SAD is a severe neurological syndrome that significantly increases mortality among affected patients, leading to long-term mental and cognitive impairments, and even dementia, placing a substantial burden on patients and their families (21, 22). We developed a machine learning prediction model based on GBM, which showed promising results in internal validation (AUC: 0.732). Utilizing the SHAP method, we enhanced the clinical interpretability of the model. Although several predictive models have been developed to assess the risk of SAD in the ICU, their practicality in clinical practice remains insufficient, failing to effectively translate into clinical tools (6, 7). Therefore, we created a simple web-based calculator to assist healthcare professionals in quickly identifying SAD and timely adjusting medical strategies to improve patient outcomes.

Currently, clinical recognition of SAD in the ICU is not optimistic, mainly due to physicians’ insufficient understanding of the complication. This underscores the importance of enhancing clinical awareness. Through systematic training, we can improve healthcare teams’ ability to recognize sepsis-associated delirium, thereby allowing for more accurate identification of early intervention opportunities and a scientific assessment of the risk-benefit ratio of treatment strategies. Commonly used delirium assessment methods in the ICU include CAM-ICU and the Intensive Care Delirium Screening Checklist (ICDSC). CAM-ICU is applicable for patients requiring mechanical ventilation, offering rapid and effective assessment; however, it is not suitable for deeply sedated or comatose patients and requires multiple evaluations for diagnosis (23–25). In contrast, the ICDSC has a broader applicability, as it does not require patient cooperation and is based on nurses observing patients’ behavior over a 24-hour period. Nonetheless, it relies on nurses’ subjective judgment, which may lead to lower consistency among different evaluators and increase the nursing workload (26–28). The time-consuming nature of both methods may delay treatment decisions and compromise patient safety. To address this issue, we constructed a web-based calculator using the GBM model to enable clinicians to quickly detect SAD early. It is important to emphasize that, although preliminary validation shows promising performance, multi-center, prospective cohort studies are still required to ensure the model’s applicability in various clinical scenarios, providing external validation with independent datasets. This will help objectively assess the model’s generalizability and diagnostic accuracy, offering evidence-based medicine to support its clinical translation.

Feature selection is a crucial step in building predictive models, as its validity directly affects the model’s predictive performance (29). We obtained a substantial sample from the MIMIC-IV database and utilized MLR to identify independent risk factors for SAD patients. Lasso regression was applied to process features, avoiding multicollinearity and reducing the risk of overfitting (30). Additionally, we utilized the Boruta algorithm for variable selection, employing a rigorous statistical significance filtering mechanism to retain key features while suppressing model complexity and enhancing the model’s generalizability on independent datasets. Ultimately, we included common variables to achieve accurate feature selection and stability. In the realm of healthcare big data, machine learning algorithms demonstrate significant advantages in handling complex medical data (31). Compared to traditional statistical models, the nonlinear modeling capabilities of machine learning can capture higher-order interactions between variables and enable multivariable synchronous analysis of high-dimensional data through parallel computing frameworks, thereby increasing the ability to predict diseases (32). In this study, we applied eight machine learning models for performance comparison and ultimately chose GBM as the best model.

GBM is one of the most representative methods in ensemble learning, which iteratively combines weak learners and utilizes gradient descent to optimize prediction errors, simulating the progressive learning and collaborative optimization characteristics found in biological systems (33–35). As an efficient predictive model, GBM is able to capture nonlinear relationships and complex interaction effects in the data and is widely used in the construction of clinical prediction models. The clinical interpretability of machine learning models is critical for medical practice; however, interpretability has long been one of the core challenges in this field (36). To address this issue, we adopted the SHAP method to analyze features and enhance model interpretability (37). Compared to traditional weight-based explanatory methods, SHAP exhibits superior consistency and performance, while demonstrating greater stability across various models (38). This study utilized SHAP value analysis, which significantly improved the model’s interpretability compared to the coefficient interpretations of traditional generalized linear regression models. SHAP values not only quantify the contributions of each feature to the predictive outcomes but also provide intuitive visualizations through feature importance plots (39). This analytical approach offers a new perspective for understanding the decision-making mechanisms of machine learning models, clearly illustrating the specific impacts of feature variables on model predictions, thereby effectively enhancing the model’s interpretability and transparency.

According to the SHAP feature importance plot, a low GCS score is identified as the most significant risk factor for SAD. Studies have shown that low GCS scores, high SOFA scores, advanced age, prolonged ICU stay, hypernatremia, and the use of midazolam are all risk factors for SAD. Specifically, the GCS score is one of the strong predictive features, originally designed by Graham Teasdale and Bryan Jennett at the University of Glasgow for assessing traumatic brain injury. It quantifies eye-opening, verbal, and motor responses to objectively evaluate the degree of consciousness impairment (40). The lower the GCS score, the higher the risk of delirium; research indicates that for each point decrease in GCS, the risk of delirium increases by approximately 34% (41, 42). Notably, SHAP analysis also revealed a complex nonlinear relationship between GCS scores and SAD: scores of 1-3 typically indicate severe brain injury, whereas a score of 15 suggests full consciousness; the probability of delirium occurrence is significantly higher in the former group compared to those in the intermediate scoring range, a phenomenon that has also been reported in other delirium prediction models (43). The SOFA score is an effective tool for assessing organ dysfunction in sepsis patients, with the central nervous system score relying on the GCS. By treating GCS as an independent variable, we avoided the limitation of the SOFA score, which might focus solely on single-organ function assessment, thus more accurately reflecting brain function impairment. An elevated SOFA score signifies systemic inflammatory response, tissue hypoxia, and organ dysfunction, with central nervous system dysfunction being the most common. These factors are interrelated via a systemic inflammation-brain injury axis, where many inflammatory factors can disrupt the blood-brain barrier and subsequently induce delirium (44–46). Research has found that patients with a SOFA score exceeding 9 have a probability of SAD occurrence greater than 70%, making the SOFA score a core indicator for predicting SAD; a high SOFA score is an independent risk factor for delirium (47).

Advanced age and prolonged ICU stay are both significant risk factors for delirium. As age increases, older patients often have various chronic diseases, malnutrition, sensory impairments, and cognitive deficits, resulting in decreased brain physiological reserve, thereby heightening the probability of delirium (48). Patients who are in the ICU for extended periods are more likely to be exposed to mechanical ventilation, sedative medications, sleep deprivation, and infections, which can further trigger delirium. Delirious patients commonly exhibit complications such as agitation, respiratory dysfunction, and infections, which may significantly prolong their ICU stays, creating a vicious cycle. Studies have shown that remaining in the ICU for more than 7 days can transition delirium from an “acute” to a “persistent” state, with about 30%-40% of patients experiencing long-term cognitive sequelae (49, 50). Additionally, midazolam, as a benzodiazepine, suppresses the central nervous system, potentially interfering with cholinergic neurotransmission and disrupting sleep architecture, thus increasing the risk of delirium. Our findings indicate that the pre-illness use of midazolam significantly raises the risk of delirium, consistent with recent studies (51, 52). Hypernatremia also contributes to an increased incidence of delirium. Hypernatremia affects sodium-potassium pump function, leading to abnormal neural excitability and interfering with the release of inhibitory neurotransmitters such as gamma-aminobutyric acid. Furthermore, hypernatremia decreases brain energy metabolism, affecting glucose utilization and ATP production, resulting in insufficient neuronal energy, which further contributes to the onset of delirium (53–55).

In summary, the SHAP method provides significant support for personalized diagnosis and treatment of SAD patients. By quantifying the contributions of various feature variables to predictive outcomes, it enhances the interpretability of the model’s predictions and offers intuitive decision-making support for clinicians. The SHAP values clearly illustrate the impact of clinical features on patient prognosis, aiding physicians in identifying key risk factors and formulating personalized intervention strategies. By integrating GBM and SHAP, this model not only improves predictive accuracy but also significantly enhances interpretability. The visualization of SHAP values allows clinicians to gain deeper insights into important features and their interactions, thereby increasing the transparency and credibility of medical decisions and providing a scientific basis for the personalized risk management of SAD. This interpretable machine learning approach holds substantial practical value in clinical settings and advances the development of precision medicine.

This study has some limitations. First, as a retrospective analysis, the nature of the study restricts the establishment of causal relationships between features and outcomes. Additionally, certain features (such as PCT and IL-6) had limited data availability in the database, resulting in the exclusion of some potential risk factors from the predictive model. Therefore, future prospective randomized controlled clinical trials should be conducted to validate the model’s effectiveness. Second, the data originated from a single center, lacking sufficient external validation, which may affect the credibility of the GBM model. Lastly, the limitations of sample size reduce the performance of the machine learning algorithms. Future research should establish a systematic data collection mechanism to expand the sample size and diversity, thereby improving the predictive accuracy and reliability of the model and ensuring that the research findings have clinical application value.