Evaluation of D-dimer and prothrombin time in alcohol related liver cirrhosis with comparison of machine learning analyses

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 186; p. 105407
• Main Authors: Lee, Hyeongyu, Yoo, Gilsung, Pak, Daewoo, Lee, Jong-Han
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2024
• Subjects: D-dimer; End Stage Liver Disease; Fibrin Fibrinogen Degradation Products; Humans; Liver cirrhosis; Liver Cirrhosis - diagnosis; Machine Learning; Prothrombin Time; Prothrombin time (PT); Quality of Life; Severity of Illness Index; D-dimer; Liver cirrhosis; Prothrombin time (PT); Machine learning
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2024.105407

Liver cirrhosis (LC) can be caused by obesity, alcohol consumption, viral infection, and autoimmune disease. Early diagnosis and management of LC is important for patient quality of life. Non-invasive diagnostic methods are useful for predicting the current status and mortality risk of LC. The purpose of this study is to identify relevant diagnostic factors measured in routine laboratory test of alcohol-related liver cirrhosis (ALC) patients. This study analyzed data from 127 patients with ALC, including their laboratory test results and clinical information, including coagulation parameters, hematologic parameters, and biochemical parameters. These data were used to compare the performance of the prediction models from three machine learning algorithms including K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). Higher Model for End-stage Liver Disease (MELD) score were associated with prothrombin time (PT) and D-dimer. Logistic and multiple linear regression analyses revealed significant factors predicting mortality in the MELD group. Machine learning approaches were used to predict death in ALC patients using some laboratory parameters associated with mortality. The prediction model based on SVM exhibited better prediction performance than others. PT and D-dimer were the factors that were most strongly associated with 90-day mortality, and machine learning methods can create prediction models with good predictive power.