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

TL;DR

This study identifies key factors affecting survival in cancer patients with sepsis and builds a predictive model using machine learning.

Contribution

A novel random forest predictive model for in-hospital survival in cancer patients with sepsis, validated with clinical data.

Findings

Age, SOFA score, coagulation dysfunction, and metabolic abnormalities are significant risk factors for mortality.

The random forest model achieved an AUC of 0.95, sensitivity of 91%, and specificity of 85% in predicting survival.

Ten key clinical features were identified as most predictive using recursive feature elimination.

Abstract

To investigate the factors associated with in-hospital survival prognosis in participants with malignant tumors complicated by sepsis and to develop a predictive model.

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.

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.

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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