What question did this study set out to answer?

This research aims to identify survival predictors for patients with malignant tumors suffering from sepsis and create a predictive model based on these factors.

February 28, 2026Open Access

In-hospital survival characteristics and predictive model for patients with malignant tumors and sepsis

Puntos clave

This research aims to identify survival predictors for patients with malignant tumors suffering from sepsis and create a predictive model based on these factors.
Retrospective study involving 2,152 hospitalized participants.
Utilization of univariate and multivariable logistic regression for data analysis.
Implementation of the ADASYN oversampling technique to manage class imbalance.
Random dataset split into training and testing sets.
Evaluation and optimization of eight machine learning models including random forest.
The in-hospital mortality rate for participants was 12.6%.
Age, SOFA score, coagulation dysfunction, and metabolic abnormalities were identified as significant prognostic factors.
The random forest model achieved an AUC of 0.95, with sensitivity at 91% and specificity at 85%.
Ten key predictive features were selected using the recursive feature elimination method.

Resumen

Objectives 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 2,152 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 2,152 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%. A total of 10 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.

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