Early identification of sepsis-induced coagulopathy in critical ill patients: an analysis from MIMIC IV database

To develop a model for early identification of coagulopathy in septic patients. Patients with sepsis were identified from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who did not meet the sepsis-induced coagulopathy (SIC) scoring criteria upon admission but developed SIC within the subsequent 7 days were considered to be in a pre-SIC state at baseline. Baseline clinical features of the patients were screened by lasso regression. Subsequently, these features underwent multivariate logistic regression for model construction, followed by testing the stability of the model in the test set. A total of 7,806 patients were included in the study from the MIMIC-IV database, comprising 7,080 without SIC and 726 with pre-SIC. Patients with pre-SIC had higher criticality scores compared to patients with Non-SIC. Pre-SIC was identified as an independent risk factor for hospitalization, 28-day, 90-day, and 1-year mortality in patients with sepsis. Patients with pre-SIC who received early heparin had lower 28-day mortality compared to those without treatment. The SIC scoring system demonstrated a sensitivity of 77.0% for identifying pre-SIC, a specificity of 53.9%, and an AUC of 0.694 (95% CI: 0.659–0.730). Based on SIC scoring system, additional clinical features were added to the pre-SIC model, ultimately yielding 70% sensitivity and 76.2% specificity with an AUC of 0.802 (95% CI: 0.773–0.830) in the validation set. The development of SIC is associated with increased mortality rate in patients with sepsis, and precise identification of this group of patients and individualized treatment may be important for improving prognosis.

Authors

Institutions

Publication Details

Journal
BMC Infectious Diseases
Published
2025-08-26
DOI
https://doi.org/10.1186/s12879-025-11482-5
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Early identification of sepsis-induced coagulopathy in critical ill patients: an analysis from MIMIC IV database

Shujian Wei, Yuguo Chen, N. Cao, Jinlei Wu et al.
BMC Infectious Diseases
Sepsis Diagnosis and Treatment
article

Early identification of sepsis-induced coagulopathy in critical ill patients: an analysis from MIMIC IV database

Shujian Wei, Yuguo Chen, N. Cao, Jinlei Wu, Xiaofei Yin, Shuo Wu, Yiming Dong, Bailu Wang, Xuyang Ji, Xilong Liu
article en

Abstract

To develop a model for early identification of coagulopathy in septic patients. Patients with sepsis were identified from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who did not meet the sepsis-induced coagulopathy (SIC) scoring criteria upon admission but developed SIC within the subsequent 7 days were considered to be in a pre-SIC state at baseline. Baseline clinical features of the patients were screened by lasso regression. Subsequently, these features underwent multivariate logistic regression for model construction, followed by testing the stability of the model in the test set. A total of 7,806 patients were included in the study from the MIMIC-IV database, comprising 7,080 without SIC and 726 with pre-SIC. Patients with pre-SIC had higher criticality scores compared to patients with Non-SIC. Pre-SIC was identified as an independent risk factor for hospitalization, 28-day, 90-day, and 1-year mortality in patients with sepsis. Patients with pre-SIC who received early heparin had lower 28-day mortality compared to those without treatment. The SIC scoring system demonstrated a sensitivity of 77.0% for identifying pre-SIC, a specificity of 53.9%, and an AUC of 0.694 (95% CI: 0.659–0.730). Based on SIC scoring system, additional clinical features were added to the pre-SIC model, ultimately yielding 70% sensitivity and 76.2% specificity with an AUC of 0.802 (95% CI: 0.773–0.830) in the validation set. The development of SIC is associated with increased mortality rate in patients with sepsis, and precise identification of this group of patients and individualized treatment may be important for improving prognosis.

BMC Infectious DiseasesVol. 25(1)
Qilu Hospital of Shandong University (CN)
Shandong University, National Natural Science Foundation of China
Openalex Percentile: Top 69%
Sepsis Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.