Details

Autor(en) / Beteiligte

• Wang, Zhengfeng
• long shi, zhi
• zheng, jnxing
• rui, shaozhen
• zhang, hui
• li, xun
• zhou, wence

Titel

P-L02 Machine learning to evaluate liver reserve function based on venous blood biochemistry

Ist Teil von

• British journal of surgery, 2021-12, Vol.108 (Supplement_9)

Erscheinungsjahr

2021

Link zum Volltext

Beschreibungen/Notizen

• Abstract Background The accurate and comprehensive evaluation of liver reserve function is crucial for daily follow-up and medical treatment of patients with liver disease. However, existing techniques are too complex and costly for universal implementation.To develop a convenient, reliable method to evaluate liver reserve function based on eight biochemical indicators measured from venous blood.  Methods Blood test results (albumin (Alb), total bilirubin (TBIL), prothrombin time (PT), international normalized ratio (INR), total cholesterol (TC), cholinesterase (ChE), aspartate amino transferase (AST), and alanine transaminase (ALT)) were collected retrospectively from 660 patients treated at the first hospital of Ianzhou University from 2016 to 2018. As the reference standard for liver reserve function, indocyanine green (ICG) clearance test results were also collected from the same patients at the same times. The patient data were processed and analyzed to construct a machine learning model, eXtreme Gradient Boosting (XGBoost), and a generalized linear model (GLM) to predict liver reserve function based on the eight biochemical indicators. Results Results showed that the predicted XGBoost values were closely correlated with the actual ICG 15-minute retention rates (R = 0.969, R2 = 0.939), while the GLM values had a relatively low correlation (R = 0.566, R2 = 0.320). These findings indicate that the developed model can be used to evaluate liver reserve function with comparable performance to the ICG clearance test. Furthermore, the XGBoost model exhibited superior prediction compared with the GLM. Hence, the XGBoost model developed using machine learning can be utilized to evaluate liver reserve function from eight biochemical indicators that are closely related to liver function, commonly used clinically, and easier to obtain than ICG clearance measures. Conclusions The results predicted by the XGBoost model were highly accurate when compared with the results of the actual ICG test, demonstrating the strong practical clinical value of the model.