Discussion
sepsis, organ failure, MIMIC-III, intracranial aneurysms
RECURRENCE, REGRESSION, SEPSIS, DISEASES, INTRACRANIAL ANEURYSMS
In this retrospective study, we developed and validated machine learning-based models for predicting sepsis in AP patients. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The results of this study showed that the GBDT model had an excellent performance in the prediction of sepsis in patients with AP, with the AUC in the testing set at 0.985. Furthermore, the GBDT model achieved better predictive performance for sepsis prediction in AP patients compared with the LR model, and scoring systems.Advanced machine learning methods are good at dealing with high-order interactions and fitting complex nonlinear relationships, and can be used to integrate large amounts of data from electronic health records (EHRs). The application of machine learning to data-driven analysis shows promise for improving predictive performance in healthcare [The results of this study showed that the GBDT model had an excellent performance in predicting sepsis in AP patients. The GBDT model has been applied to diagnose and predict the outcomes of several diseases. A study that developed and assessed machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms found that the GBDT model showed an optimal prediction performance for predicting recurrence risk in patients with intracranial aneurysms after endovascular treatment in 6 months [GBDT is an ensemble algorithm widely used for regression and classification tasks. The GBDT algorithm creates multiple weak learners or individual trees by bootstrapping training samples and integrates their outputs to make predictions. The GBDT algorithm is less sensitive to hyperparameters, less prone to overfitting, and easy to implement. For the practical applicability of the GBDT model in a clinical setting, an example of how SHAP can be used locally to explain individual prediction was provided (Supplementary Fig. This study suggested that the basic characteristics of patients (age, temperature, and insurance) and vital signs (heart rate, respiratory rate, and SpO2 were associated with the risk of sepsis in AP. A study by Hong et al. indicated that age may be useful for predicting the development of persistent organ failure in patients with AP [Our study has several strengths. To the best of our knowledge, we first report the application of machine learning models to predict the risk of sepsis in AP patients using the MIMIC database. The optimal model was screened using a variety of machine learning methods and showed significantly better predictive value than LR and scoring systems, providing a basis for the accurate prediction of sepsis risk in AP patients. The sample size in this study is very sufficient for the construction and validation of prediction models. A larger sample size is valuable for developing a more robust prediction model, which has good generalization ability and good statistical efficacy for a wider population. However, the study was still subject to some limitations. First, the retrospective nature of the study may have introduced unavoidable selection bias, which limits the interpretation of the results. Second, the MIMIC data were obtained from a single center in the United States, which may affect the generalizability of the prediction model to other populations. The results may not be representative of the entire population of AP patients, although we attempted to provide detailed information in our study. Third, the study included AP patients in MIMIC-III and IV, which included hospitalized patients from 2001 to 2019. The population studied here is not consecutive and therefore different biases may have been introduced. As treatment regimens are developed and optimized, consistency of treatment regimens cannot be guaranteed, which may introduce some bias into the results. Fourth, radiological results in AP, specific chemoradiotherapy information, and medication dosage in vasopressors and mechanical ventilation may have an impact on our results, but the lack of radiological data in the database prevented us from performing further analyses. Fifth, the study lacked external validation. External validation is crucial to assess the generalizability and reliability of the model, especially when using data from a single center. Therefore, it would be important to perform further validation on an independent dataset in future studies to examine the robustness and generalization ability of the proposed model, which might greatly increase the impact of the current finding. Future research will need to explore other machine learning algorithms for predicting sepsis in AP patients.