基于透析基本数据、超声和常规血生化构建维持性血液透析患者容量超负荷的早期预警模型及临床效用分析

Constructing a model of MHD capacity overload and its clinical effectiveness

目的基于透析基本数据、超声和常规血生化构建维持性血液透析(maintenance hemodialysis,MHD)容量超负荷的早期预警模型及临床效用。方法回顾性纳入2023年1月至2023年12月在南昌大学第一附属医院肾内血透中心接受MHD患者603例,按照2:1比例分为建模集(n=402)、验证集(n=201)。收集患者临床资料,单因素、LASSO-Logistic回归分析建模集中MHD容量超负荷的影响因素,并构建风险预测列线图模型,采用一致性指数、校准曲线、受试者工作特征曲线(receiver operating characteristic curve,ROC)、决策曲线对该预测模型进行内外部验证。结果建模集中超负荷组血清白蛋白、尿素清除指数(urea clearanceindex,Kt/V)、残余肾功能(residual renal function,RRF)、左室射血分数低于正常组,N末端脑钠肽前体(N-terminal pro-brain natriuretic peptide,NT-proBNP),肺超声显示B线,右房内径高于正常组(P<0.05);LASSO结果显示,血清白蛋白、Kt/V、RRF、左室射血分数、NT-proBNP、超声肺B线、右房内径是MHD容量超负荷的前期的特征变量。Logistic回归结果显示,Kt/V、血清白蛋白、RRF、左室射血分数是MHD容量超负荷情况的保护因素,NT-proBNP,肺超声显示B线,右房内径是MHD容量超负荷情况的危险因素(P<0.05);根据Logistic回归影响因素构建患者容量超负荷的风险预测列线图模型,在建模集和验证集中一致性指数分别为0.860、0.814,校准曲线显示该模型在建模集和验证集中预测MHD容量超负荷发生率与实际发生率基本一致,ROC曲线显示该模型在建模集和验证集中预测的曲线下面积分别为0.917(95%CI:0.886~0.948)、0.916(95%CI:0.869~0.962),决策曲线显示,当建模集高风险阈值在0~0.7时、验证集高风险阈值在0~0.7时该模型具有较好的临床净收益。结论MHD患者容量超负荷发生受众多因素影响,如血清白蛋白、Kt/V、RRF、左室射血分数、NT-proBNP,肺超声显示B线,右房内径,根据患者发生容量超负荷相关因素构建的早期预警模型具有良好预测能力和临床获益度。

Objective To construct an early warning model of maintenance hemodialysis(MHD)capacity overload based upon basic dialysis data,ultrasound and routine blood biochemistry and examine its clinical effectiveness.Methods From January 2023 to December 2023,603 patients on MHD were retrospectively reviewed.They were assigned into two sets of modeling(n=402)and validation(n=201)according to a ratio of 2:1.The relevant clinical were collected.And the influencing factors of MHD capacity overload in modeling set were analyzed by univariate and LASSO-Logistic regression.The risk prediction nomogram model was constructed.And consistency index,calibration curve,receiver operating characteristic(ROC)curve and decision curve were utilized for verifying the prediction model internally and externally.Results Serum albumin,Kt/V,RRF and left ventricular ejection fraction(LVEF)in overload group were lower than those in normal group.NT-proBNP,ultrasonic lung B-line and right atrial diameter were higher than those in normal group(P<0.05).LASSO results indicated that serum albumin,Kt/V,RRF,LVEF,NT-proBNP,ultrasonic lung B-line and right atrial internal diameter were Top 7 characteristic variables of volume overload in MHD.Logistic regression results revealed that Kt/V,serum albumin,RRF and LVEF were the protective factors for MHD volume overload.And NT-proBNP,ultrasonic lung B-line and right atrial diameter were the risk factors for MHD volume overload(P<0.05).The risk prediction nomogram model of capacity overload was constructed according to the influencing factors of Logistic regression.Consistency index in modeling and validation sets was 0.860 and 0.814,respectively.Calibration curve showed that the incidence of MHD capacity overload predicted by the model in modeling and validation sets was basically consistent with the actual incidence.ROC curve indicated that the area under the curve as predicted by the model in modeling and validation sets was 0.917(95%CI:0.886-0.948)and 0.916(95%CI:0.869-0.962).Decision curve revealed that when the high-risk threshold of modeling set was 0-0.7 and the high risk threshold of validation set 0-0.7.The model had an excellent clinical net benefit.Conclusion In MHD patients,the occurrence of volume overload is affected by serum albumin,Kt/V,RRF,LVEF,NT-proBNP,ultrasonic lung B-line and right atrial diameter.The early warning model built according to the factors related to volume overload has excellent predictive capability and clinical benefit.