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1.徐州市儿童医院药学部,江苏 徐州 221006
2.徐州市儿童医院重症医学科,江苏 徐州 221006
3.徐州医科大学附属医院药学部,江苏 徐州 221000
副主任药师,硕士。研究方向:儿童合理用药及个体化给药。E-mail:lyqxzj2012@163.com
副主任药师。研究方向:医院药学。E-mail:xuzhouzhaolei2008@163.com
收稿:2025-07-16,
修回:2025-11-28,
录用:2025-11-29,
纸质出版:2026-01-15
移动端阅览
刘育青,朱磊,韩兆欢,等.他克莫司治疗儿童激素耐药型肾病综合征期间出现急性肾损伤的危险因素及预测模型研究 [J].中国药房,2026,37(01):66-71.
LIU Yuqing,ZHU Lei,HAN Zhaohuan,et al.Study on the risk factors and predictive model for acute kidney injury during tacrolimus treatment for pediatric steroid-resistant nephrotic syndrome[J].ZHONGGUO YAOFANG,2026,37(01):66-71.
刘育青,朱磊,韩兆欢,等.他克莫司治疗儿童激素耐药型肾病综合征期间出现急性肾损伤的危险因素及预测模型研究 [J].中国药房,2026,37(01):66-71. DOI: 10.6039/j.issn.1001-0408.2026.01.12.
LIU Yuqing,ZHU Lei,HAN Zhaohuan,et al.Study on the risk factors and predictive model for acute kidney injury during tacrolimus treatment for pediatric steroid-resistant nephrotic syndrome[J].ZHONGGUO YAOFANG,2026,37(01):66-71. DOI: 10.6039/j.issn.1001-0408.2026.01.12.
目的
2
探讨儿童激素耐药型肾病综合征(SRNS)患者在接受他克莫司治疗期间出现急性肾损伤(AKI)的危险因素,并构建其预测模型。
方法
2
回顾性选取2022年1月1日至2023年12月31日在徐州市儿童医院确诊为SRNS并接受他克莫司治疗的155例患儿作为研究对象。通过调阅病历系统收集患儿的各类临床资料,将用药期间发生AKI的患儿作为AKI组(
n
=26),未发生AKI的患儿作为对照组(
n
=129)。采用单因素和多因素Logistic回归分析筛选出独立危险因素,并基于显著变量构建临床预测模型,同时绘制列线图、校准曲线、受试者操作特征曲线及决策曲线以评价模型性能。
结果
2
单因素分析显示,血尿素氮(BUN)、血肌酐(Scr)、估算肾小球滤过率(eGFR)、他克莫司最大谷浓度(
c
min
)、
CYP3A5
*3/*3基因型、并发感染、并发高血压以及使用非甾体抗炎药是SRNS患儿接受他克莫司治疗期间发生AKI的影响因素(
P
<0.05);多因素Logistic回归分析显示,BUN≥9.58 mmol/L、Scr≥125 μmol/L、eGFR<37 mL/(min·1.73 m²)、他克莫司最大
c
min
≥11.26 ng/mL、
CYP3A5
*3/*3基因型、并发感染及并发高血压是影响SRNS患儿接受他克莫司治疗期间发生AKI的独立危险因素(
P
<0.05)。构建的临床预测模型的曲线下面积=0.747,预测AKI发生情况与实际AKI发生情况较为吻合,并且在预测患儿AKI发生时具有良好的临床净获益。
结论
2
基线肾功能受损(BUN升高、Scr升高及eGFR下降)、他克莫司最大
c
min
偏高、
CYP3A5
*3/*3基因型、治疗过程中并发感染和高血压是SRNS患儿使用他克莫司治疗期间发生AKI的独立危险因素。所建临床预测模型为实施风险分层管理提供了科学依据。
OBJECTIVE
2
To explore the risk factors for acute kidney injury (AKI) in children with steroid-resistant nephrotic syndrome (SRNS) during tacrolimus treatment and construct a predictive model.
METHODS
2
A retrospective selection was made of 155 children diagnosed with SRNS and treated with tacrolimus at Xuzhou Children’s Hospital from January 1, 2022, to December 31, 2023, serving as the study subjects. Various clinical data of the children were collected by reviewing the medical record system. Children who developed AKI during medication were assigned to the AKI group (
n
=26), and those who did not develop AKI were assigned to the control group (
n
=129). Univariate and multivariate Logistic regression analyses were used to screen independent risk factors. A clinical predictive model was constructed based on significant variables, and nomogram, calibration curve, receiver operator characteristic curve, and decision curve were drawn to evaluate the model’s performance.
RESULTS
2
Univariate analysis showed that blood urea nitrogen (BUN), serum creatinine (Scr), estimated glomerular filtration rate (eGFR), the maximum trough concentration (
c
min
) of tacrolimus,
CYP3A5
*3/*3 genotype, concurrent infection, concurrent hypertension, and the use of non-steroidal anti-inflammatory drugs were influencing factors for AKI in children with SRNS during tacrolimus treatment (
P
<0.05). Multivariate Logistic regression analysis revealed that BUN≥9.58 mmol/L, Scr≥125 μmol/L, eGFR<37 mL/(min·1.73 m
2
), tacrolimus maximum
c
min
≥11.26 ng/mL,
CYP3A5
*3/*3 genotype, concurrent infection, and concurrent hypertension were independent risk factors for AKI in children with SRNS during tacrolimus treatment (
P
<0.05). The constructed clinical predictive model had an area under the curve of 0.747, showing good agreement between predicted and actual AKI occurrence and demonstrating favorable clinical net benefit in predicting AKI in children.
CONCLUSIONS
2
Impaired baseline renal function (elevated BUN, elevated Scr, and decreased eGFR), elevated maximum
c
min
of tacrolimus,
CYP3A5
*3/*3 genotype, concurrent infection, and hypertension during treatment are independent risk factors for AKI in children with SRNS during tacrolimus treatment. The established clinical predictive model provides a scientific basis for implementing risk stratification management.
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