Luo X L, Xu J, Xue C, Ruan M N, Yang M, Chen J Q, Huang X C, Chen J, Mei C L, Mao Z G
Department of Nephrology, Changzheng Hospital, Shanghai 200003, China.
Zhonghua Yi Xue Za Zhi. 2021 Nov 16;101(42):3490-3494. doi: 10.3760/cma.j.cn112137-20210715-01587.
To validate the accuracy and consistency of a previously established prediction model for the occurrence of hyperkalemia in non-dialytic chronic kidney disease (CKD) patients. All patients diagnosed with CKD from Outpatient Department of Shanghai Changzheng Hospital during the 4th quarter of 2020 were recruited. Demographic data, clinical characteristics and prediction model-related parameters of the patients were collected and analyzed. Receiver operating characteristic (ROC) curve was drawn to evaluate the effectiveness of the model, and the specificity and sensitivity were calculated based on the cut-off value of 4 obtained from the previous model. The improved Hanley method was used to compare the area under the curve (AUC) between the previously established model and current validation dataset. The calibration curve was drawn to verify the model calibration degree. A total of 434 patients diagnosed with non-dialytic CKD were enrolled, among whom 233 were males and 201 were females, with an average age of (55±16) years. According to the measured serum potassium values, the prevalence of hyperkalemia was 7.6%. And 33 patients were allocated to the hyperkalemia group and 401 patients were to the normal potassium group. There was no significant difference in age and sex between the two groups (both >0.05). A combination of hyperkalemia and heart failure (27.3% vs 3.7%, <0.001), diabetes (42.4% vs 19.7%, =0.002), and acidosis (51.5% vs 7.0%, <0.001) were more frequently in the hyperkalemia group, compared with the normal serum potassium group. Patients in the hyperkalemia group were more likely to have a past history of serum potassium ≥5.0 mmol/L (48.5% vs 2.5%, <0.001). For the drugs that could increase serum potassium levels, there was a significant correlation between Chinese herbal medicine and the occurrence of hyperkalemia, while renin-angiotensin-aldosterone system inhibitor (RAASi) and potassium supplementation showed no significant difference between the two groups. The results of ROC curve analysis showed that the AUC was 0.914, with the sensitivity of 84.8% and the specificity of 79.8% with the cut-off value of 4. The difference of AUC between the previously established risk assessment model of hyperkalemia in patients with non-dialytic CKD and current validation dataset was not statistically significant (=1.924, =0.054), indicating the good accuracy and consistency of the prediction model. In the calibration curve, when the predicted risk of patients was below 0.4 or above 0.6, the prediction effect of the model was better. The previously-constructed hyperkalemia prediction model in non-dialytic CKD patients had good accuracy and consistency, and could be used to evaluate the risk of hyperkalemia in all stages of non-dialytic CKD patients.
验证先前建立的非透析慢性肾脏病（CKD）患者高钾血症发生预测模型的准确性和一致性。招募了2020年第四季度在上海长征医院门诊诊断为CKD的所有患者。收集并分析患者的人口统计学数据、临床特征和预测模型相关参数。绘制受试者工作特征（ROC）曲线以评估模型的有效性，并根据先前模型获得的截断值4计算特异性和敏感性。采用改进的Hanley法比较先前建立的模型与当前验证数据集之间的曲线下面积（AUC）。绘制校准曲线以验证模型校准程度。共纳入434例诊断为非透析CKD的患者，其中男性233例，女性201例，平均年龄（55±16）岁。根据测得的血清钾值，高钾血症患病率为7.6%。33例患者被分配至高钾血症组，401例患者被分配至正常血钾组。两组患者的年龄和性别无显著差异（均>0.05）。与正常血钾组相比高钾血症组中高钾血症合并心力衰竭（27.3%对3.7%，<0.001）、糖尿病（42.4%对19.7%，=0.002）和酸中毒（51.5%对7.0%，<0.001）更为常见。高钾血症组患者既往血清钾≥5.0 mmol/L的病史更常见（48.5%对2.5%，<0.001）。对于可升高血清钾水平的药物，中草药与高钾血症的发生之间存在显著相关性，而肾素-血管紧张素-醛固酮系统抑制剂（RAASi）和补钾在两组之间无显著差异。ROC曲线分析结果显示，AUC为0.914，截断值为4时敏感性为84.8%，特异性为79.8%。非透析CKD患者先前建立的高钾血症风险评估模型与当前验证数据集之间的AUC差异无统计学意义（=1.924，=0.054），表明预测模型具有良好的准确性和一致性。在校准曲线中，当患者的预测风险低于0.4或高于0.6时，模型的预测效果更好。先前构建的非透析CKD患者高钾血症预测模型具有良好的准确性和一致性，可用于评估非透析CKD各阶段患者的高钾血症风险。
