Dickson E R, Grambsch P M, Fleming T R, Fisher L D, Langworthy A
Division of Gastroenterology and Internal Medicine, Mayo Clinic, Rochester, Minnesota 55905.
Hepatology. 1989 Jul;10(1):1-7. doi: 10.1002/hep.1840100102.
The ideal mathematical model for predicting survival for individual patients with primary biliary cirrhosis should be based on a small number of inexpensive, noninvasive measurements that are universally available. Such a model would be useful in medical management by aiding in the selection of patients for and timing of orthotopic liver transplantation. This paper describes the development, testing and use of a mathematical model for predicting survival. The Cox regression method and comprehensive data from 312 Mayo Clinic patients with primary biliary cirrhosis were used to derive a model based on patient's age, total serum bilirubin and serum albumin concentrations, prothrombin time and severity of edema. When cross-validated on an independent set of 106 Mayo Clinic primary biliary cirrhosis patients, the model predicted survival accurately. Our model was found to be comparable in quality to two other primary biliary cirrhosis survival models reported in the literature and to have the advantage of not requiring liver biopsy.
预测原发性胆汁性肝硬化个体患者生存率的理想数学模型应基于少量普遍可用的廉价、非侵入性测量指标。这样的模型在医疗管理中有助于选择原位肝移植的患者并确定移植时机,具有重要作用。本文描述了一个预测生存率的数学模型的开发、测试及应用。采用Cox回归方法以及梅奥诊所312例原发性胆汁性肝硬化患者的综合数据,构建了一个基于患者年龄、血清总胆红素和血清白蛋白浓度、凝血酶原时间以及水肿严重程度的模型。在另一组106例梅奥诊所原发性胆汁性肝硬化患者中进行交叉验证时,该模型准确地预测了生存率。我们发现,该模型的质量与文献中报道的其他两个原发性胆汁性肝硬化生存模型相当,且具有无需肝活检的优势。