al., 2022), our model demonstrated superior adaptability for polytrauma patients through the inclusion of trauma-specific indicators such as Injury Severity Score (ISS). While ISS significantly discriminated between groups, its utility as a reliable predictor was constrained by the limited critical trauma representation in our cohort, necessitating exclusion during model optimization. Future large-scale studies should validate its reintegration to enhance trauma-specific applicability. While Dana’s emergency informatics framework emphasizes data acquisition efficiency (Im et al., 2023), our study achieved concurrent feature selection and parameter optimization using AutoML technology, significantly enhancing computational efficiency. Importantly, previous research has largely overlooked the predictive value of coagulation markers (Stollings et al., 2021), whereas our findings highlight the critical role of FDP dynamics in delirium risk stratification, potentially mediated by neuroinflammatory cascades secondary to microcirculatory dysfunction in polytrauma (Bramley et al., 2021). Although Kang et al.'s sleep quality intervention reduced delirium incidence (Kang et al., 2023), its reliance on subjective clinician assessments contrasts with our objective predictive model that enables early targeted interventions.

Model refinement and SHAP analysis identified five core predictors, with their pathophysiological implications analyzed as follows: (1) GCS score: As a standardized consciousness assessment tool, GCS showed an inverse correlation with delirium risk. Severe brain injury (GCS ≤8) may trigger thalamocortical feedback loop dysregulation (attentional deficits), locus coeruleus norepinephrine system hyperactivation (neurotransmitter imbalance), and blood-brain barrier disruption (neuroinflammation via IL-6/TNF-α infiltration) (Raquer et al., 2024). For ED physicians, dynamic GCS monitoring (particularly in TBI patients) facilitates early identification of high-risk individuals (GCS ≤12), enabling timely preventive measures. (2) Lactate levels: This biomarker of tissue hypoperfusion quantifies oxygen metabolism dysregulation. Levels >2 mmol/L promote delirium via three pathways: 1) astrocytic glutamate uptake inhibition (excitotoxicity); 2) microglial TLR4/NF-κB pathway activation (neuroinflammation); 3) cerebral acidosis impairing neurotransmitter dynamics (Qian et al., 2024). The sharp SHAP value increase at >4 mmol/L suggests a threshold effect. Integrating central venous oxygen saturation monitoring for fluid resuscitation optimization (as shown by Taylor et al. (Taylor et al., 2022)) could reduce delirium incidence by 19%. (3) Clinical Frailty Scale (CFS): Scores ≥5 indicate depleted physiological reserves, amplifying trauma effects through immunosenescence (sustained inflammation), autonomic dysregulation (circadian disruption), and altered pharmacokinetics (sedative accumulation) (Zhang et al., 2021; Mazzola et al., 2021). Our model ranks CFS third in SHAP importance, warranting “precision trauma care” strategies including nutritional support (protein ≥1.2 g/kg/day), early mobilization (bedside sitting within 24h), and benzodiazepine restriction. (4) BMI: The U-shaped delirium risk (optimal range 18.5–24.9 kg/m² (Feinkohl et al., 2023)) reflects dual mechanisms: low BMI exacerbates catabolism (neurotransmitter precursor deficiency), while obesity induces leptin resistance (insulin resistance/BBB disruption). Obese patients require vigilance for occult hypoperfusion from intra-abdominal hypertension, whereas underweight patients may benefit from enteral nutrition with branched-chain amino acids (Fu et al., 2024). (5) FDP: Elevated FDP (>20 μg/mL) signals coagulopathy via complement C5a activation (microvascular NETosis) and competitive fibrinogen inhibition (hemorrhagic risk) (Payne et al., 2024). Six-hourly FDP monitoring combined with tranexamic acid administration may mitigate microcirculatory dysfunction-related delirium (Lu et al., 2024).

Despite constructing this automated prediction model, limitations persist in data quality and clinical implementation: Data source bias: Though standardized across six regional hospitals, geographical disparities in trauma protocols and monitoring standardization may introduce bias. While multicenter recruitment enhances external validity, the moderate cohort size limited subgroup analyses for rare trauma phenotypes. We also recognize inherent inter-hospital variability in scoring systems despite standardized training. Future large-scale validation should prioritize algorithmic adaptation to institution-specific documentation patterns using federated learning frameworks. While median imputation mitigates leakage risk, FDP remains susceptible to bias due to its higher missing rate (2.6%). Future studies should employ advanced methods like multiple imputation chained equations (MICE) for variables exceeding 2% missingness. Retrospective acquisition of FDP values, though demonstrating critical prognostic value, necessitates future validation of our dynamic prediction updating protocol in prospective studies employing point-of-care viscoelastic testing to eliminate turnaround delays. Model constraints: Missing core variables and incomplete capture of nonlinear interactions may reduce sensitivity in complex trauma scenarios. Temporal resolution: Static-input prediction systems face intrinsic latency in dynamic ED environments requiring real-time biomarker feedback (e.g., rapidly changing lactate/FDP). Study design limitations: While retrospective validation provides preliminary evidence, prospective cohorts remain essential for examining delirium’s temporal progression and intervention dynamics.

The Intensive Care Big Data Steward Consensus publishes future industry standards in this area (Su et al., 2024), this consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. Aligned with the Intensive Care Big Data Consensus, our future research framework will embed its 29 evidence-based recommendations across five core dimensions: establishing harmonized multimodal trauma databases adhering to standardized ICU data protocols, implementing federated learning architectures for privacy-preserving multicenter integration, applying advanced AutoML optimization for feature engineering, developing clinical translation pathways within evidence-based safety parameters, and creating real-time SHAP interpretability dashboards for predictive governance. This structured methodology will operationalize the consensus guidelines—particularly regarding scientific question formulation, database standardization, and ethical computational methods—as applied to dynamic delirium prediction in trauma ecosystems. It includes the following aspects: (1) Data integration: Establish multimodal trauma databases incorporating real-time vital signs, continuous EEG, and cytokine profiles to transcend retrospective “time-slice” limitations. Our real-time data pipeline implements sliding-window RNNs for hourly risk-score updates coupled with automatic quarterly calibration audits against AAST/WSES standards, ensuring temporal relevance through federated learning with patient-level partitioning. (2) Algorithm enhancement: Develop spatiotemporal architectures (e.g., temporal convolutional networks for biomarker trends, graph neural networks for multi-organ injury topology) to transition from “point prediction” to “process warning.” We implement federated learning and ensemble transition strategies where legacy models are progressively weighted with PAN-GAN-synthesized newer cohorts, enabling continuous adaptation to clinical practice shifts during model development. (3) Clinical translation: Implement edge computing-embedded decision systems integrated with bedside monitors/laboratory streams during the “golden hour” of trauma care. Edge-computing-embedded decision systems integrated with bedside monitors/laboratory streams during the “golden hour” now incorporate SHAP-based performance dashboards triggering alerts for critical predictor drift (e.g., >1.5σ change in GCS or FDP contributions). Future iterations should integrate multimodal neurological assessments such as the Full Outline of UnResponsiveness (FOUR) scale to enhance sensitivity in patients with communication barriers (e.g., intubation, aphasia). To advance translational implementation, our research road now explicitly prioritizes EHR interoperability through three parallel initiatives: Development of HL7 FHIR-compliant APIs enabling automated data exchange with hospital information systems at participating centers; Design of clinician-centered mobile interfaces with offline functionality to support bedside risk stratification during resuscitation, featuring real-time SHAP visualizations when FDP trends exceed >1.5σ baseline deviations; Prospective workflow integration trials launching Q4-2026 to quantify adoption metrics and time-motion efficiency gains using the System Usability Scale across three trauma networks. This aligns with our prioritization of spatiotemporal feature engineering and edge-computing integration, potentially improving real-time risk stratification during the “golden hour” of trauma care. Synergizing evidence-based medicine with AI could enable personalized interventions (e.g., circadian modulation for high-CFS patients, anticoagulant optimization for coagulopathic cases), ultimately creating a closed-loop “prediction-intervention-verification” ecosystem through SHAP-guided precision pathways.