PDF(Pubmed)
Abstract：
UNASSIGNED: Acute liver failure (ALF) is a severe complication of spontaneous ruptured hepatocellular carcinoma (SRHCC) that requires accurate prediction for effective treatment strategies. We aimed to develop a predictive nomogram to estimate the risk of ALF in patients with SRHCC undergoing treatment.
UNASSIGNED: We performed a retrospective analysis of historical data from 284 patients diagnosed with SRHCC at the First Hospital of Jilin University over the past decade. Variables were selected through univariate and multivariate logistic regression analyses, and a predictive nomogram was constructed. We evaluated its predictive accuracy against the Child-Pugh Score, R.MELD, and ALBI by assessing discrimination, calibration, and net clinical benefit.
UNASSIGNED: Among the 284 patients, 65 developed ALF. The risk factors identified for model development included largest tumor size (LTS), platelet counts, prolonged prothrombin time, and elevated serum α-fetoprotein levels. The nomogram exhibited high accuracy in predicting ALF risk with a C-index of 0.91 (0.87-0.95). The Delong test showed a significant difference between the nomogram and the other three models (p<0.05). The calibration curve for the nomogram fit well, and the decision curve analysis revealed superior net benefit. The optimal cut-off point for the nomogram was determined to be 40, yielding sensitivity, specificity, positive predictive value, and negative predictive value of 83.10%, 87.20%, 65.90% and 94.60%, respectively.
UNASSIGNED: The nomogram we developed provides an optimized tool for predicting ALF in SRHCC patients. Its application can help determine individual patient\'s risk of ALF, enabling more rational and personalized treatment strategies.
UNASSIGNED: We performed a retrospective analysis of historical data from 284 patients diagnosed with SRHCC at the First Hospital of Jilin University over the past decade. Variables were selected through univariate and multivariate logistic regression analyses, and a predictive nomogram was constructed. We evaluated its predictive accuracy against the Child-Pugh Score, R.MELD, and ALBI by assessing discrimination, calibration, and net clinical benefit.
UNASSIGNED: Among the 284 patients, 65 developed ALF. The risk factors identified for model development included largest tumor size (LTS), platelet counts, prolonged prothrombin time, and elevated serum α-fetoprotein levels. The nomogram exhibited high accuracy in predicting ALF risk with a C-index of 0.91 (0.87-0.95). The Delong test showed a significant difference between the nomogram and the other three models (p<0.05). The calibration curve for the nomogram fit well, and the decision curve analysis revealed superior net benefit. The optimal cut-off point for the nomogram was determined to be 40, yielding sensitivity, specificity, positive predictive value, and negative predictive value of 83.10%, 87.20%, 65.90% and 94.60%, respectively.
UNASSIGNED: The nomogram we developed provides an optimized tool for predicting ALF in SRHCC patients. Its application can help determine individual patient\'s risk of ALF, enabling more rational and personalized treatment strategies.
摘要：
■急性肝衰竭（ALF）是自发性破裂肝细胞癌（SRHCC）的严重并发症，需要准确预测有效的治疗策略。我们旨在开发一个预测列线图来估计接受治疗的SRHCC患者的ALF风险。
■我们对过去十年来吉林大学第一医院诊断为SRHCC的284例患者的历史数据进行了回顾性分析。通过单变量和多变量逻辑回归分析选择变量，并构建了预测列线图。我们根据Child-Pugh评分评估了其预测准确性，R.MELD,和ALBI通过评估歧视，校准,和净临床效益。
■在284名患者中，65发展ALF。确定用于模型开发的风险因素包括最大肿瘤大小(LTS)，血小板计数,凝血酶原时间延长，血清甲胎蛋白水平升高。列线图在预测ALF风险方面表现出很高的准确性，C指数为0.91（0.87-0.95）。Delong检验显示列线图与其他三个模型之间存在显着差异（p<0.05）。列线图的校正曲线拟合良好,决策曲线分析显示出优越的净收益。将列线图的最佳截止点确定为40，从而产生灵敏度，特异性，正预测值，阴性预测值为83.10%，87.20%,65.90%和94.60%，分别。
■我们开发的列线图提供了预测SRHCC患者ALF的优化工具。它的应用可以帮助确定个体患者的ALF风险，实现更合理和个性化的治疗策略。
■我们对过去十年来吉林大学第一医院诊断为SRHCC的284例患者的历史数据进行了回顾性分析。通过单变量和多变量逻辑回归分析选择变量，并构建了预测列线图。我们根据Child-Pugh评分评估了其预测准确性，R.MELD,和ALBI通过评估歧视，校准,和净临床效益。
■在284名患者中，65发展ALF。确定用于模型开发的风险因素包括最大肿瘤大小(LTS)，血小板计数,凝血酶原时间延长，血清甲胎蛋白水平升高。列线图在预测ALF风险方面表现出很高的准确性，C指数为0.91（0.87-0.95）。Delong检验显示列线图与其他三个模型之间存在显着差异（p<0.05）。列线图的校正曲线拟合良好,决策曲线分析显示出优越的净收益。将列线图的最佳截止点确定为40，从而产生灵敏度，特异性，正预测值，阴性预测值为83.10%，87.20%,65.90%和94.60%，分别。
■我们开发的列线图提供了预测SRHCC患者ALF的优化工具。它的应用可以帮助确定个体患者的ALF风险，实现更合理和个性化的治疗策略。
