From the Information Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
From the Department of Medical Informatics, West China Medical School, Chengdu, Sichuan Province, China.
Exp Clin Transplant. 2022 Dec;20(12):1099-1104. doi: 10.6002/ect.2021.0431.
The purpose of this study was to assess and predict risk factors for death within 30 days after orthotopic liver transplant and to develop a nomogram to predict mortality after liver transplant.
We retrospectively studied 185 patients who underwent orthotopic livertransplant at Sichuan Provincial People's Hospital from January 1, 2010, to December 31, 2018. Multivariable logistic regression analyses were used to identify independent risk factors. A nomogram model was developed to predict mortality after liver transplant. The performance of the prediction model was assessed and validated by receiver operating characteristic curve and bootstrap methods (1000 replications).
Multivariable logistic regression analyses revealed that tracheal extubation time, postoperative infection, and intraperitoneal hemorrhage posttransplant were independentrisk factors for mortality after liver transplant. The receiver operating characteristic curve of the nomogram prediction model was 0.896 (96% CI, 0.803-0.989), and the mean absolute error of internal validation by bootstrap (1000 replications) was 0.019 (n = 184). These results showed that the nomogram model had an excellent prediction accuracy.
A nomogram model can provide clinicians with an individualized risk assessment of perioperative mortality in liver transplant recipients.
本研究旨在评估和预测原位肝移植术后 30 天内死亡的风险因素,并制定预测肝移植后死亡率的列线图。
我们回顾性研究了 2010 年 1 月 1 日至 2018 年 12 月 31 日在四川省人民医院接受原位肝移植的 185 例患者。采用多变量逻辑回归分析确定独立的风险因素。建立列线图模型预测肝移植后的死亡率。通过接受者操作特征曲线和自举法(1000 次重复)评估和验证预测模型的性能。
多变量逻辑回归分析显示,气管拔管时间、术后感染和移植后腹腔内出血是肝移植后死亡的独立风险因素。列线图预测模型的接受者操作特征曲线为 0.896(96%置信区间,0.803-0.989),bootstrap 法(1000 次重复)内部验证的平均绝对误差为 0.019(n=184)。这些结果表明,该列线图模型具有良好的预测准确性。
列线图模型可为临床医生提供肝移植受者围手术期死亡率的个体化风险评估。