Construction of a multi-dimensional predictive model for sepsis-associated disseminated intravascular coagulation and its clinical utility

Background: This study aimed to analyze the independent risk factors for sepsis patients complicated by disseminated intravascular coagulation (DIC) using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and to construct and validate a dynamic predictive model, thereby providing a basis for early clinical intervention. Methods: A total of 3586 Intensive Care Unit (ICU) patients meeting the Sepsis-3 criteria from the MIMIC-IV database between 2008 and 2019 were included. Patients were categorized into a DIC group (1311 cases) and a non-DIC group (2275 cases) based on the International Society on Thrombosis and Haemostasis (ISTH) overt DIC scoring criteria. Predictive variables were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and a multivariate logistic regression model was constructed. The model’s performance was evaluated using the receiver operating characteristic (ROC) curve. Results: The independent risk factors for sepsis complicated by DIC included the Sequential Organ Failure Assessment (SOFA) score, international normalized ratio (INR), total bilirubin, red blood cell distribution width (RDW), red blood cell count (RBC), absolute neutrophil count (Neutrophils Abs), age, chronic kidney disease (renal disease), mean corpuscular hemoglobin concentration (MCHC), and absolute monocyte count (Monocytes Abs). The predictive model achieved an area under the curve (AUC) of 0.781, with a sensitivity of 68.9% and a specificity of 75.3%, outperforming single indicators (e.g., INR with an AUC of 0.761). Conclusions: The predictive model constructed in this study integrates multidimensional indicators encompassing inflammation, coagulation, and red blood cell parameters, demonstrating good clinical utility. It can assist in the early identification of high-risk critically ill patients and optimize personalized intervention strategies. This model is specifically applicable to critically ill patients admitted to the ICU.

Sepsis; Disseminated intravascular coagulation; Risk factors; Predictive model; MIMIC-IV database; Logistic regression

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