Evaluate prognostic accuracy of SOFA component score for mortality among adults with sepsis by machine learning method

Abstract

Introduction Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance. Methods Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models. Result A total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including renal system score, central nervous system score and cardio vascular system score were used to establish prediction models. The accuracy of the LR, GNB, SVM were 0.851, 0.844 and 0.862, respectively, which were better than linear regression analysis (0.123) and LASSO (0.130). The AUROCs of LR, GNB and SVM were 0.76, 0.76 and 0.67, respectively. K-fold cross validation showed that the average AUROCs of LR, GNB and SVM were 0.757 ± 0.005, 0.762 ± 0.006, 0.630 ± 0.013, respectively. For the probability threshold of 5–50%, LY and GNB models both showed positive net benefits. Conclusion The two machine learning-based models (LR and GNB models) based on SOFA components can be used to predict in-hospital mortality of septic patients admitted to ICU.

Zero Shot Health Trajectory Prediction Using Transformer

“…On the lower end, Chen et al (2023) 18 reported an AUC of 0.642 ± 0.101. Within a specific subgroup of the test set of 3,324 patients with sepsis with 10.8% mortality prevalence, ETHOS’s prediction of ICU mortality exhibited an AUC of 0.889 (95% CI: 0.870-0.906), which is a better performance than obtained in a study by Pan et al (2023) 19 , which estimated ICU mortality in adult sepsis patients using SOFA and additional features, achieving an AUC of 0.762 ± 0.006. We also estimated performance for a task of ICU mortality estimation for patients remaining in ICU for at least 24 hours in which we obtained an AUC of 0.928 (95% CI: 0.916-0.939).…”

“…We conducted an analysis focusing on risk estimation for inpatient and ICU mortality, calculated at the respective points of patient admission to the hospital and ICU. 19 , which estimated ICU mortality in adult sepsis patients using SOFA and additional features, achieving an AUC of 0.762 ± 0.006. We also estimated performance for a task of ICU mortality estimation for patients remaining in ICU for at least 24 hours in which we obtained an AUC of 0.928 (95% CI: 0.916-0.939).…”

Assessing SOFA score trajectories in sepsis using machine learning: A pragmatic approach to improve the accuracy of mortality prediction

“…Despite the digital age of medicine, the 25-year-old SOFA is still a key element in the diagnosis and assessment of the severity of sepsis worldwide, without the need for technological support [ 26 ]. Consequently, recent research groups also aimed to adopt the development of ML-based algorithms into a more pragmatic approach and limited the input variables, for example, to the six components of the SOFA score on day 1 [ 27 ]. By analyzing these variables using artificial intelligence, their results yielded a promising prediction of 30-day mortality, and these results were still understandable for most ICU physicians, thus, supporting their clinical acceptance.…”

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study

“…Currently, sequential organ failure assessment (SOFA) component scores are used in clinical risk stratification of patients with sepsis; however, they have limitations of poor specificity and sensitivity. 7 8 9 10 11 The SOFA score is one of the main criteria for sepsis-3 definitions. Several previous studies have suggested predictive results for patients with sepsis using the SOFA score; however, they have achieved varying performances in predicting short-term mortality.…”

“…It was calculated by simply adding the scores of the six organ systems and did not consider the interaction with various organ systems. 11 Thus, it may have different effects on the mortality risk of patients with sepsis and reduce the predictive power of the model.…”