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Updated: Jun 4, 2026
Assessment of Plasma Coagulation on Liver Tissue in a Large Animal Model In Vivo
Published on: August 4, 2018
Development of a new disseminated intravascular coagulation score tailored for patients with chronic liver disease: a
Ecaterina Scarlatescu1, Ton Lisman2
1Department of Anaesthesia and Intensive Care Medicine, University of Medicine and Pharmacy "Carol Davila", Bucharest, Romania; Department of Anaesthesia and Intensive Care Medicine, Fundeni Clinical Institute, Bucharest, Romania.
Insights
Diagnosing disseminated intravascular coagulation (DIC) in cirrhosis patients is difficult due to similar lab results. A new prediction tool accurately identifies DIC risk using common parameters, improving diagnosis and patient outcomes.
Area of Science:
- Hematology
- Critical Care Medicine
- Medical Informatics
Background:
- Diagnosing disseminated intravascular coagulation (DIC) in chronic liver disease patients is challenging.
- Cirrhosis-related hemostatic abnormalities mimic DIC laboratory features, leading to overdiagnosis with conventional scoring systems.
Purpose of the Study:
- Evaluate existing DIC scores in cirrhotic patients.
- Develop a tailored diagnostic prediction tool for DIC in cirrhosis.
Main Methods:
- Prospective enrollment of 80 cirrhosis patients in an ICU.
- Assessed DIC manifestations, conventional scores, hemostatic parameters, and advanced biomarkers.
- Developed a prediction model using machine learning (Random Forest, Logistic Regression, XGBoost, SMOTE) with CLIF-SOFA, platelets, fibrinogen, and D-dimer.
Main Results:
- Conventional DIC scores showed overdiagnosis (>50% positive vs. 1.3% clinical DIC).
- New model achieved 100% sensitivity and 87% specificity in discriminating clinical DIC.
- Progressive fibrin monomer depletion distinguished overt DIC from compensated hemostatic imbalance.
Conclusions:
- The developed prediction tool accurately identifies DIC risk in cirrhosis patients using readily available parameters.
- Implementation in a web-based application may aid clinical use after validation.
Background:
Diagnosing disseminated intravascular coagulation (DIC) in patients with chronic liver disease is challenging because cirrhosis-related hemostatic abnormalities mimic DIC laboratory features, leading to frequent overdiagnosis using conventional DIC scoring systems.
Objectives:
This study aimed to evaluate the performance of existing DIC scores in patients with cirrhosis and develop a tailored diagnostic prediction tool.
Methods:
We prospectively enrolled 80 consecutive patients with cirrhosis admitted to a single intensive care unit. Clinical manifestations of DIC, conventional DIC scores, routine hemostatic parameters, and advanced coagulation biomarkers were assessed. Machine learning techniques combining random forest, logistic regression, and extreme gradient boosting with the Synthetic Minority Oversampling Technique were used to develop a DIC prediction model using 4 routinely available parameters: chronic liver failure-sequential organ failure assessment score, platelet count, fibrinogen, and D-dimer levels.
Results:
Conventional DIC scores (the International Society on Thrombosis and Haemostasis, the Japanese Association for Acute Medicine, and the Japanese Ministry of Health, Labour, and Welfare) were positive in >50% of patients at study inclusion despite only 1.3% having clinical DIC manifestations, demonstrating substantial overdiagnosis. During follow-up, 10 patients (12.5%) developed clinical DIC with 90% mortality, while positive DIC scores did not predict outcomes. The newly developed ensemble prediction model achieved 100% sensitivity and 87% specificity, accurately discriminating clinical DIC from cirrhosis-related hemostatic abnormalities. Coagulation activation marker levels were elevated universally, while progressive fibrin monomer level depletion distinguished overt DIC from compensated hemostatic imbalance.
Conclusion:
The proposed prediction tool showed excellent internal performance in identifying DIC risk in patients with cirrhosis using readily available parameters, and its implementation in a freely accessible web-based application may facilitate future clinical use after external validation.
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