Hosseini-Asl Seyed Mohammad Kazem, Masoumi Seyed Jalil, Rashidizadeh Ghazaleh, Hassani Amir Hossein, Mehrabani Golnoush, Ebrahimi Vahid, Malek-Hosseini Seyed Ali, Nikeghbalian Saman, Shakibafard Alireza
Department of Internal Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz 7193613311, Iran.
Nutrition Research Center, Department of Clinical Nutrition, School of Nutrition and Food Sciences, Shiraz University of Medical Science, Shiraz 7134814336, Iran.
J Clin Med. 2025 Jun 27;14(13):4559. doi: 10.3390/jcm14134559.
Cirrhosis is responsible for a large proportion of mortality worldwide. Despite having multiple scoring systems, organ allocation for end-stage liver disease remains a major problem. Since anthropometric indices play important roles in predicting the prognosis of patients with cirrhosis, these variables were used in establishment of a novel scoring system. In order to evaluate a machine learning approach for predicting the probability of three-month mortality in cirrhotic patients awaiting liver transplantation, the clinical and anthropometric information of 64 patients referred to Abu-Ali-Sina Transplantation Center were collected and followed for three months. A LASSO logistic regression model was used to devise and validate a new machine learning approach and compare it to the Model for End-Stage Liver Disease (MELD) regarding the three-month mortality of cirrhotic patients. Hand grip, skeletal muscle mass index, average mean arterial pressure, serum sodium, and total bilirubin were assessed with this new machine learning approach to predict the prognosis of patients with cirrhosis, which we named the Sina score. Sixty-four patients were enrolled, with a mean age of 46.50 ± 12.871 years. Like the MELD score, the Sina score is a precise prognostic tool for predicting the three-month mortality probability in cirrhotic patients [area under the curve (AUC) = 0.753 and = 0.005 vs. AUC = 0.607 and = 0.238]. Our machine learning approach, the Sina score, was shown to be a precise prognostic tool, like the MELD, for the prediction of the three-month mortality probability of cirrhotic patients awaiting liver transplantation. The Sina score, given that its level of precision is on par with the MELD, can be recommended for the prediction of three-month mortality in cirrhotic patients awaiting liver transplantation.
肝硬化在全球范围内导致了很大一部分死亡率。尽管有多种评分系统，但终末期肝病的器官分配仍然是一个主要问题。由于人体测量指标在预测肝硬化患者的预后中起着重要作用，这些变量被用于建立一种新的评分系统。为了评估一种机器学习方法来预测等待肝移植的肝硬化患者三个月死亡率的概率，收集了转诊至阿布-阿里-西纳移植中心的64例患者的临床和人体测量信息，并对其进行了三个月的随访。使用套索逻辑回归模型设计并验证一种新的机器学习方法，并将其与终末期肝病模型（MELD）在肝硬化患者三个月死亡率方面进行比较。采用这种新的机器学习方法评估握力、骨骼肌质量指数、平均平均动脉压、血清钠和总胆红素，以预测肝硬化患者的预后，我们将其命名为西纳评分。共纳入64例患者，平均年龄为46.50±12.871岁。与MELD评分一样，西纳评分是预测肝硬化患者三个月死亡概率的精确预后工具[曲线下面积（AUC）=0.753，P = 0.005，而AUC = 0.607，P = 0.238]。我们的机器学习方法，即西纳评分，被证明是一种精确的预后工具，与MELD一样，可用于预测等待肝移植的肝硬化患者的三个月死亡概率。鉴于西纳评分的精确程度与MELD相当，可推荐其用于预测等待肝移植的肝硬化患者的三个月死亡率。
