Discussion
sepsis, death, Sepsis, infection
RENAL, INFECTION, SEPSIS, SEPSIS
Our results suggest that machine learning based on SOFA components can be used to predict the risk of death in sepsis patients and has predictive value. Five machine learning models were built in our study, and the accuracy of LR, GNB and SVM was significantly better than the other two models. LR and GNB models had similar performance and were better than SVM. We further conducted k-fold cross validation and showed that LR and GNB models both had similar AUROC in each cross validation, indicating good generalization ability. In the model, SOFA components were selected as the predictive factors of each organ system function, which could be obtained quickly and easily in the hospital. Renal system score, central nervous system score and circulatory system score were the three feature variables of machine learning models in our study. To our knowledge, this was the first time that SOFA score was subdivided into 6 organ scores and machine learning was used to establish a prediction model for the assessment of mortality risk of sepsis patients, providing a more simple and effective prognostic tool for sepsis patients.Sepsis was characterized by high morbidity and mortality [The three feature variables in our study were renal system score, central nervous system score, and cardio vascular system in SOFA score. Previous studies have shown that the top three organ systems with the greatest impact on prognosis in SOFA score were the cerebral nervous system, renal system and circulatory system, which was consistent with this study [The merits of this study are as follows: Firstly, the data in this study came from MIMIC IV public database, and machine learning method was adopted to hand missing values and establish mortality risk prediction model, which was more consistent with objective facts. Secondly, we further subdivided SOFA score into six organ system scores, and selected feature variables by machine learning to further build the prediction model, fully considering the different weight of each organ system in SOFA score, and improved the prediction ability of SOFA score based prediction model.However, there are limitations in this study: firstly, the validation of models were realized through internal verification of the test set, which needs to be verified by external validation in future studies; Secondly, the variables adopted in our prediction model are only the content of SOFA score. Since the purpose of our study is to better use SOFA score to predict the risk of death in patients with sepsis, our study did not consider other risk factors for death and we didn’t do stratified analysis of source of infection, severity of the subject and comorbidities, etc. This makes the model does not achieve the ideal predictive ability. Next, we will further combine other clinical indicators and biological markers to build more accurate models based on this study. In addition, this paper observes the outcome without reflecting the effect of time on the outcome. What’s more, we excluded patients discharged from hospital within 24 h in order to obtain more complete case data and scores of patients, which had a higher mortality rate (18.7%), which may have skewed the results. Finally, the data in this paper came from MIMIC IV public database, which was a retrospective cohort study and were collected from relatively long periods (2008–2019), which might be risk of bias due to change of standard of care/protocol of sepsis. Patients suspected of infection could only be extracted from the database system diagnosis, which could not fully reflect the real situation.