Xing X Y, Yao L, Li Y B, Zhang F X, Li P, Qiao Y J, Liang X H, Wang P, Liu Z S
Blood Purification Center, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
Zhonghua Yi Xue Za Zhi. 2021 Nov 16;101(42):3495-3500. doi: 10.3760/cma.j.cn112137-20210525-01203.
To explore risk factors for hyperkalemia in hemodialysis (HD) patients, and establish and verify a risk assessment model of hyperkalemia in HD patients. The clinical data of HD patients who were admitted to the Department of Nephrology of the First Affiliated Hospital of Zhengzhou University between April 2020 and January 2021 were retrospectively collected and divided into training dataset and validation dataset by using the conversion-random number generator. In the training dataset, multivariate logistic regression analysis was used to screen the risk factors for hyperkalemia in HD patients and the factors were scored to establish the risk assessment model. The validation dataset was substituted into the model and the receiver operating characteristic (ROC) curve was drawn and the area under the curve (AUC) was calculated to verify the effectiveness of the risk prediction model in predicting hyperkalemia. A total of 502 HD patients were enrolled and further divided into training dataset (=372) and validation dataset (=130). There were 268 males and 234 females, with a mean age of (54±13) years. Multivariate logistic regression analysis showed that metabolic acidosis, high potassium diet, history of hyperkalemia, the change of electrocardiogram (ECG), disfunction of vascular access and time interval from last dialysis were risk factors for causing hyperkalemia in patients undergoing HD. Risk assessment model was established based on these risk factors. The AUC of the ROC curve was 0.799. Using 5 as the cut-off value, the sensitivity and specificity for predicting hyperkalemia events was 61.4% and 86.3%, respectively. The current study preliminarily established a risk assessment model for hyperkalemia in HD patients, which can help clinicians manage the potassium level of HD patients.
探讨血液透析（HD）患者高钾血症的危险因素，建立并验证HD患者高钾血症风险评估模型。回顾性收集2020年4月至2021年1月在郑州大学第一附属医院肾内科住院的HD患者的临床资料，并使用转换随机数生成器将其分为训练数据集和验证数据集。在训练数据集中，采用多因素logistic回归分析筛选HD患者高钾血症的危险因素，并对这些因素进行评分以建立风险评估模型。将验证数据集代入模型，绘制受试者工作特征（ROC）曲线并计算曲线下面积（AUC），以验证风险预测模型预测高钾血症的有效性。共纳入502例HD患者，进一步分为训练数据集（n=372）和验证数据集（n=130）。其中男性268例，女性234例，平均年龄（54±13）岁。多因素logistic回归分析显示，代谢性酸中毒、高钾饮食、高钾血症病史、心电图变化、血管通路功能障碍以及距上次透析的时间间隔是HD患者发生高钾血症的危险因素。基于这些危险因素建立了风险评估模型。ROC曲线的AUC为0.799。以5为临界值，预测高钾血症事件的灵敏度和特异度分别为61.4%和86.3%。本研究初步建立了HD患者高钾血症风险评估模型，有助于临床医生管理HD患者的血钾水平。
