Predictive value of red blood cell distribution width in septic shock patients with thrombocytopenia: A retrospective study using machine learning

Abstract

Background: Sepsis-associated thrombocytopenia (SAT) is common in critical patients and results in the elevation of mortality. Red cell distribution width (RDW) can reflect body response to inflammation and oxidative stress. We try to investigate the relationship between the RDW and the prognosis of patients with SAT through machine learning.

Methods: 809 patients were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to analyze the impact of each feature. Logistic regression analysis, propensity score matching (PSM), receiver-operating characteristics (ROC) curve analysis, and the Kaplan-Meier method were used for data processing.

Results: The patients with thrombocytopenia had higher 28-day mortality (48.2%). Machine learning indicated that RDW was the second most important in predicting 28-day mortality. The RDW was significantly increased in non-survivors by logistic regression and PSM. ROC curve shows that RDW has moderate predictive power for 28-day mortality. The patients with RDW>16.05 exhibited higher mortality through Kaplan-Meier analysis.

Conclusions: Interpretable machine learning can be applied in clinical research. Elevated RDW is not only common in patients with SAT but is also associated with a poor prognosis.

Keywords: inflammation; machine learning; red cell distribution width; septic shock; thrombocytopenia.