Construction and evaluation of hyperkalemia risk prediction model in patients with chronic kidney disease
董栋 1张秋玲2
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作者信息
1. 杭州市临平区第一人民医院肾内科,杭州 311100
2. 杭州师范大学附属医院内分泌科,杭州 310015
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摘要
本研究通过回顾性分析慢性肾脏病(chronic kidney disease,CKD)患者发生高钾血症的危险因素,建立风险评估模型并进行验证.回顾性收集2022年6月至2024年1月于临平区第一人民医院肾内科就诊的CKD患者505例,通过7∶3比例分为建模集(354例)和验证集(151例).通过最小绝对收缩和选择算子(least absolute shrinkage and selection operator,Lasso)筛选CKD患者发生高钾血症的独立相关因素,通过多因素Logistic回归构建风险预测模型,并采用Hosmer-Lemeshow检验判断预测模型的拟合度,绘制列线图、受试者工作特征曲线、校正曲线和临床决策曲线.结果显示,505例CKD患者中高钾血症155例,患病率为30.69％.Lasso回归和多因素Logistic回归分析结果显示,血清白蛋白、血钠、血磷、甲状旁腺素和风险评分X(基于2020年国内学者建立的CKD高钾血症风险预测模型所得数值)为CKD患者高钾血症的独立相关因素(均P＜0.05).受试者工作特征曲线分析显示,建模集曲线下面积为0.840(95％CI0.796～0.884),验证集曲线下面积为0.849(95％CI 0.784～0.915),校准曲线提示该模型预测结果和实际结果的一致性良好,决策曲线分析提示使用该模型能够使患者获益度增加.最后作者绘制了该模型的可视化列线图.作者认为,本研究构建的CKD患者高钾血症的列线图预测模型有较好的预测能力,有助于临床医务人员早期发现和管理CKD患者的高钾血症.
Abstract
This study retrospectively analyzed the risk factors of hyperkalemia in patients with chronic kidney disease(CKD),established a risk assessment model,and validated it.It was a retrospective study.Data from 505 CKD patients who visited the Department of Nephrology at Linping First People's Hospital from June 2022 to January 2024 were retrospectively collected.Patients were divided into a modeling set(n=354)and a validation set(n=151)in a 7:3 ratio.The independent risk factors for hyperkalemia in CKD patients were screened using least absolute shrinkage and selection operator(Lasso)regression,and a risk prediction model was constructed using multivariate Logistic regression.Hosmer-Lemeshow test was used to judge the fit degree of the prediction model,and the nomogram,the receiver operating characteristic curve,the calibration curve and clinical decision curve were drawn.The results showed that 155 out of 505 CKD patients developed hyperkalemia,with an incidence rate of 30.69％.Lasso regression and multivariate logistic regression analysis showed that serum albumin,blood sodium,blood phosphorus,parathyroid hormone,and risk score X(the value was based on the CKD hyperkalemia risk prediction model established in China in 2020)were the independent factors associated with hyperkalemia in CKD patients(all P＜0.05).The analysis of the receiver operating characteristic curve of the subjects showed that the area under the modeling set curve was 0.840(95％CI 0.796-0.884),and the area under the validation set curve was 0.849(95％CI 0.784-0.915).The calibration curve suggested good consistency between the predicted and actual results of the model,and the decision curve analysis suggested that the model could increase patient benefits.Finally,the authors drew a visual nomogram of the model.The authors believe that the column chart prediction model for hyperkalemia in CKD patients constructed based on the predictive variables selected by Lasso regression ha good predictive ability and is helpful for clinical medical personnel to detect and manage hyperkalemia early in CKD patients.
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