Article(id=1322929655648485757, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.14.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753113600000, receivedDateStr=2025-07-22, revisedDate=1777478400000, revisedDateStr=2026-04-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340608751, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340608751, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340608751, creator=13701087609, updateTime=1793340608751, updator=13701087609, issue=Issue{id=1322929547464815391, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='14', pageStart='2385', pageEnd='2576', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793340582958, creator='13701087609', updateTime=1793343111003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940150912790807, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940150912790808, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2430, endPage=2436, ext={EN=ArticleExt(id=1322929655874978174, articleId=1322929655648485757, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Construction and validation of a risk prediction model for adrenal crisis in children with nephrotic syndrome, columnId=1220689484526572183, journalTitle=Chinese Nursing Research, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the influencing factors of adrenal crisis(AC) in children with nephrotic syndrome.To construct a risk prediction model,and validate its performance.

Methods

A total of 436 children with nephrotic syndrome admitted to the department of pediatric nephrology and rheumatology of a tertiary hospital in Guangdong province from May 2021 to May 2024 were selected as the study subjects.The children were divided into a training set(305 cases) and a validation set(131 cases) at a ratio of 7∶3.LASSO regression analysis and ten-fold cross-validation were used to screen significant variables influencing the occurrence of AC in children with nephrotic syndrome.Logistic regression analysis was employed to identify the influencing factors of AC in children with nephrotic syndrome.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC),the Hosmer-Lemeshow goodness-of-fit test,calibration curves,and clinical decision curves.

Results

Among the 436 children with nephrotic syndrome,60(13.76%) developed AC.Logistic regression analysis showed that the type of hormone effect,nephrotic syndrome recurrence>3 times,concurrent infection,previous history of AC,irregular use of traditional Chinese medicine,and excessively rapid hormone tapering or abrupt withdrawal were influencing factors for AC in children with nephrotic syndrome(all P<0.05).In the training set,the AUC was 0.831,the sensitivity was 0.767,the specificity was 0.756,the Kappa value was 0.343,and the Youden index was 0.523.In the validation set,the AUC was 0.855,the sensitivity was 1.000,the specificity was 0.561,the Kappa value was 0.249,and the Youden index was 0.561.The calibration curves of both the training set and the validation set were close to the ideal curves.The Hosmer-Lemeshow test yielded a χ2 of 1.131(P=0.769) for the training set and a χ2 of 7.271(P=0.064) for the validation set.The clinical decision curves indicated that the risk threshold probability ranged from 0.02 to 0.94 in the training set and from 0.02 to 0.60 in the validation set.

Conclusions

The risk prediction model for AC in children with nephrotic syndrome demonstrated good discrimination,accuracy,and clinical decision-making performance,which may assist healthcare providers in the early identification of children with nephrotic syndrome at risk of AC.

, authors=Baoling ZHONG1, Zhiying LI2, Liaoli QIU1, Wei YANG1, Xian ZHU1, Ling LIAO1, Zhijun HUANG1, Yiyue BI1, Xiaohong LIU1, authorsList=Baoling ZHONG, Zhiying LI, Liaoli QIU, Wei YANG, Xian ZHU, Ling LIAO, Zhijun HUANG, Yiyue BI, Xiaohong LIU, authorCompany=null, correspAuthors=Zhiying LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322929660014756241, articleId=1322929655648485757, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=肾病综合征患儿肾上腺危象发生风险预测模型的构建与验证, columnId=1220689484681761433, journalTitle=护理研究, columnName=科研论著, runingTitle=null, highlight=null, articleAbstract=
目的

探讨肾病综合征患儿发生肾上腺危象(AC)的影响因素,构建风险预测模型并验证模型效能。

方法

选取2021年5月—2024年5月广东省某三级甲等医院小儿肾脏风湿病科收治的436例肾病综合征患儿为研究对象,以7∶3划分为训练集(305例)和验证集(131例)。以LASSO回归分析和十折交叉验证筛选肾病综合征患儿AC发生影响因素的显著变量,采用Logistic回归分析探讨肾病综合征患儿AC发生的影响因素,采用受试者工作特征曲线下面积(AUC)、Hosmer-Lemeshow拟合优度检验、校准曲线、临床决策曲线评价模型效能。

结果

436例肾病综合征患儿中,有60例(13.76%)患儿发生AC。Logistic回归分析结果显示,激素效应类型、NS复发>3次、合并感染、AC史、不规范使用中药、激素减量过快或骤停为肾病综合征患儿AC发生的影响因素(均P<0.05)。训练集AUC为0.831,敏感度为0.767,特异度为0.756,Kappa值为0.343,约登指数为0.523。验证集AUC为0.855,敏感度为1.000,特异度为0.561,Kappa值为0.249,约登指数为0.561。训练集和验证集的校准曲线和理想曲线接近。训练集中Hosmer-Lemeshow检验结果显示,χ2=1.131(P=0.769),验证集中Hosmer-Lemeshow检验结果显示,χ2=7.271(P=0.064)。临床决策曲线结果显示,训练集的风险阈值概率为0.02~0.94,验证集的风险阈值概率为0.02~0.60。

结论

肾病综合征患儿AC发生风险预测模型具有较好的区分度、准确度、临床决策性能,可帮助医护人员早期识别有AC风险的肾病综合征患儿。

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钟宝玲,主管护师,硕士研究生

, correspAuthorsNote=
李智英,E-mail:
LI Zhiying, E-mail:
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tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929655648485757, language=EN, label=Table 1, caption=

Univariate analysis of factors influencing AC in children with NS

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类非AC组(n=262)AC组(n=43)统计值P
生活在农村否89(34.0)8(18.6)χ2=4.0210.045
是173(66.0)35(81.4)
复发否88(33.6)8(18.6)χ2=3.8450.050
是174(66.4)35(81.4)
激素效应类型激素敏感172(65.6)21(48.8)
激素依赖35(13.4)3(7.0)χ2=11.0470.004
激素耐药55(21.0)19(44.2)
NS复发>3次否142(54.2)11(25.6)χ2=12.100<0.001
是120(45.8)32(74.4)
创伤或应激无157(59.9)19(44.2)χ2=3.7480.053
有105(40.1)24(55.8)
合并感染无113(43.1)5(11.6)χ2=15.453<0.001
有149(56.9)38(88.4)
呼吸道感染无139(53.1)15(34.9)χ2=4.8780.027
有123(46.9)28(65.1)
AC史无256(97.7)35(81.4)<0.001①
有6(2.3)8(18.6)
不规范使用中药否247(94.3)35(81.4)0.008①
是15(5.7)8(18.6)
使用≥2种免疫抑制剂否205(78.2)26(60.5)χ2=6.3540.012
是57(21.8)17(39.5)
激素减量过快或骤停否94(35.9)4(9.3)χ2=11.963<0.001
是168(64.1)39(90.7)
免疫抑制剂减量或减停否223(85.1)29(67.4)χ2=8.0350.005
是39(14.9)14(32.6)
白细胞计数(×109/L)9.36(7.05,11.80)14.39(11.86,20.04)Z=-6.4140.056
中性分叶核粒细胞计数(×109/L)4.91(3.04,7.39)11.11(7.63,15.02)Z=-5.985<0.001
血清白蛋白(g/L)28(19,37)16(14,19)Z=7.348<0.001
血清钙(mmol/L)2.20(2.00,2.34)1.80(1.77,1.90)Z=7.857<0.001
), ArticleFig(id=1322929669229642212, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929655648485757, language=CN, label=表1, caption=

NS患儿AC发生影响因素的单因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
项目分类非AC组(n=262)AC组(n=43)统计值P
生活在农村否89(34.0)8(18.6)χ2=4.0210.045
是173(66.0)35(81.4)
复发否88(33.6)8(18.6)χ2=3.8450.050
是174(66.4)35(81.4)
激素效应类型激素敏感172(65.6)21(48.8)
激素依赖35(13.4)3(7.0)χ2=11.0470.004
激素耐药55(21.0)19(44.2)
NS复发>3次否142(54.2)11(25.6)χ2=12.100<0.001
是120(45.8)32(74.4)
创伤或应激无157(59.9)19(44.2)χ2=3.7480.053
有105(40.1)24(55.8)
合并感染无113(43.1)5(11.6)χ2=15.453<0.001
有149(56.9)38(88.4)
呼吸道感染无139(53.1)15(34.9)χ2=4.8780.027
有123(46.9)28(65.1)
AC史无256(97.7)35(81.4)<0.001①
有6(2.3)8(18.6)
不规范使用中药否247(94.3)35(81.4)0.008①
是15(5.7)8(18.6)
使用≥2种免疫抑制剂否205(78.2)26(60.5)χ2=6.3540.012
是57(21.8)17(39.5)
激素减量过快或骤停否94(35.9)4(9.3)χ2=11.963<0.001
是168(64.1)39(90.7)
免疫抑制剂减量或减停否223(85.1)29(67.4)χ2=8.0350.005
是39(14.9)14(32.6)
白细胞计数(×109/L)9.36(7.05,11.80)14.39(11.86,20.04)Z=-6.4140.056
中性分叶核粒细胞计数(×109/L)4.91(3.04,7.39)11.11(7.63,15.02)Z=-5.985<0.001
血清白蛋白(g/L)28(19,37)16(14,19)Z=7.348<0.001
血清钙(mmol/L)2.20(2.00,2.34)1.80(1.77,1.90)Z=7.857<0.001
), ArticleFig(id=1322929669330305509, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929655648485757, language=EN, label=Table 2, caption=

Multivariate analysis of factors influencing AC in children with NS

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Z值POR值95%CI
常量-5.3810.568-9.474<0.001
激素效应类型 激素依赖-1.0500.819-1.2820.2000.350[0.055,1.461]
激素耐药1.0250.4102.5000.0122.787[1.242,6.258]
NS复发>3次0.8910.4232.1050.0352.438[1.088,5.801]
合并感染1.6680.5363.1120.0025.301[2.009,17.029]
AC史2.4200.7553.2070.00111.249[2.658,52.868]
不规范使用中药1.3930.6172.2580.0244.028[1.177,13.622]
激素减量过快或骤停1.5360.5852.6260.0094.645[1.636,17.048]
), ArticleFig(id=1322929669430968806, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322929655648485757, language=CN, label=表2, caption=

NS患儿AC发生影响因素的多因素分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量回归系数标准误Z值POR值95%CI
常量-5.3810.568-9.474<0.001
激素效应类型 激素依赖-1.0500.819-1.2820.2000.350[0.055,1.461]
激素耐药1.0250.4102.5000.0122.787[1.242,6.258]
NS复发>3次0.8910.4232.1050.0352.438[1.088,5.801]
合并感染1.6680.5363.1120.0025.301[2.009,17.029]
AC史2.4200.7553.2070.00111.249[2.658,52.868]
不规范使用中药1.3930.6172.2580.0244.028[1.177,13.622]
激素减量过快或骤停1.5360.5852.6260.0094.645[1.636,17.048]
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肾病综合征患儿肾上腺危象发生风险预测模型的构建与验证
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钟宝玲 1 , 李智英 2, *, * , 丘聊丽 1 , 杨薇 1 , 朱娴 1 , 廖玲 1 , 黄志君 1 , 毕乙越 1 , 刘晓红 1
护理研究 | 科研论著 2026,40(14): 2430-2436
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护理研究 |科研论著 2026 , 40 (14) : 2430 -2436
肾病综合征患儿肾上腺危象发生风险预测模型的构建与验证
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钟宝玲1, 李智英2, *, * , 丘聊丽1, 杨薇1, 朱娴1, 廖玲1, 黄志君1, 毕乙越1, 刘晓红1
作者信息
  • 1中山大学附属第一医院,广东 510030
  • 2中山大学附属第一医院广西医院
通讯作者:
李智英,E-mail:
LI Zhiying, E-mail:
作者简介:

钟宝玲,主管护师,硕士研究生

Construction and validation of a risk prediction model for adrenal crisis in children with nephrotic syndrome
Baoling ZHONG1, Zhiying LI2 , Liaoli QIU1, Wei YANG1, Xian ZHU1, Ling LIAO1, Zhijun HUANG1, Yiyue BI1, Xiaohong LIU1
Affiliations
  • 1The First Affiliated Hospital of Sun Yat-Sen University, Guangdong 510030 China
  • 2Guangxi Hospital of the First Affiliated Hospital, Sun Yat-Sen University
出版时间: 2026-07-25 doi: 10.12102/j.issn.1009-6493.2026.14.006
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目的

探讨肾病综合征患儿发生肾上腺危象(AC)的影响因素,构建风险预测模型并验证模型效能。

方法

选取2021年5月—2024年5月广东省某三级甲等医院小儿肾脏风湿病科收治的436例肾病综合征患儿为研究对象,以7∶3划分为训练集(305例)和验证集(131例)。以LASSO回归分析和十折交叉验证筛选肾病综合征患儿AC发生影响因素的显著变量,采用Logistic回归分析探讨肾病综合征患儿AC发生的影响因素,采用受试者工作特征曲线下面积(AUC)、Hosmer-Lemeshow拟合优度检验、校准曲线、临床决策曲线评价模型效能。

结果

436例肾病综合征患儿中,有60例(13.76%)患儿发生AC。Logistic回归分析结果显示,激素效应类型、NS复发>3次、合并感染、AC史、不规范使用中药、激素减量过快或骤停为肾病综合征患儿AC发生的影响因素(均P<0.05)。训练集AUC为0.831,敏感度为0.767,特异度为0.756,Kappa值为0.343,约登指数为0.523。验证集AUC为0.855,敏感度为1.000,特异度为0.561,Kappa值为0.249,约登指数为0.561。训练集和验证集的校准曲线和理想曲线接近。训练集中Hosmer-Lemeshow检验结果显示,χ2=1.131(P=0.769),验证集中Hosmer-Lemeshow检验结果显示,χ2=7.271(P=0.064)。临床决策曲线结果显示,训练集的风险阈值概率为0.02~0.94,验证集的风险阈值概率为0.02~0.60。

结论

肾病综合征患儿AC发生风险预测模型具有较好的区分度、准确度、临床决策性能,可帮助医护人员早期识别有AC风险的肾病综合征患儿。

儿童  /  肾病综合征  /  肾上腺危象  /  风险预测模型  /  LASSO回归分析  /  影响因素
Objective

To explore the influencing factors of adrenal crisis(AC) in children with nephrotic syndrome.To construct a risk prediction model,and validate its performance.

Methods

A total of 436 children with nephrotic syndrome admitted to the department of pediatric nephrology and rheumatology of a tertiary hospital in Guangdong province from May 2021 to May 2024 were selected as the study subjects.The children were divided into a training set(305 cases) and a validation set(131 cases) at a ratio of 7∶3.LASSO regression analysis and ten-fold cross-validation were used to screen significant variables influencing the occurrence of AC in children with nephrotic syndrome.Logistic regression analysis was employed to identify the influencing factors of AC in children with nephrotic syndrome.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC),the Hosmer-Lemeshow goodness-of-fit test,calibration curves,and clinical decision curves.

Results

Among the 436 children with nephrotic syndrome,60(13.76%) developed AC.Logistic regression analysis showed that the type of hormone effect,nephrotic syndrome recurrence>3 times,concurrent infection,previous history of AC,irregular use of traditional Chinese medicine,and excessively rapid hormone tapering or abrupt withdrawal were influencing factors for AC in children with nephrotic syndrome(all P<0.05).In the training set,the AUC was 0.831,the sensitivity was 0.767,the specificity was 0.756,the Kappa value was 0.343,and the Youden index was 0.523.In the validation set,the AUC was 0.855,the sensitivity was 1.000,the specificity was 0.561,the Kappa value was 0.249,and the Youden index was 0.561.The calibration curves of both the training set and the validation set were close to the ideal curves.The Hosmer-Lemeshow test yielded a χ2 of 1.131(P=0.769) for the training set and a χ2 of 7.271(P=0.064) for the validation set.The clinical decision curves indicated that the risk threshold probability ranged from 0.02 to 0.94 in the training set and from 0.02 to 0.60 in the validation set.

Conclusions

The risk prediction model for AC in children with nephrotic syndrome demonstrated good discrimination,accuracy,and clinical decision-making performance,which may assist healthcare providers in the early identification of children with nephrotic syndrome at risk of AC.

children  /  nephrotic syndrome  /  adrenal crisis  /  risk prediction model  /  LASSO regression analysis  /  influencing factors
钟宝玲, 李智英, 丘聊丽, 杨薇, 朱娴, 廖玲, 黄志君, 毕乙越, 刘晓红. 肾病综合征患儿肾上腺危象发生风险预测模型的构建与验证. 护理研究, 2026 , 40 (14) : 2430 -2436 . DOI: 10.12102/j.issn.1009-6493.2026.14.006
Baoling ZHONG, Zhiying LI, Liaoli QIU, Wei YANG, Xian ZHU, Ling LIAO, Zhijun HUANG, Yiyue BI, Xiaohong LIU. Construction and validation of a risk prediction model for adrenal crisis in children with nephrotic syndrome[J]. Chinese Nursing Research, 2026 , 40 (14) : 2430 -2436 . DOI: 10.12102/j.issn.1009-6493.2026.14.006
肾上腺危象(adrenal crisis,AC)[1]是肾病综合征(nephrotic syndrome,NS)的危急并发症之一[2-3]。已有研究结果显示,长期接受糖皮质激素治疗的NS患儿肾上腺皮质功能抑制发生率为40.0%~69.2%[4]。成年肾上腺皮质功能减退病人中AC年发病率为6%~8%,死亡率为0.5/100病人年[5-6],儿童可能面临更高的风险。目前,AC的早期识别面临医护人员对AC及其影响因素认知不足、NS治疗周期长、病情反复、患儿家庭护理能力薄弱等挑战,且缺乏相应的风险评估工具。国内外关于儿童AC相关风险预测模型研究较少,尤其是NS患儿AC的研究难以全面反映疾病复杂性。因此,本研究分析NS患儿发生AC的影响因素,构建风险预测模型并进行验证,以期帮助临床医护人员早期识别高危人群,为病人的精准护理提供科学依据。
选取2021年5月—2024年5月广东省某三级甲等医院小儿肾脏风湿病科收治的NS患儿为研究对象。纳入标准:1)患有NS[符合儿童激素敏感、复发/依赖NS诊治循证指南(2016)[7]诊断标准];2)年龄0~<18岁。排除标准:1)无法完整获取病历资料;2)先天性肾上腺皮质增生和先天性肾上腺发育不全。参照多因素Logistic 回归的每个变量事件数(EPV)估算方法[8-9]估计最小样本量,样本量至少为研究变量的5~10个EPV。本研究最终纳入患儿436例,以7∶3划分为训练集(305例)和验证集(131例)。本研究已通过中山大学附属第一医院伦理委员会批准(伦理审批号:伦审临〔2023〕697号)。
课题组讨论后自行设计资料调查表,包括患儿一般资料,如年龄、性别、身高、体重、体质指数(BMI)、生长发育情况、患儿文化程度、生活环境、主要照顾者及其文化程度等;现病史,如急性肾损伤、病程、激素效应类型、是否为慢性肾脏病、入院次数、复发次数、合并感染、创伤或应激等;既往史,如肾上腺功能减退史、手术史等;用药史,如糖皮质激素、免疫抑制剂、抗癫痫药或甲状腺素药服用情况等;实验室检查结果,如C反应蛋白、白细胞计数、中性分叶核粒细胞计数、血清钙、皮质醇和促肾上腺皮质激素等。
资料收集前对相关人员进行统一培训,通过电子病历系统和发放纸质问卷的方式进行数据收集,共发放问卷450份,回收问卷445份,其中有效问卷436份,问卷有效回收率为96.89%。
运用R 4.4.1语言软件进行分析。符合正态分布的定量资料以均数±标准差(x±s)表示,组间比较采用两独立样本t检验;不符合正态分布的定量资料以中位数(四分位数)[M(P25,P75)]表示,组间比较采用Wilcoxon秩和检验;定性资料采用频数及百分比(%)表示,组间比较采用χ2检验、Fisher精确概率法。以LASSO回归分析和十折交叉验证筛选NS患儿AC发生影响因素中的显著变量,以显著变量为自变量,以是否发生AC为因变量,采用Logistic回归分析探讨NS患儿AC发生的影响因素。通过“rms”包绘制列线图。计算受试者工作特征(ROC)曲线下面积(AUC)、敏感度和特异度等指标评估模型效能。采用校准曲线评估模型校准能力。采用Hosmer-Lemeshow检验评估模型拟合优度,P>0.05即预测值与实际观测值间差异不明显,说明拟合良好。构建临床决策曲线评估模型临床应用价值,量化阈值概率范围内的净收益。在验证集中通过Bootstrap法(1 000次重复抽样)对模型进行内部验证。
436例NS患儿中,有60例(13.76%)患儿发生AC,其中,训练集305例患儿中有43例(14.10%)患儿发生AC,验证集131例NS患儿中有17例(12.98%)患儿发生AC。训练集根据患儿是否发生AC将其分为AC组和非AC组,并进行NS患儿AC发生影响因素的单因素分析,结果见表1。
将NS患儿AC发生影响因素单因素分析中P<0.1的16个变量进行LASSO回归分析和十折交叉验证。基于最小偏差对应的标准误lambda值(0.049),结果显示,激素效应类型、NS复发>3次、合并感染、AC史、不规范使用中药和激素减量过快或骤停6个影响因素具有显著特征(非零系数变量)。见图1、图2。
以患儿是否发生AC为因变量(非AC=0,AC=1),将LASSO回归筛选出的6个显著变量为自变量,采用多因素Logistic回归分析(逐步法),结果显示,激素效应类型、NS复发>3次、合并感染、AC史、不规范使用中药、激素减量过快或骤停为NS患儿AC发生的影响因素(P<0.05),见表2。
以Logistic回归分析结果为基础构建NS患儿AC发生风险预测模型:Logit(P)=-5.381-1.050×激素依赖+1.025×激素耐药+0.891×NS复发>3次+1.668×合并感染+2.420×AC史+1.393×不规范使用中药+1.536×激素减量过快或骤停。绘制可视化列线图,见图3。预测模型的训练集AUC为0.831,敏感度为0.767,特异度为0.756,Kappa值为0.343,约登指数为0.523,见图4。验证集AUC为0.855,敏感度为1.000,特异度为0.561,Kappa值为0.249,约登指数为0.561,见图5。
训练集和验证集的校准曲线和理想曲线接近,表明模型预测概率和实际发生率基本吻合,一致性良好。见图6和图7。训练集中Hosmer-Lemeshow检验结果显示,χ2=1.131(P=0.769),验证集中Hosmer-Lemeshow检验结果显示,χ2=7.271(P=0.064),表明预测模型具有良好的校准能力。构建临床决策曲线,结果显示,训练集和验证集中病人进行干预的净获益曲线均位于两条极端线之上,训练集的风险阈值概率为0.02~0.94,验证集的风险阈值概率为0.02~0.60,表明使用该模型比不使用模型能得到更高的净获益,模型具有良好的临床决策性能,临床实用性较好。见图8和图9。
本研究结果显示,436例NS患儿中发生AC的NS患儿为60例,AC发生率为13.76%,高于Bizzarri等[6]研究中关于成人肾上腺皮质功能不全病人的AC发生率(6%~8%)。这可能与本研究纳入的病人来自三级甲等综合医院,疑难病例相对较多有关;其次,由于患儿的AC表现特异性不明显,常易被误认为其他疾病,在基层医院可能存在诊断和治疗延误,具有地域性医疗条件差异,本研究病例来源医院具有较先进的医疗设施,专家资源相对丰富,利于及时给予明确诊断,AC诊出比例较高。故应明确NS患儿发生AC的影响因素,做到早预测和早干预,实施精准干预措施,有效预防患儿发生AC,减轻疾病负担,提高NS患儿生存质量。
本研究结果显示,激素耐药型NS患儿的AC发生风险是激素敏感型NS患儿的2.787倍,激素耐药是NS患儿AC发生的危险因素(P<0.05),与Krishna等[10]研究结果相似。提示护理人员需针对不同激素效应类型患儿建立风险分层模型,对激素耐药型人群进行主动防控,加强监测。
本研究结果显示,NS复发>3次的NS患儿AC发生风险是NS复发≤3次NS患儿的2.438倍,NS复发>3次是NS患儿AC发生的危险因素(P<0.05),与Eyal等[11]研究结果相似。Mantan等[4]研究也发现,与其他类型的NS相比,频繁NS复发个体的平均血清皮质醇水平(85.9 nmol/L)降低(P=0.05),AC发生风险较高。患儿NS复发表明其病情尚未得到控制,需要多次调整激素剂量,增加了肾上腺皮质功能不全风险,肾上腺皮质功能难以迅速恢复容易导致AC。胆固醇水平较高、反复感染、家属疾病护理知识水平较低是患儿复发NS的危险因素[12-13]。因此,对于NS复发患儿,临床医护人员需更重视其预防工作,定期评估肾上腺皮质功能,加强营养干预,强化健康宣教,严密监测管理,预防感染,提升患儿及家属对疾病的认知水平。
本研究结果显示,合并感染NS患儿的AC发生风险是未合并感染NS患儿的5.301倍,合并感染是NS患儿AC发生的危险因素(P<0.05)。周楠等[3]研究发现,感染会增加身体的应激需求,长期使用激素的NS患儿因处于肾上腺皮质功能不全状态而无法满足这种需求,容易导致AC发生。同时,感染也是患儿NS复发的常见因素[14]。因此,强化感染防控非常必要,大多数NS患儿免疫功能较低,居家时间较长,护理人员应指导家庭照顾者落实居家环境清洁、消毒和个人卫生管理等措施,提升患儿及家属防感染意识,从而减少NS患儿感染发生率,降低AC发生风险,提升NS患儿的健康水平和生活质量。
本研究结果显示,有AC史NS患儿的AC发生风险是无AC史NS患儿的11.249倍,AC史是NS患儿AC发生的危险因素(P<0.05),与Hahner等[15]对成年肾上腺功能不全病人的研究结果相似。有AC史表明患儿曾存在肾上腺皮质功能不全,再次发生AC的风险增加。因此,针对有AC史的NS患儿,医护人员需给予重视并做好预防管理。在患儿入院时通过询问病史进行AC风险评估,有AC病史者列为高危人群;住院时进行用药护理、营养管理等健康教育;出院前给予患儿及其家属避免NS复发的管理教育,提高对AC的认识。
本研究结果显示,不规范使用中药NS患儿的AC发生风险是规范使用中药NS患儿的4.028倍,不规范使用中药是NS患儿AC发生的危险因素(P<0.05)。目前,NS主要通过使用糖皮质激素和免疫抑制剂等西药治疗[16]。由于对中药认知能力不足等,很多家属会出现不规范使用中药的情况,进而可能影响患儿糖皮质激素代谢,导致激素水平波动,增加AC发生风险。研究表明,中药可能对肾脏产生毒性,导致肾损伤[17-18]。因此,护理人员需提高患儿及家属对不规范使用中药危害性的认识,提高其肾病治疗依从性,加强健康教育。
本研究结果显示,激素减量过快或骤停NS患儿的AC发生风险是无激素减量过快或骤停NS患儿的4.645倍,激素减量过快或骤停是NS患儿AC发生的危险因素(P<0.05)。已有研究表明,突然停止使用糖皮质激素或减量过快会导致肾上腺皮质功能无法及时恢复,身体对皮质醇的需求无法得到满足,从而引发AC。Ahmet等[19]研究发现,及时调整激素为晨起顿服,可最大限度减少对下丘脑-垂体-肾上腺(HPA)轴的抑制。患儿及家属的服药依从性低容易导致严重后果,尤其是在手术后或并发疾病期间,漏服或突然停止使用激素可能诱发AC[20-21]。有研究结果显示,服药依从性与年龄、母亲受教育程度和服用药物障碍有关[22]。因此,需要对幼年期被诊断为NS的青少年社会和心理需求给予关注、支持,提高患儿和家属服药依从性,降低AC发病率和死亡率,通过预防性服药管理降低NS患儿远期医疗成本。
本研究基于LASSO回归构建NS患儿AC发生风险预测模型,模型在训练集及验证集中的AUC均>0.8,说明模型具有良好的区分效能,可以精准识别发生AC的高危NS患儿;列线图可以将复杂数据可视化输出,便于临床应用,有利于指导临床护理人员及早制订精准化的干预方案,降低NS患儿AC发生风险,减轻慢性肾脏病患儿家庭的经济负担,降低社会医疗成本。
本研究构建的NS患儿AC发生风险预测模型具有较好的区分度、校准度和预测能力,以列线图方式呈现,简单易操作,能够在NS领域为儿科医护人员提供有效的AC风险评估工具。但本研究为单中心回顾性研究,样本代表性不足。未来研究可扩大样本量及覆盖范围,开展多中心纵向研究,进一步完善和优化模型。

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2026年第40卷第14期
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doi: 10.12102/j.issn.1009-6493.2026.14.006
  • 接收时间:2025-07-22
  • 首发时间:2026-10-30
  • 出版时间:2026-07-25
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  • 收稿日期:2025-07-22
  • 修回日期:2026-04-30
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    1中山大学附属第一医院,广东 510030
    2中山大学附属第一医院广西医院

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