PDF(Pubmed)
Abstract：
UNASSIGNED: This study aimed to develop two predictive nomograms for the assessment of long-term survival status in hemodialysis (HD) patients by examining the prognostic factors for all-cause mortality and cardiovascular (CVD) event mortality.
UNASSIGNED: A total of 551 HD patients with an average age of over 60 were included in this study. The patients\' medical records were collected from our hospital and randomly allocated to two cohorts: the training cohort (n=385) and the validation cohort (n=166). We employed multivariate Cox assessments and fine-gray proportional hazards models to explore the predictive factors for both all-cause mortality and cardiovascular event mortality risk in HD patients. Two nomograms were established based on predictive factors to forecast patients\' likelihood of survival for 3, 5, and 8 years. The performance of both models was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis.
UNASSIGNED: The nomogram for all-cause mortality prediction included seven factors: age ≥ 60, sex (male), history of diabetes and coronary artery disease, diastolic blood pressure, total triglycerides (TG), and total cholesterol (TC). The nomogram for cardiovascular event mortality prediction included three factors: history of diabetes and coronary artery disease, and total cholesterol (TC). Both models demonstrated good discrimination, with AUC values of 0.716, 0.722 and 0.725 for all-cause mortality at 3, 5, and 8 years, respectively, and 0.702, 0.695, and 0.677 for cardiovascular event mortality, respectively. The calibration plots indicated a good agreement between the predictions and the decision curve analysis demonstrated a favorable clinical utility of the nomograms.
UNASSIGNED: Our nomograms were well-calibrated and exhibited significant estimation efficiency, providing a valuable predictive tool to forecast prognosis in HD patients.
UNASSIGNED: A total of 551 HD patients with an average age of over 60 were included in this study. The patients\' medical records were collected from our hospital and randomly allocated to two cohorts: the training cohort (n=385) and the validation cohort (n=166). We employed multivariate Cox assessments and fine-gray proportional hazards models to explore the predictive factors for both all-cause mortality and cardiovascular event mortality risk in HD patients. Two nomograms were established based on predictive factors to forecast patients\' likelihood of survival for 3, 5, and 8 years. The performance of both models was evaluated using the area under the curve (AUC), calibration plots, and decision curve analysis.
UNASSIGNED: The nomogram for all-cause mortality prediction included seven factors: age ≥ 60, sex (male), history of diabetes and coronary artery disease, diastolic blood pressure, total triglycerides (TG), and total cholesterol (TC). The nomogram for cardiovascular event mortality prediction included three factors: history of diabetes and coronary artery disease, and total cholesterol (TC). Both models demonstrated good discrimination, with AUC values of 0.716, 0.722 and 0.725 for all-cause mortality at 3, 5, and 8 years, respectively, and 0.702, 0.695, and 0.677 for cardiovascular event mortality, respectively. The calibration plots indicated a good agreement between the predictions and the decision curve analysis demonstrated a favorable clinical utility of the nomograms.
UNASSIGNED: Our nomograms were well-calibrated and exhibited significant estimation efficiency, providing a valuable predictive tool to forecast prognosis in HD patients.
摘要：
■本研究旨在通过检查全因死亡率和心血管（CVD）事件死亡率的预后因素，开发两个预测列线图，用于评估血液透析（HD）患者的长期生存状态。
■本研究共纳入551名平均年龄超过60岁的HD患者。从我们医院收集患者的医疗记录，并随机分配到两个队列：训练队列（n=385）和验证队列（n=166）。我们采用多变量Cox评估和精细灰色比例风险模型来探索HD患者全因死亡率和心血管事件死亡风险的预测因素。根据预测因子建立两个列线图来预测患者3年、5年和8年的生存可能性。使用曲线下面积（AUC）评估两个模型的性能，校准图,和决策曲线分析。
■全因死亡率预测的列线图包括七个因素：年龄≥60岁，性别（男性），糖尿病和冠状动脉疾病史，舒张压,总甘油三酯（TG），和总胆固醇（TC）。心血管事件死亡率预测的列线图包括三个因素：糖尿病史和冠状动脉疾病，和总胆固醇（TC）。两种模式都表现出良好的鉴别力，3、5和8年全因死亡率的AUC值为0.716、0.722和0.725，分别,心血管事件死亡率为0.702、0.695和0.677，分别。校准图表明预测和决策曲线分析之间的良好一致性，证明了列线图的有利临床实用性。
■我们的列线图经过了很好的校准，并显示了显着的估计效率，为预测HD患者的预后提供了有价值的预测工具。
■本研究共纳入551名平均年龄超过60岁的HD患者。从我们医院收集患者的医疗记录，并随机分配到两个队列：训练队列（n=385）和验证队列（n=166）。我们采用多变量Cox评估和精细灰色比例风险模型来探索HD患者全因死亡率和心血管事件死亡风险的预测因素。根据预测因子建立两个列线图来预测患者3年、5年和8年的生存可能性。使用曲线下面积（AUC）评估两个模型的性能，校准图,和决策曲线分析。
■全因死亡率预测的列线图包括七个因素：年龄≥60岁，性别（男性），糖尿病和冠状动脉疾病史，舒张压,总甘油三酯（TG），和总胆固醇（TC）。心血管事件死亡率预测的列线图包括三个因素：糖尿病史和冠状动脉疾病，和总胆固醇（TC）。两种模式都表现出良好的鉴别力，3、5和8年全因死亡率的AUC值为0.716、0.722和0.725，分别,心血管事件死亡率为0.702、0.695和0.677，分别。校准图表明预测和决策曲线分析之间的良好一致性，证明了列线图的有利临床实用性。
■我们的列线图经过了很好的校准，并显示了显着的估计效率，为预测HD患者的预后提供了有价值的预测工具。
