Association between Thymosin beta-4, acute kidney injury, and mortality in patients with sepsis: An observational cohort study

Machine-learning models for prediction of sepsis patients mortality

OBJECTIVES

Sepsis is an infection-caused syndrome, that leads to life-threatening organ damage. We aim to develop machine learning models with large-scale data to predict sepsis patients' mortality.

DESIGN

we extracted sepsis patients from two databases, Medical Information Mart for Intensive Care IV (MIMIC-IV) as a train set and Philips eICU Collaborative Research Database as a test set.

SETTING

ICUs in multicenter hospitals in the USA during 2012-2019.

PATIENTS OR PARTICIPANTS

A total of 21,680 sepsis-3 patients are included in the study, in which, 3771 patients were dead and 17,909 survived during hospitalization, respectively.

INTERVENTIONS

No interventions.

MAIN VARIABLES OF INTEREST

Basic information, examination items during hospitalization and some medication and treatment information are incorporated into analyzed. Seven different models were built with a Support vector machine, Decision Tree Classifier, Random Forest, Gradients Boosting, Multiple Layer Perception, Xgboost, light Gradients Boosting to predict dead or live during hospitalization.

RESULTS

Algorithms with an AUC value in the test set of the top three: light GBM, GBM, Xgboost. Considering the performance of the training set and the test set, the light GBM model performs best, and then the parameters of the model were adjusted, after that the AUC value was 0.99 in the train set, 0.96 in the test set, respectively.

CONCLUSIONS

Models built with light GBM algorithm from real-world sepsis patients from electronic health records accurately predict whether sepsis patients are dead and can be incorporated into clinical decision tools to enhance the prognosis of the patient and prevent adverse outcomes.

Bone Marrow Mesenchymal Stem Cells (BMSC) from Exosome with High miR-184 Level Ameliorates Sepsis

This study assesses whether BMSC from exosome with high miR-184 level ameliorates sepsis. BMSC with high miR-184 expression established. RAW264.7 cells were cultivated in vitro and divided into control set, model set, BMSC set and BMSC with high miR-184 level set. The model was established through infection of RAW264.7 cells with LPS followed by analysis of cell proliferation and apoptosis, activity of ROS and SOD, secretion of IL-1β, IL-6 and TNF-α as well as the expression of NF-κB and TRAIL. BMSC set showed significantly upregulated miR-184 expression, increased cell proliferation and SOD activity, reduced ROS activity, decreased secretion of IL-1β, IL-6 and TNF-α as well as the expression of NF-κB and TRAIL. The above changes were more significant in the set of BMSC with overexpression of miR-184. In conclusion, cell proliferation, apoptosis and inflammation in RAW264.7 cells induced with LPS is regulated by BMSC from exosome with high expression of miR-184, which is possibly through restraining the NF-κB and TRAIL and oxidative stress.