ORIGINAL RESEARCH article
Front. Med.
Sec. Translational Medicine
In-Hospital Survival Characteristics and Predictive Model for Patients with Malignant Tumors and Sepsis
Yonglin Li
Ziyan Gan
Jiahao Zhang
Jinpeng Huang
Shunqin Long
Wanyin Wu
Guo Wang
Xiaobing Yao
Qiang Li
Xiaobing Yang
Second Clinical Medical College, Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
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Abstract
Objective: To investigate the factors associated with in-hospital survival prognosis in participants with malignant tumors complicated by sepsis and to develop a predictive model. Methods: A retrospective study was conducted to collect data from 2152 participants with malignant tumors complicated by sepsis, hospitalized at Guangdong Provincial Hospital of Chinese Medicine between January 2014 and June 2024. Univariate and multivariable logistic regression analyses were performed to identify independent risk factors, and the ADASYN oversampling technique was applied to address class imbalance. The dataset was randomly split into training and testing sets at an 8:2 ratio. Key features were selected using the recursive feature elimination (RFE) method, and eight machine learning models (logistic regression, decision tree, random forest, K-nearest neighbors, support vector machine, naive Bayes, stochastic gradient boosting, and neural network) were evaluated and hyperparameter-optimized. Results: A total of 2152 participants were included in the study, with an in-hospital mortality rate of 12.6%. Multivariable analysis indicated that age, SOFA score, coagulation dysfunction, and metabolic abnormalities were important prognostic risk factors. The random forest model showed excellent discriminative ability on the validation set, with an AUC of 0.95, sensitivity of 91%, and specificity of 85%. Ten features with the highest predictive value were selected using the RFE method, including troponin T, platelet distribution width, neutrophil count, red blood cell distribution width, fibrinogen, prothrombin time activity, aspartate transaminase, urea, low-density lipoprotein cholesterol, and creatinine. Conclusion: Age, SOFA score, coagulation dysfunction, and metabolic abnormalities are important prognostic risk factors for participants with malignant tumors complicated by sepsis. The random forest model constructed based on these key features has good predictive performance and can provide a powerful tool for the prognosis assessment of participants with malignant tumors complicated by sepsis. Future research needs to further validate the applicability and practical value of the model in different populations.
Summary
Keywords
Machine l earning, Malignant tumors, prognostic factors, random forest, Sepsis
Received
21 November 2025
Accepted
06 February 2026
Copyright
© 2026 Li, Gan, Zhang, Huang, Long, Wu, Wang, Yao, Li and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yonglin Li; Xiaobing Yang
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