Yang Jie, Zhang Xu, Chen Jia, Hou Xianghong, Shi Minghong, Yin Longlong, Hua Longchun, Wang Cheng, Han Xiaolong, Zhao Shuyan, Kang Guolan, Mai Ping, Jiang Rui, Tian Hongwei
Endoscopic Diagnosis and Treatment Center, Gansu Provincial Hospital, Lanzhou, Gansu, China.
Department of Gastroenterology, Third People's Hospital of Yuzhong County, Lanzhou, Gansu, China.
Ann Med. 2025 Dec;57(1):2457521. doi: 10.1080/07853890.2025.2457521. Epub 2025 Jan 29.
Liver cirrhosis complicated by portal vein thrombosis (PVT) is a fatal complication with no specific manifestations but often misdiagnosed, it crucially increases the mortality worldwide. This study aimed to identify risk factors and establish a predictive model for diagnosis of venous thrombosis clinical by routine blood tests and endoscopic characteristics.
Patients from Gansu Provincial Hospital from October 2019 to December 2023 were enrolled. The retrospective modelling cohort was screened by propensity score matching (PSM) at a 1:1 ratio from the baseline characteristics before endoscopic diagnosis. Variables were collected from blood test and endoscopic signs using machine learning method (ML). Logistic regression determined risk factors. The predictive performance was evaluated by receiver operation curve (ROC), calibration curve, clinical decision analysis (DCA) and influence curve (CIC). Furthermore, external cohort was used for validation, an online nomogram was established.
A total of 1,058 patients were enrolled, and 470 patients were included after PSM 1: 1. The model identified 7 factors, including splenectomy, blood urea nitrogen (BUN), serum sodium, activated partial thromboplastin time (APTT), thrombin time (TT), D-dimer, and degree of oesophageal varices. The area under the curve (AUC) was 0.907 (95% CI, 0.877-0.931). The calibration curve, decision and clinical impact curves showed the model demonstrated a good predictive accuracy and clinical benefits. The validation got an AUC of 0.890 (95% CI, 0.831-0.934), A nomogram tool was finally established for application.
Blood test combined endoscopic characters could preliminarily predict the liver cirrhosis with portal vein thrombosis for cirrhotic patients undergoing endoscopic examination.
肝硬化合并门静脉血栓形成（PVT）是一种致命的并发症，无特异性表现，常被误诊，在全球范围内显著增加死亡率。本研究旨在通过常规血液检查和内镜特征识别危险因素并建立静脉血栓形成临床诊断的预测模型。
纳入2019年10月至2023年12月甘肃省医院的患者。回顾性建模队列通过倾向评分匹配（PSM）以1:1的比例从内镜诊断前的基线特征中筛选。使用机器学习方法（ML）从血液检查和内镜征象中收集变量。逻辑回归确定危险因素。通过受试者操作曲线（ROC）、校准曲线、临床决策分析（DCA）和影响曲线（CIC）评估预测性能。此外，使用外部队列进行验证，建立了在线列线图。
共纳入1058例患者，1:1 PSM后纳入470例患者。该模型识别出7个因素，包括脾切除术、血尿素氮（BUN）、血清钠、活化部分凝血活酶时间（APTT）、凝血酶时间（TT）、D-二聚体和食管静脉曲张程度。曲线下面积（AUC）为0.907（95%CI，0.877-0.931）。校准曲线、决策曲线和临床影响曲线显示该模型具有良好的预测准确性和临床效益。验证得到的AUC为0.890（95%CI，0.831-0.934），最终建立了列线图工具以供应用。
血液检查结合内镜特征可为接受内镜检查的肝硬化患者初步预测肝硬化合并门静脉血栓形成。
