Sepsis is triggered by an acute infection that induces an excessive and dysregulated immune response in the body, causing multiple organ dysfunctions (1). Each year, approximately 49 million people worldwide are affected by sepsis (2), and about 30% of intensive care unit (ICU) patients are diagnosed with it (3). The mortality rate for sepsis can be as high as 40% (4). Treatment options and outcomes for sepsis patients vary widely due to the differences in infectious agents, individual characteristics, and medical history. Consequently, it is impractical to assess and manage sepsis cases using a single scoring system, and greater attention should be given to the heterogeneity among sepsis patients (5).

Lung cancer remains one of the most prevalent and deadly tumors in the world, with complex challenges in diagnosis, staging, therapy, and future outlook. Accurate diagnosis and staging are crucial for guiding treatment decisions and assessing prognosis. Diagnostic methods involve imaging techniques such as computed tomography (CT) and positron emission tomography (PET), along with histopathological examination and the use of molecular markers to identify specific mutations (e.g., EGFR, ALK) (6). Molecular typing of lung carcinoma, particularly the distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), has further refined treatment approaches, enabling targeted therapies that improve patient outcomes (7). Treatment strategies for lung cancer have evolved significantly, transitioning from conventional chemotherapy and radiation to more individualized approaches. Targeted therapies and immune checkpoint inhibitors, which harness the body’s immune response against cancer cells, have demonstrated efficacy in NSCLC patients with specific genetic alterations (8). For instance, EGFR inhibitors and ALK inhibitors have shown improved survival in patients with these mutations (9). Meanwhile, immunotherapies such as PD-1 and PD-L1 inhibitors have revolutionized treatment for advanced or metastatic cases by extending survival times, although their efficacy varies widely among patients (10). In clinical practice, diagnosing and treating patients with lung cancer complicated by sepsis presents significant challenges. Patients with lung cancer often have compromised immune function, making them more susceptible to infections that can rapidly progress to sepsis, leading to multi-organ dysfunction and increased mortality (11). The complexity of managing lung cancer with concurrent sepsis stems from factors such as tumor burden, immunosuppression, and the adverse effects of anticancer treatments, which complicate early diagnosis and treatment strategies (12).

Nowadays, nomograms have gained widespread application in predicting tumor mortality (13). However, the sensitivity, specificity and generalizability of the previous models, as well as the existing assessment tools, could be inadequate, highlighting the pressing need for more accurate and specific prognostic prediction methods (14). Machine learning (ML), a branch of artificial intelligence, has garnered increasing popularity owing to its proficiency in managing complex, non-linear relationships, especially when dealing with large datasets and loosely structured data (15). The emergence of big data analytics and ML algorithms has rendered new approaches for identifying risk factors influencing prediction feasible. A number of predictive models utilizing these technologies have demonstrated exceptional performance and are progressively being incorporated into clinical practice (16). Nevertheless, to date, no elaborate model exists for predicting ICU mortality in patients with sepsis complicated by lung cancer, underscoring the necessity for the construction and verification of a new ML model for risk stratification. To the best of our knowledge, our research represents the first endeavor to construct and verify a predictive model employing multiple ML algorithms for ICU mortality prediction in patients with sepsis and lung cancer. Our model harnesses extensive population information and the competence of ML, thereby providing an individual predictive model that can help clinicians in meticulously appraising the ICU mortality risk of septic patients with lung cancer.

Because of the immunosuppression occurred in cancer patients, sepsis may begin and develop suddenly. The co-occurrence of lung cancer and sepsis presents significant challenges in diagnosis, treatment, and prognosis. Diagnostically, distinguishing between infection-induced systemic inflammatory response syndrome (SIRS) and tumor-related fever is complex, often leading to delays in appropriate therapy. Advanced imaging and biomarker analysis are essential but may be limited by the patient’s critical condition, calling a need for an outstanding biomarker to predict prognosis. In our study, we found that Oxford Acute Severity of Illness Score (OASIS) has the maximum predictability for ICU mortality in patients with sepsis and lung cancer. Several studies have compared OASIS with other severity scores such as Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), and Acute Physiology and Chronic Health Evaluation II (APACHE-II). A previous study demonstrated that OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of acute kidney injury patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute (26). Another study compared APACHE II, SOFA, SAPS II, and OASIS in predicting mortality in patients with sepsis or septic shock. The study found that all scoring systems were positively correlated with mortality, with SAPS II and OASIS showing higher correlations compared to others (27). These studies support our findings that OASIS is a robust predictor in the context of critical illness, which shed light on its predictive capabilities for septic patients with lung cancer.

Therapeutically, managing sepsis in lung cancer patients requires a delicate balance. Immunosuppressive effects of chemotherapy and the cancer itself increase susceptibility to infections, complicating sepsis management. Broad-spectrum antibiotics are standard; however, the potential for drug interactions and organ dysfunction necessitates careful selection and dosing. Recent studies have explored targeted therapies, such as aumolertinib, a third-generation EGFR-TKI, which has shown effectiveness in NSCLC cases with EGFR mutations (28). In the phase 3 AENEAS trial, aumolertinib significantly extended progression-free survival compared to gefitinib in patients diagnosed as advanced EGFR mutation-positive NSCLC (29). Prognostically, the combination of lung cancer and sepsis portends a poor outcome. Sepsis exacerbates the already compromised physiological state due to malignancy, leading to higher mortality rates. Early recognition and prompt, aggressive treatment of sepsis are crucial to improving survival (30).

Due to these challenges, accurate prediction of ICU mortality and identification of its risk factors are crucial for lung cancer patients with sepsis. The goal of our study is to establish a novel ML model for early ICU mortality prediction. By collecting essential clinic information and constructing ML models using a benchmark framework, we calculated risk scores for ICU mortality prediction, enabling precise prediction of ICU death probability. Once the risk tiers are established, the next step is to translating these into actionable clinical adjustments, which involves tailoring interventions based on the identified risk level. The clinical significance of this work is in enhancing patient management and therapy plan for patients with both lung cancer and sepsis, aiding clinicians in planning more informed, individual therapies. Moreover, the model’s predictions can assist in selecting adjuvant therapies, determining follow-up frequency, and deciding on additional lab tests. Incorporating this predictive model into clinic practices promotes data-driven decision-making, enhancing therapy outcomes and optimizing resource utilization. Ultimately, this integration helps standardize care across various healthcare providers and institutions, potentially decreasing diversity in treatment methods and therapy outcomes.

Besides, the key contribution of our study is the demonstration of how interpretable ML algorithms, particularly using SHAP values, can effectively identify critical factors influencing ICU mortality. The CatBoost algorithm, a gradient boosting framework based on symmetric decision trees (oblivious trees), excels in accuracy and efficiency, especially in handling categorical features, while requiring fewer parameters (31). Its performance often matches or surpasses that of other advanced ML algorithms, displaying outperforming discrimination, calibration, and clinical utility. However, due to its black-box nature, interpretation is essential for ML model. SHAP summary plots and force maps provide clinicians with clear, visual insights into the factors driving predictions, enhancing the model’s interpretability and highlighting key risk factors. Additionally, advanced ML techniques such as RFECV for feature selection, GridSearchCV for hyperparameter tuning, and SMOTE oversampling to address sample imbalance further improved the accuracy of ICU death prediction. This precise predictive model enables clinicians to develop personalized treatment strategies, ensuring timely interventions and improving the prognosis of lung cancer patients combined sepsis.

For critically sick patients, proactive and proactive treatment to address risk variables is essential (32). Nevertheless, some clinical variables are challenging to obtain in clinical practice, and many clinic variables show varying degrees of limitations in terms of accuracy, sensitivity, or specificity. Studies have indicated that SOFA scores often lack both sensitivity and specificity (33). Additionally, the clinical profiles and therapy outcomes of patients with both sepsis and lung carcinoma differ significantly from these patients with no cancer (34). Notably, the majority of critical illness scoring systems fail to consider cancer-specific factors (35). Specifically, we conducted univariate and multivariate logistic regression analyses to identify significant predictors of ICU mortality, including some cancer-related clinical factors. Leveraging these readily accessible clinic data, we successfully developed a robust CatBoost model for early ICU mortality prediction, thereby assisting clinicians in personalized therapy and decision-making. As observed in Table 2 , there are some notable differences in the baseline profiles of patients among the training, internal testing and external testing databases, likely attributable to variations in hospital admissions. Despite these differences, the model demonstrated brilliant performances in both internal and external validation datasets, highlighting its robust applicability.

In our research, we observed that the presence of distant metastasis is linked to poor prognosis, likely due to the immunocompromised state of these patients (36). Immunosuppression has been shown to correlate with adverse outcomes in septic patients (37). Greater focus is needed on managing patients with distant metastasis to improve their outcomes. Older patients are at a higher risk of developing sepsis compared to younger individuals, and they often exhibit reduced resilience when managing the condition (38). Previous research has explored the association between the anion gap and prognosis across various diseases. As a well-established marker for evaluating acid–base balance (39), an abnormal anion gap is linked to acid–base disturbances, which are considered to significantly affect outcomes in critically sick patients (40). Similarly, our findings indicate that serum anion gap is a significantly risk variable for ICU mortality in patients with both sepsis and lung carcinoma. Several scoring systems for critical illness, including SAPS II, OASIS and SOFA scores, have been established to assess disease intensity and forecast short-term outcomes. The SAPS II is a scoring system developed to assess the intensity of illness in patients admitted to ICU (35). It evaluates 17 physiological variables, including vital signs and laboratory results, to generate a score that predicts the probability of hospital mortality, which was robust in our research for septic lung cancer patients’ mortality prediction. The OASIS is a prognostic tool to appraise the severity of intensity in critically sick patients. It incorporates variables such as age, heart rate, mean arterial pressure, temperature, respiratory rate, urine output, Glasgow Coma Scale, and specific laboratory values to generate a score that predicts in-hospital mortality (41), which was the most powerful indicator in our analysis for lung cancer patients’ mortality prediction, calling its application especially in lung cancer patients. The SOFA score is a clinical metric used to evaluate and quantify the degree of organ dysfunction across six physiological systems: respiratory, cardiovascular, hepatic, coagulation, renal, and neurological. It is particularly valuable in ICUs for monitoring disease progression, especially in sepsis cases (42), which was also validated in our analysis with septic lung cancer patients.

This study, while showcasing notable strengths, also has several limitations. First, we determined the required sample size for our external validation cohort. However, due to the limited availability of patients with complete follow-up data, we were unable to assemble a sufficiently large external validation set. While we acknowledge that larger sample sizes enhance the reliability of model evaluation, we have endeavored to utilize the maximum possible sample size given the current research constraints. To maximize the validation reliability despite the smaller external validation set, we employed a 10-fold cross-validation method to assess the model’s generalizability. Moving forward, we intend to expand the sample size of the external validation cohort in future research to further substantiate the model’s universality and reliability. Second, the study relies on retrospective information in the MIMIC IV database, which introduces the potential for selection bias. Variations in data collection across hospitals and the retrospective design also resulted in some missing clinical features. Additionally, the absence of key clinicopathological parameters, such as smoking, socioeconomic factors, and gene mutations, was a limitation, as the MIMIC IV database does not include imaging data. While we included a broad range of baseline and routine clinical features to improve predictive accuracy, this added complexity to the model’s practical use in clinical settings. Lastly, the model remains to be integrated into clinic practices, necessitating additional prospective, multicenter, and large-scale validation studies to confirm its applicability and practical utility in future settings.

In our research, we successfully established a CatBoost-based prediction model using a ML benchmark framework to precisely forecast ICU mortality in lung carcinoma patients combined sepsis. We succeeded in identifying significantly predictive variables for ICU mortality in this patient population. This study establishes a groundwork for subsequent endeavors to refine ICU mortality predictions and prognostic forecasts, which may assist clinicians in making informed decisions and customizing therapeutic strategies.