Article(id=1199703042372235596, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1199703037368430831, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.1725.2023.0407, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1660579200000, receivedDateStr=2022-08-16, revisedDate=null, revisedDateStr=null, acceptedDate=1675785600000, acceptedDateStr=2023-02-08, onlineDate=1763961094706, onlineDateStr=2025-11-24, pubDate=1701100800000, pubDateStr=2023-11-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763961094706, onlineIssueDateStr=2025-11-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763961094706, creator=13701087609, updateTime=1763961094706, updator=13701087609, issue=Issue{id=1199703037368430831, tenantId=1146029695717560320, journalId=1189873630562394117, year='2023', volume='48', issue='11', pageStart='1237', pageEnd='1358', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763961093513, creator=13701087609, updateTime=1763961140964, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1199703236451070744, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1199703037368430831, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1199703236451070745, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1199703037368430831, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1328, endPage=1337, ext={EN=ArticleExt(id=1199703042951049569, articleId=1199703042372235596, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Construction and evaluation of risk prediction model for renal injury in tumor patients receiving PD-1 inhibitor treatment, columnId=1190310109000602400, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Clinical Research, runingTitle=null, highlight=null, articleAbstract=

Objective To explore the risk factors for renal injury in tumors patients treated with programmed death receptor-1 (PD-1) inhibitor, and further construct a column chart model to predict the likelihood of renal injury in patients. Methods The present study is a single center retrospective analysis. 447 patients with tumors treated with PD-1 inhibitors in the Third Affiliated Hospital of Soochow University between January 2018 and January 2021 were included and followed up until January 2022. Kidney injury was defined as acute kidney disease (AKD). All patients were divided into AKD group (n=71) and non-AKD group (n=376 according to whether PD-1 inhibitor associated with AKD development at the end of follow-up. Basic information, disease and medication situation, laboratory indicators, and the incidence of extrarenal immune related adverse events (irAEs) during follow-up period were compared between the two groups. Univariate and multivariate logistic regression models were used to identify independent risk factors for PD-1 inhibitor associated AKD. The present study randomly divided all samples (n=447) into training set (n=313) and validation set (n=134) in a 7:3 ratio, built nomogram prediction models in the training set according to the screened independent risk factors, drawn the receiver operating characteristic (ROC) curves to evaluate the discrimination of the models, drawn calibration curves to evaluate the calibration of the models, and drawn clinical decision curve analysis (DCA) to explore the clinical validity and benefit rate of the models. Results The combination of antibiotics, diabetes, hypertension, extrarenal irAEs and cystatin C (Cys C) in AKD group were significantly higher than those in non-AKD group (P<0.05), but hemoglobin (Hb) was significantly lower than that in non-AKD group (P<0.05). Single factor logistic regression analysis showed that combination of antibiotics, diabetes, hypertension, extrarenal irAEs, lower Hb, estimated glomerular filtration rate (eGFR), higher blood urea nitrogen (BUN), serum creatinine (SCr), Cys C, fasting blood glucose (FBG), and alanine transaminase (ALT) were risk factors for PD-1 inhibitor related AKD (P<0.05). Multivariate logistic regression analysis showed that concomitant extrarenal irAEs, lower Hb, higher SCr, and direct bilirubin (DBIL) were independent risk factors for PD-1 inhibitor associated AKD (P<0.05). Based on the independent risk factors mentioned above, a column chart prediction model was further established and validated. The results showed that the area under the ROC curve (AUC) of the training and validation sets of the model were 0.703 (95%CI 0.628-0.777) and 0.791 (95%CI 0.671-0.911), respectively, indicating good discrimination. The calibration curves of both the training and validation sets hover around the ideal line of 45°, indicating that the model has good calibration. DCA shows that the constructed model curve is far away from the two polar lines (the curve with a net benefit of 0 and the curve with all samples being positive), indicating that the model has good clinical benefits. Conclusion The combination of extrarenal irAEs, lower Hb, higher SCr, and higher DBIL are independent risk factors for the occurrence of PD-1 inhibitor related AKD; The established column chart model has good discrimination and calibration, which can provide guidance for clinical practice.

, correspAuthors=Min Yang, authorNote=null, correspAuthorsNote=
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目的 探讨接受程序性死亡受体-1(PD-1)抑制剂治疗的肿瘤患者发生肾损伤的危险因素,并进一步构建列线图模型预测患者发生肾损伤的可能性。方法 本研究为单中心回顾性分析。纳入苏州大学附属第三医院2018年1月-2021年1月使用PD-1抑制剂治疗的肿瘤患者447例,随访至2022年1月。肾损伤定义为急性肾脏疾病(AKD)。根据随访结束时是否发生PD-1抑制剂相关AKD将患者分为AKD组(n=71)与非AKD组(n=376)。比较两组患者的基本信息、疾病信息、用药情况、实验室指标,以及随访期间肾外免疫相关不良事件(irAEs)的发生情况。采用单因素及多因素logistic回归模型确定PD-1抑制剂相关AKD的独立危险因素。将所有患者按照7∶3的比例随机分成训练集(n=313)和验证集(n=134),根据筛选出的独立危险因素在训练集中建立列线图预测模型,通过受试者工作特征(ROC)曲线对模型的区分度进行评价,绘制校准曲线对模型的校准度进行评价,绘制临床决策曲线分析(DCA)探讨模型的临床有效性与获益率。结果 AKD组患者联用抗生素、合并糖尿病、高血压、肾外irAEs的比例及胱抑素C(Cys C)水平明显高于非AKD组(P<0.05),而血红蛋白(Hb)水平低于非AKD组(P<0.05)。单因素logistic回归分析显示,联用抗生素,合并糖尿病、高血压、肾外irAEs,较低的Hb、估算肾小球滤过率(eGFR)、较高的血尿素氮(BUN)、血清肌酐(SCr)、Cys C、空腹血糖(FBG)、谷丙转氨酶(ALT)是PD-1抑制剂相关AKD的危险因素(P<0.05)。多因素logistic回归分析显示,合并肾外irAEs、较低的Hb、较高的SCr及直接胆红素(DBIL)是PD-1抑制剂相关AKD的独立危险因素(P<0.05)。基于以上独立危险因素进一步建立列线图预测模型,并对该模型进行验证,结果显示该模型的训练集和验证集ROC曲线下面积(AUC)分别为0.703(95%CI 0.628~0.777)、0.791(95%CI 0.671~0.911),提示其具有良好的区分度。训练集和验证集的校准曲线均徘徊在45°的理想线附近,提示该模型具有良好的校准度。DCA显示构建的模型曲线远离两条极端线(净获益为0的曲线和所有样本都是阳性的曲线),提示该模型具有良好的临床效益。结论 合并肾外irAEs、较低的Hb、较高的SCr和较高的DBIL是PD-1抑制剂相关AKD的独立危险因素,据此建立的列线图模型具有较好的区分度和校准度,可为临床提供指导。

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杨敏,E-mail:
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卢珮宇,硕士研究生,主要从事免疫检查点抑制剂相关肾损伤方面的研究

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卢珮宇,硕士研究生,主要从事免疫检查点抑制剂相关肾损伤方面的研究

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Neonatal hyperbilirubinemia management: clinical assessment of bilirubin production[J]. Semin Perinatol, 2021, 45(1): 151351., articleTitle=Neonatal hyperbilirubinemia management: clinical assessment of bilirubin production, refAbstract=null), Reference(id=1199711042319253795, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, doi=null, pmid=null, pmcid=null, year=2015, volume=7, issue=8, pageStart=1385, pageEnd=1390, url=null, language=null, rfNumber=[29], rfOrder=32, authorNames=Zhao H, Pan X, Gong Z, journalName=J Thorac Dis, refType=null, unstructuredReference=Zhao H, Pan X, Gong Z, et al. Risk factors for acute kidney injury in overweight patients with acute type A aortic dissection: a retrospective study[J]. 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Kidney Int, 2019, 96(3): 555-567., articleTitle=Summary of the International Conference on Onco-Nephrology: an emerging field in medicine, refAbstract=null)], funds=[Fund(id=1199711037445472433, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, awardId=CE20215024, language=EN, fundingSource=Science and Technology Support (Social Development) Project of Bureau of Science and Technology of Changzhou(CE20215024), fundOrder=null, country=null), Fund(id=1199711037554524340, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, awardId=CE20215024, language=CN, fundingSource=常州市科技局科技支撑(社会发展)项目(CE20215024), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1199711030562620394, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, xref=null, ext=[AuthorCompanyExt(id=1199711030566814699, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, companyId=1199711030562620394, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Nephrology, the Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213004, China), AuthorCompanyExt(id=1199711030579397612, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, companyId=1199711030562620394, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=苏州大学附属第三医院肾内科,江苏常州 213004)])], figs=[ArticleFig(id=1199711034912112762, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=EN, label=Fig.1, caption=Nomogram model to predict risk of AKD in tumor patients receiving PD-1 inhibitor therapy, figureFileSmall=yEE8g/X8wVPmLtwkAtSbMA==, figureFileBig=pkW7AhfpSg3ym8nQQ6TWvw==, tableContent=null), ArticleFig(id=1199711035075690625, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=CN, label=图1, caption=接受PD-1抑制剂治疗的肿瘤患者AKD发生风险的列线图预测模型

PD-1. 程序性死亡受体-1;AKD. 急性肾脏疾病;Hb. 血红蛋白;SCr. 血清肌酐;DBIL. 直接胆红素;irAEs. 免疫相关不良事件

, figureFileSmall=yEE8g/X8wVPmLtwkAtSbMA==, figureFileBig=pkW7AhfpSg3ym8nQQ6TWvw==, tableContent=null), ArticleFig(id=1199711035218296969, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=EN, label=Fig.2, caption=ROC curves and AUC values of the nomogram prediction models for training and validation sets, figureFileSmall=wGFiJIDsSDd8AiC6Ty6yTg==, figureFileBig=WZlOxNLNmixuMIJalUOT9A==, tableContent=null), ArticleFig(id=1199711035335737485, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=CN, label=图2, caption=训练集和验证集中列线图模型的ROC曲线和AUC值

ROC. 受试者工作特征;AUC. 曲线下面积

, figureFileSmall=wGFiJIDsSDd8AiC6Ty6yTg==, figureFileBig=WZlOxNLNmixuMIJalUOT9A==, tableContent=null), ArticleFig(id=1199711035440595089, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=EN, label=Fig.3, caption=Calibration curves for training and validation sets, figureFileSmall=KCl9LGF4KcTZwPs9f9qXVA==, figureFileBig=xZQlQfrwbc7/DZFiX+EjuQ==, tableContent=null), ArticleFig(id=1199711035532869781, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=CN, label=图3, caption=训练集和验证集的校准曲线

AKD. 急性肾脏疾病

, figureFileSmall=KCl9LGF4KcTZwPs9f9qXVA==, figureFileBig=xZQlQfrwbc7/DZFiX+EjuQ==, tableContent=null), ArticleFig(id=1199711035646115993, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=EN, label=Fig.4, caption=Clinical decision curve analysis of the prediction model for training set nomogram, figureFileSmall=9FywYrN5eDdsozq4UfnS3w==, figureFileBig=/FYhfD7reGi99aLAHiEzfw==, tableContent=null), ArticleFig(id=1199711035767750812, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=CN, label=图4, caption=训练集中预测模型的临床决策曲线分析

All. 所有样本均为阳性的参考线;None. 净获益为0的参考线;DCA. 决策曲线分析;AKD. 急性肾脏疾病

, figureFileSmall=9FywYrN5eDdsozq4UfnS3w==, figureFileBig=/FYhfD7reGi99aLAHiEzfw==, tableContent=null), ArticleFig(id=1199711036883435679, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=EN, label=Tab.1, caption=

Comparison of baseline characteristics between the two groups of tumor patients

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 AKD组(n=71) 非AKD组(n=376) χ2/Z/t P
男/女(例) 53/18 272/104 0.160 0.689
年龄[岁, M(Q1, Q3)] 66.0(57.5, 70.0) 65.0(57.0, 69.5) -0.957 0.338
肿瘤类型[例(%)] 4.546 0.208
消化系统肿瘤 31(17.1) 150(82.9)
肺恶性肿瘤 28(13.5) 180(86.5)
黑色素瘤 5(33.3) 10(66.7)
其他 7(16.3) 36(83.7)
PD-1抑制剂[例(%)] 3.748 0.570
信迪利单抗 16(18.4) 71(81.6)
卡瑞利珠单抗 33(15.6) 179(84.4)
帕博利珠单抗 14(19.4) 58(80.6)
特瑞普利单抗 6(14.6) 35(85.4)
纳武利尤单抗 2(6.1) 31(93.9)
替雷利珠单抗 0 2(100.0)
用药史[例(%)]
化疗药物和(或)靶向药物 51(16.5) 259(83.5) 0.244 0.621
ACEI/ARB 9(18.0) 41(82.0) 0.189 0.664
PPIs 53(16.9) 261(83.1) 0.782 0.376
NSAIDs 31(18.6) 136(81.4) 1.432 0.231
抗生素 16(25.8) 46(74.2) 5.305 0.021
造影剂 34(15.0) 193(85.0) 0.283 0.595
疾病史[例(%)]
心脑血管病史 3(15.0) 17(85.0) 0.000 1.000
糖尿病 13(27.7) 34(72.3) 5.451 0.020
高血压 23(22.3) 80(77.7) 4.163 0.041
甲状腺功能亢进 1(100.0) 0 0.159
甲状腺功能减退 0 8(100.0) 0.366
肝硬化 4(25.0) 12(75.0) 0.446 0.504
吸烟[例(%)] 13(18.3) 58(81.7) 0.372 0.542
血压[mmHg, M(Q1, Q3)]
收缩压 126.0(120.0, 132.0) 125.0(119.0, 133.0) -1.093 0.275
舒张压 78.0(75.0, 80.0) 78.0(72.8, 81.0) -0.232 0.817
MAP 93.3(90.8, 97.2) 93.3(89.3, 97.3) -0.417 0.676
BMI(kg/m2, $\bar{x}±s$) 22.4±3.3 21.8±3.4 1.204 0.229
WBC[×109/L, M(Q1, Q3)] 6.1(4.4, 8.3) 6.0(4.7, 8.3) -0.392 0.695
Hb(g/L, $\bar{x}±s$) 114.9±21.4 122.2±19.3 -2.853 0.005
PLT[×109/L, M(Q1, Q3)] 209.0(142.5, 270.5) 214.0(154.0, 274.0) -0.834 0.404
NE[×109/L, M(Q1, Q3)] 4.3(2.8, 5.5) 4.0(3.0, 6.1) -0.271 0.786
Lym[×109/L, M(Q1, Q3)] 1.1(0.8, 1.6) 1.2(0.8, 1.6) -1.396 0.163
BUN[mmol/L, M(Q1, Q3)] 4.8(4.0, 6.0) 4.6(3.7, 5.8) -1.292 0.196
SCr[μmol/L, M(Q1, Q3)] 72.0(62.5, 86.5) 69.0(60.0, 78.2) -1.539 0.124
eGFR[ml/min, M(Q1, Q3)] 94.0(76.4, 104.3) 97.0(85.7, 106.5) -1.937 0.053
Cys C[mg/L, M(Q1, Q3)] 1.3(1.0, 1.5) 1.1(1.0, 1.3) -2.641 0.008
UA[μmol/L, M(Q1, Q3)] 309.4(239.6, 397.0) 300.6(237.1, 369.7) -0.994 0.320
血钾[mmol/L, M(Q1, Q3)] 4.0(3.7, 4.3) 4.0(3.8, 4.3) -1.003 0.316
血钠[mmol/L, M(Q1, Q3)] 138.6(137.1, 140.9) 139.1(137.1, 140.8) -0.567 0.571
血氯[mmol/L, M(Q1, Q3)] 102.8(101.5, 104.8) 102.9(100.9, 105.1) -0.236 0.813
血钙[mmol/L, M(Q1, Q3)] 2.3(2.3, 2.4) 2.3(2.2, 2.4) -0.865 0.387
血磷[mmol/L, M(Q1, Q3)] 1.2(1.0, 1.3) 1.2(1.1, 1.3) -0.555 0.579
CRP[mg/L, M(Q1, Q3)] 4.6(3.5, 39.5) 4.8(3.3, 24.4) -0.172 0.864
LDH[U/L, M(Q1, Q3)] 204.0(170.5, 296.0) 199.0(164.0, 250.5) -1.134 0.257
ALB[g/L, M(Q1, Q3)] 37.1(34.6, 41.2) 38.7(35.3, 42.1) -1.269 0.205
FBG[mmol/L, M(Q1, Q3)] 5.3(4.7, 6.2) 5.2(4.6, 5.8) -1.558 0.119
ALT[U/L, M(Q1, Q3)] 20.0(11.8, 35.0) 18.4(11.6, 32.0) -0.712 0.476
AST[U/L, M(Q1, Q3)] 25.5(18.0, 45.9) 24.7(18.0, 34.0) -1.008 0.313
TBIL [μmol/L, M(Q1, Q3)] 9.6(7.1, 14.1) 10.0(7.6, 13.4) -0.437 0.662
DBIL[μmol/L, M(Q1, Q3)] 3.5(2.3, 5.1) 3.4(2.6, 4.6) -0.113 0.910
CHOL[mmol/L, M(Q1, Q3)] 4.4(4.0, 5.0) 4.7(4.0, 5.3) -1.063 0.288
TG[mmol/L, M(Q1, Q3)] 1.1(0.8, 1.5) 1.2(0.8, 1.6) -0.822 0.411
合并肾外irAEs[例(%)] 64(90.1) 255(67.8) 14.562 <0.001
), ArticleFig(id=1199711037000876194, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=CN, label=表1, caption=

两组肿瘤患者基线特征比较

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 AKD组(n=71) 非AKD组(n=376) χ2/Z/t P
男/女(例) 53/18 272/104 0.160 0.689
年龄[岁, M(Q1, Q3)] 66.0(57.5, 70.0) 65.0(57.0, 69.5) -0.957 0.338
肿瘤类型[例(%)] 4.546 0.208
消化系统肿瘤 31(17.1) 150(82.9)
肺恶性肿瘤 28(13.5) 180(86.5)
黑色素瘤 5(33.3) 10(66.7)
其他 7(16.3) 36(83.7)
PD-1抑制剂[例(%)] 3.748 0.570
信迪利单抗 16(18.4) 71(81.6)
卡瑞利珠单抗 33(15.6) 179(84.4)
帕博利珠单抗 14(19.4) 58(80.6)
特瑞普利单抗 6(14.6) 35(85.4)
纳武利尤单抗 2(6.1) 31(93.9)
替雷利珠单抗 0 2(100.0)
用药史[例(%)]
化疗药物和(或)靶向药物 51(16.5) 259(83.5) 0.244 0.621
ACEI/ARB 9(18.0) 41(82.0) 0.189 0.664
PPIs 53(16.9) 261(83.1) 0.782 0.376
NSAIDs 31(18.6) 136(81.4) 1.432 0.231
抗生素 16(25.8) 46(74.2) 5.305 0.021
造影剂 34(15.0) 193(85.0) 0.283 0.595
疾病史[例(%)]
心脑血管病史 3(15.0) 17(85.0) 0.000 1.000
糖尿病 13(27.7) 34(72.3) 5.451 0.020
高血压 23(22.3) 80(77.7) 4.163 0.041
甲状腺功能亢进 1(100.0) 0 0.159
甲状腺功能减退 0 8(100.0) 0.366
肝硬化 4(25.0) 12(75.0) 0.446 0.504
吸烟[例(%)] 13(18.3) 58(81.7) 0.372 0.542
血压[mmHg, M(Q1, Q3)]
收缩压 126.0(120.0, 132.0) 125.0(119.0, 133.0) -1.093 0.275
舒张压 78.0(75.0, 80.0) 78.0(72.8, 81.0) -0.232 0.817
MAP 93.3(90.8, 97.2) 93.3(89.3, 97.3) -0.417 0.676
BMI(kg/m2, $\bar{x}±s$) 22.4±3.3 21.8±3.4 1.204 0.229
WBC[×109/L, M(Q1, Q3)] 6.1(4.4, 8.3) 6.0(4.7, 8.3) -0.392 0.695
Hb(g/L, $\bar{x}±s$) 114.9±21.4 122.2±19.3 -2.853 0.005
PLT[×109/L, M(Q1, Q3)] 209.0(142.5, 270.5) 214.0(154.0, 274.0) -0.834 0.404
NE[×109/L, M(Q1, Q3)] 4.3(2.8, 5.5) 4.0(3.0, 6.1) -0.271 0.786
Lym[×109/L, M(Q1, Q3)] 1.1(0.8, 1.6) 1.2(0.8, 1.6) -1.396 0.163
BUN[mmol/L, M(Q1, Q3)] 4.8(4.0, 6.0) 4.6(3.7, 5.8) -1.292 0.196
SCr[μmol/L, M(Q1, Q3)] 72.0(62.5, 86.5) 69.0(60.0, 78.2) -1.539 0.124
eGFR[ml/min, M(Q1, Q3)] 94.0(76.4, 104.3) 97.0(85.7, 106.5) -1.937 0.053
Cys C[mg/L, M(Q1, Q3)] 1.3(1.0, 1.5) 1.1(1.0, 1.3) -2.641 0.008
UA[μmol/L, M(Q1, Q3)] 309.4(239.6, 397.0) 300.6(237.1, 369.7) -0.994 0.320
血钾[mmol/L, M(Q1, Q3)] 4.0(3.7, 4.3) 4.0(3.8, 4.3) -1.003 0.316
血钠[mmol/L, M(Q1, Q3)] 138.6(137.1, 140.9) 139.1(137.1, 140.8) -0.567 0.571
血氯[mmol/L, M(Q1, Q3)] 102.8(101.5, 104.8) 102.9(100.9, 105.1) -0.236 0.813
血钙[mmol/L, M(Q1, Q3)] 2.3(2.3, 2.4) 2.3(2.2, 2.4) -0.865 0.387
血磷[mmol/L, M(Q1, Q3)] 1.2(1.0, 1.3) 1.2(1.1, 1.3) -0.555 0.579
CRP[mg/L, M(Q1, Q3)] 4.6(3.5, 39.5) 4.8(3.3, 24.4) -0.172 0.864
LDH[U/L, M(Q1, Q3)] 204.0(170.5, 296.0) 199.0(164.0, 250.5) -1.134 0.257
ALB[g/L, M(Q1, Q3)] 37.1(34.6, 41.2) 38.7(35.3, 42.1) -1.269 0.205
FBG[mmol/L, M(Q1, Q3)] 5.3(4.7, 6.2) 5.2(4.6, 5.8) -1.558 0.119
ALT[U/L, M(Q1, Q3)] 20.0(11.8, 35.0) 18.4(11.6, 32.0) -0.712 0.476
AST[U/L, M(Q1, Q3)] 25.5(18.0, 45.9) 24.7(18.0, 34.0) -1.008 0.313
TBIL [μmol/L, M(Q1, Q3)] 9.6(7.1, 14.1) 10.0(7.6, 13.4) -0.437 0.662
DBIL[μmol/L, M(Q1, Q3)] 3.5(2.3, 5.1) 3.4(2.6, 4.6) -0.113 0.910
CHOL[mmol/L, M(Q1, Q3)] 4.4(4.0, 5.0) 4.7(4.0, 5.3) -1.063 0.288
TG[mmol/L, M(Q1, Q3)] 1.1(0.8, 1.5) 1.2(0.8, 1.6) -0.822 0.411
合并肾外irAEs[例(%)] 64(90.1) 255(67.8) 14.562 <0.001
), ArticleFig(id=1199711037114122407, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=EN, label=Tab.2, caption=

Univariate logistic regression analysis of PD-1 inhibitor related AKD

, figureFileSmall=null, figureFileBig=null, tableContent=
危险因素 单因素logistic回归 多因素logistic回归
OR(95%CI) P OR(95%CI) P
性别 1.126(0.630~2.012) 0.689 1.110(0.554~2.224) 0.786
年龄 1.015(0.989~1.041) 0.274 1.014(0.985~1.044) 0.350
肿瘤类型
消化系统肿瘤 1(参考值) 1(参考值)
肺恶性肿瘤 0.753(0.432~1.311) 0.316 0.695(0.365~1.325) 0.269
黑色素瘤 2.419(0.773~7.573) 0.129 2.310(0.610~8.752) 0.218
其他 0.941(0.384~2.308) 0.894 1.205(0.437~3.324) 0.719
PD-1抑制剂种类(相较于信迪利单抗)
卡瑞利珠单抗 0.818(0.424~1.579) 0.549
帕博利珠单抗 1.071(0.483~2.376) 0.866
特瑞普利单抗 0.761(0.274~2.114) 0.600
纳武利尤单抗 0.286(0.062~1.321) 0.109
替雷利珠单抗 0 0.999
用药史
化疗和(或)靶向药 1.152(0.657~2.019) 0.621
ACEI/ARB 1.186(0.549~2.563) 0.664
PPIs 1.297(0.728~2.312) 0.377
NSAIDs 1.368(0.818~2.287) 0.233
抗生素 2.087(1.104~3.944) 0.023 1.728(0.838~3.565) 0.138
造影剂 0.871(0.524~1.448) 0.595
疾病史
心脑血管意外 0.932(0.266~3.266) 0.912
糖尿病 2.255(1.123~4.527) 0.022 1.659(0.712~3.863) 0.241
高血压 1.773(1.018~3.088) 0.043 1.295(0.654~2.565) 0.459
肝硬化 1.811(0.567~5.784) 0.316
吸烟 1.229(0.633~2.386) 0.543
血压
收缩压 1.007(0.989~1.025) 0.477
舒张压 1.001(0.971~1.032) 0.955
MAP 1.006(0.977~1.035) 0.683
BMI 1.047(0.972~1.128) 0.229
WBC 1.022(0.961~1.088) 0.487
Hb 0.981(0.968~0.994) 0.005 0.977(0.963~0.992) 0.002
PLT 1.000(0.997~1.002) 0.769
NE 1.022(0.950~1.099) 0.566
Lym 0.712(0.460~1.103) 0.128
BUN 1.172(1.027~1.337) 0.018
SCr 1.020(1.006~1.035) 0.006 1.018(1.001~1.036) 0.043
eGFR 0.981(0.967~0.995) 0.007
Cys C 6.617(2.134~20.521) 0.001
UA 1.002(1.000~1.005) 0.074
血钾 0.719(0.402~1.286) 0.266
血钠 1.001(0.934~1.071) 0.988
血氯 1.012(0.946~1.083) 0.721
血钙 1.384(0.250~7.650) 0.709
血磷 0.737(0.232~2.345) 0.605
CRP 1.002(0.995~1.010) 0.542
LDH 1.001(1.000~1.002) 0.112
ALB 0.973(0.925~1.022) 0.275
FBG 1.184(1.025~1.368) 0.021
ALT 1.007(1.001~1.013) 0.030 1.004(0.996~1.012) 0.294
AST 1.005(0.999~1.012) 0.106
TBIL 1.010(0.993~1.026) 0.246
DBIL 1.019(0.999~1.040) 0.067 1.037(1.002~1.073) 0.036
CHOL 0.854(0.608~1.199) 0.362
TG 0.710(0.416~1.211) 0.209
合并肾外irAEs 4.338(1.931~9.749) <0.001 6.423(2.477~16.656) <0.001
), ArticleFig(id=1199711037231562923, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1199703042372235596, language=CN, label=表2, caption=

PD-1抑制剂相关AKD的单因素及多因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
危险因素 单因素logistic回归 多因素logistic回归
OR(95%CI) P OR(95%CI) P
性别 1.126(0.630~2.012) 0.689 1.110(0.554~2.224) 0.786
年龄 1.015(0.989~1.041) 0.274 1.014(0.985~1.044) 0.350
肿瘤类型
消化系统肿瘤 1(参考值) 1(参考值)
肺恶性肿瘤 0.753(0.432~1.311) 0.316 0.695(0.365~1.325) 0.269
黑色素瘤 2.419(0.773~7.573) 0.129 2.310(0.610~8.752) 0.218
其他 0.941(0.384~2.308) 0.894 1.205(0.437~3.324) 0.719
PD-1抑制剂种类(相较于信迪利单抗)
卡瑞利珠单抗 0.818(0.424~1.579) 0.549
帕博利珠单抗 1.071(0.483~2.376) 0.866
特瑞普利单抗 0.761(0.274~2.114) 0.600
纳武利尤单抗 0.286(0.062~1.321) 0.109
替雷利珠单抗 0 0.999
用药史
化疗和(或)靶向药 1.152(0.657~2.019) 0.621
ACEI/ARB 1.186(0.549~2.563) 0.664
PPIs 1.297(0.728~2.312) 0.377
NSAIDs 1.368(0.818~2.287) 0.233
抗生素 2.087(1.104~3.944) 0.023 1.728(0.838~3.565) 0.138
造影剂 0.871(0.524~1.448) 0.595
疾病史
心脑血管意外 0.932(0.266~3.266) 0.912
糖尿病 2.255(1.123~4.527) 0.022 1.659(0.712~3.863) 0.241
高血压 1.773(1.018~3.088) 0.043 1.295(0.654~2.565) 0.459
肝硬化 1.811(0.567~5.784) 0.316
吸烟 1.229(0.633~2.386) 0.543
血压
收缩压 1.007(0.989~1.025) 0.477
舒张压 1.001(0.971~1.032) 0.955
MAP 1.006(0.977~1.035) 0.683
BMI 1.047(0.972~1.128) 0.229
WBC 1.022(0.961~1.088) 0.487
Hb 0.981(0.968~0.994) 0.005 0.977(0.963~0.992) 0.002
PLT 1.000(0.997~1.002) 0.769
NE 1.022(0.950~1.099) 0.566
Lym 0.712(0.460~1.103) 0.128
BUN 1.172(1.027~1.337) 0.018
SCr 1.020(1.006~1.035) 0.006 1.018(1.001~1.036) 0.043
eGFR 0.981(0.967~0.995) 0.007
Cys C 6.617(2.134~20.521) 0.001
UA 1.002(1.000~1.005) 0.074
血钾 0.719(0.402~1.286) 0.266
血钠 1.001(0.934~1.071) 0.988
血氯 1.012(0.946~1.083) 0.721
血钙 1.384(0.250~7.650) 0.709
血磷 0.737(0.232~2.345) 0.605
CRP 1.002(0.995~1.010) 0.542
LDH 1.001(1.000~1.002) 0.112
ALB 0.973(0.925~1.022) 0.275
FBG 1.184(1.025~1.368) 0.021
ALT 1.007(1.001~1.013) 0.030 1.004(0.996~1.012) 0.294
AST 1.005(0.999~1.012) 0.106
TBIL 1.010(0.993~1.026) 0.246
DBIL 1.019(0.999~1.040) 0.067 1.037(1.002~1.073) 0.036
CHOL 0.854(0.608~1.199) 0.362
TG 0.710(0.416~1.211) 0.209
合并肾外irAEs 4.338(1.931~9.749) <0.001 6.423(2.477~16.656) <0.001
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接受PD-1抑制剂治疗的肿瘤患者发生肾损伤的风险预测模型构建与评价
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卢珮宇 , 杨艳 , 周华 , 杨碧霞 , 杨敏 *
解放军医学杂志 | 临床研究 2023,48(11): 1328-1337
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解放军医学杂志 | 临床研究 2023, 48(11): 1328-1337
接受PD-1抑制剂治疗的肿瘤患者发生肾损伤的风险预测模型构建与评价
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卢珮宇, 杨艳, 周华, 杨碧霞, 杨敏*
作者信息
  • 苏州大学附属第三医院肾内科,江苏常州 213004
  • 卢珮宇,硕士研究生,主要从事免疫检查点抑制剂相关肾损伤方面的研究

通讯作者:

杨敏,E-mail:
Construction and evaluation of risk prediction model for renal injury in tumor patients receiving PD-1 inhibitor treatment
Pei-Yu Lu, Yan Yang, Hua Zhou, Bi-Xia Yang, Min Yang*
Affiliations
  • Department of Nephrology, the Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213004, China
出版时间: 2023-11-28 doi: 10.11855/j.issn.0577-7402.1725.2023.0407
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目的 探讨接受程序性死亡受体-1(PD-1)抑制剂治疗的肿瘤患者发生肾损伤的危险因素,并进一步构建列线图模型预测患者发生肾损伤的可能性。方法 本研究为单中心回顾性分析。纳入苏州大学附属第三医院2018年1月-2021年1月使用PD-1抑制剂治疗的肿瘤患者447例,随访至2022年1月。肾损伤定义为急性肾脏疾病(AKD)。根据随访结束时是否发生PD-1抑制剂相关AKD将患者分为AKD组(n=71)与非AKD组(n=376)。比较两组患者的基本信息、疾病信息、用药情况、实验室指标,以及随访期间肾外免疫相关不良事件(irAEs)的发生情况。采用单因素及多因素logistic回归模型确定PD-1抑制剂相关AKD的独立危险因素。将所有患者按照7∶3的比例随机分成训练集(n=313)和验证集(n=134),根据筛选出的独立危险因素在训练集中建立列线图预测模型,通过受试者工作特征(ROC)曲线对模型的区分度进行评价,绘制校准曲线对模型的校准度进行评价,绘制临床决策曲线分析(DCA)探讨模型的临床有效性与获益率。结果 AKD组患者联用抗生素、合并糖尿病、高血压、肾外irAEs的比例及胱抑素C(Cys C)水平明显高于非AKD组(P<0.05),而血红蛋白(Hb)水平低于非AKD组(P<0.05)。单因素logistic回归分析显示,联用抗生素,合并糖尿病、高血压、肾外irAEs,较低的Hb、估算肾小球滤过率(eGFR)、较高的血尿素氮(BUN)、血清肌酐(SCr)、Cys C、空腹血糖(FBG)、谷丙转氨酶(ALT)是PD-1抑制剂相关AKD的危险因素(P<0.05)。多因素logistic回归分析显示,合并肾外irAEs、较低的Hb、较高的SCr及直接胆红素(DBIL)是PD-1抑制剂相关AKD的独立危险因素(P<0.05)。基于以上独立危险因素进一步建立列线图预测模型,并对该模型进行验证,结果显示该模型的训练集和验证集ROC曲线下面积(AUC)分别为0.703(95%CI 0.628~0.777)、0.791(95%CI 0.671~0.911),提示其具有良好的区分度。训练集和验证集的校准曲线均徘徊在45°的理想线附近,提示该模型具有良好的校准度。DCA显示构建的模型曲线远离两条极端线(净获益为0的曲线和所有样本都是阳性的曲线),提示该模型具有良好的临床效益。结论 合并肾外irAEs、较低的Hb、较高的SCr和较高的DBIL是PD-1抑制剂相关AKD的独立危险因素,据此建立的列线图模型具有较好的区分度和校准度,可为临床提供指导。

程序性死亡受体-1抑制剂  /  肾损伤  /  发病率  /  危险因素  /  Nomogram模型

Objective To explore the risk factors for renal injury in tumors patients treated with programmed death receptor-1 (PD-1) inhibitor, and further construct a column chart model to predict the likelihood of renal injury in patients. Methods The present study is a single center retrospective analysis. 447 patients with tumors treated with PD-1 inhibitors in the Third Affiliated Hospital of Soochow University between January 2018 and January 2021 were included and followed up until January 2022. Kidney injury was defined as acute kidney disease (AKD). All patients were divided into AKD group (n=71) and non-AKD group (n=376 according to whether PD-1 inhibitor associated with AKD development at the end of follow-up. Basic information, disease and medication situation, laboratory indicators, and the incidence of extrarenal immune related adverse events (irAEs) during follow-up period were compared between the two groups. Univariate and multivariate logistic regression models were used to identify independent risk factors for PD-1 inhibitor associated AKD. The present study randomly divided all samples (n=447) into training set (n=313) and validation set (n=134) in a 7:3 ratio, built nomogram prediction models in the training set according to the screened independent risk factors, drawn the receiver operating characteristic (ROC) curves to evaluate the discrimination of the models, drawn calibration curves to evaluate the calibration of the models, and drawn clinical decision curve analysis (DCA) to explore the clinical validity and benefit rate of the models. Results The combination of antibiotics, diabetes, hypertension, extrarenal irAEs and cystatin C (Cys C) in AKD group were significantly higher than those in non-AKD group (P<0.05), but hemoglobin (Hb) was significantly lower than that in non-AKD group (P<0.05). Single factor logistic regression analysis showed that combination of antibiotics, diabetes, hypertension, extrarenal irAEs, lower Hb, estimated glomerular filtration rate (eGFR), higher blood urea nitrogen (BUN), serum creatinine (SCr), Cys C, fasting blood glucose (FBG), and alanine transaminase (ALT) were risk factors for PD-1 inhibitor related AKD (P<0.05). Multivariate logistic regression analysis showed that concomitant extrarenal irAEs, lower Hb, higher SCr, and direct bilirubin (DBIL) were independent risk factors for PD-1 inhibitor associated AKD (P<0.05). Based on the independent risk factors mentioned above, a column chart prediction model was further established and validated. The results showed that the area under the ROC curve (AUC) of the training and validation sets of the model were 0.703 (95%CI 0.628-0.777) and 0.791 (95%CI 0.671-0.911), respectively, indicating good discrimination. The calibration curves of both the training and validation sets hover around the ideal line of 45°, indicating that the model has good calibration. DCA shows that the constructed model curve is far away from the two polar lines (the curve with a net benefit of 0 and the curve with all samples being positive), indicating that the model has good clinical benefits. Conclusion The combination of extrarenal irAEs, lower Hb, higher SCr, and higher DBIL are independent risk factors for the occurrence of PD-1 inhibitor related AKD; The established column chart model has good discrimination and calibration, which can provide guidance for clinical practice.

programmed cell death protein-1 inhibitors  /  kidney injury  /  incidence rate  /  risk factors  /  nomogram model
卢珮宇, 杨艳, 周华, 杨碧霞, 杨敏. 接受PD-1抑制剂治疗的肿瘤患者发生肾损伤的风险预测模型构建与评价. 解放军医学杂志, 2023 , 48 (11) : 1328 -1337 . DOI: 10.11855/j.issn.0577-7402.1725.2023.0407
Pei-Yu Lu, Yan Yang, Hua Zhou, Bi-Xia Yang, Min Yang. Construction and evaluation of risk prediction model for renal injury in tumor patients receiving PD-1 inhibitor treatment[J]. Medical Journal of Chinese People’s Liberation Army, 2023 , 48 (11) : 1328 -1337 . DOI: 10.11855/j.issn.0577-7402.1725.2023.0407
近年来癌症的发病率逐渐增高,严重威胁着人们的生命安全,而各种新型癌症治疗方法也逐渐发展起来[1]。程序性死亡受体-1(programmed cell death protein-1,PD-1)抑制剂是一类新型抗肿瘤药物,属于免疫检查点抑制剂(immune checkpoint inhibitors,ICIs),已成为目前使用最广泛的抗肿瘤药物之一[2-3],可明显改善肿瘤患者的预后[4-5]。PD-1通常表达于激活的T、B细胞中,是一种抑制性信号通路分子,能够维持自身免疫系统的稳定,但肿瘤微环境可诱导浸润的T细胞高表达PD-1,从而抑制T细胞活化,促进肿瘤的发生[6-9]。PD-1抑制剂可通过靶向PD-1并与之结合,阻断抑制信号的传递,从而恢复T细胞的活性,增强抗肿瘤免疫反应。然而,在发挥强效抗肿瘤效应的同时,PD-1抑制剂也带来了一种被称为免疫相关不良事件(immune-related adverse reactions,irAEs)的独特的自身免疫现象[10]。irAEs几乎可影响机体的所有器官,其中最常受累的器官是皮肤、胃肠道、内分泌腺和肝脏[11-12]。在既往研究中,ICIs相关急性肾损伤(acute kidney injury,AKI)的发生率为1.4%~4.9%[13],但随着用药率的增高,新近研究中AKI的发生率已上升至13%~29%[14]。ICIs引起的AKI主要表现为急性小管间质性肾炎(acute tubulointerstitial nephritis,ATIN)[13,15],也有少量表现为肾小球疾病、血栓性微血管病等的报道[13,16]。目前对肾损伤的研究多为病例报道和小型单中心病例对照研究,且大多侧重于探讨其影响因素,尚无关于建立预测接受PD-1抑制剂治疗的肿瘤患者发生肾损伤风险的科学模型研究。本研究在单因素和多因素logistic回归分析的基础上,建立了接受PD-1抑制剂治疗的肿瘤患者发生肾损伤风险的列线图预测模型,以期为预防PD-1抑制剂相关肾损伤提供依据。
收集2018年1月-2021年1月在苏州大学附属第三医院接受PD-1抑制剂(单用或联用其他ICIs)治疗的所有恶性肿瘤患者的临床资料进行回顾性分析。共筛选出447例患者,随访时间截至2022年1月。患者中位年龄为65(57,70)岁,其中男325例(72.7%),女122例(27.3%)。447例中,71例(15.9%)出现急性肾脏疾病(acute kidney diseases and disorders,AKD),根据是否发生PD-1抑制剂相关AKD将所有患者分为AKD组(n=71)与非AKD组(n=376)。纳入标准:(1)2018年1月-2021年1月在苏州大学附属第三医院住院的恶性肿瘤患者;(2)接受PD-1抑制剂治疗;(3) 年龄≥18岁。排除标准:(1)存在自身免疫性疾病如系统性红斑狼疮、干燥综合征、类风湿关节炎等;(2)患泌尿系统恶性肿瘤、终末期肾脏病或既往行肾移植手术;(3)初次用药后存活时间<1周;(4)临床资料不完整。本研究已获苏州大学附属第三医院伦理审查委员会审批[2022(教)CL033-01号]。
自苏州大学附属第三医院信息系统调取2018年1月-2021年1月全院输液记录中使用PD-1抑制剂的肿瘤患者的临床资料信息。收集的基线数据包括:(1)基本信息。姓名、年龄、性别、身高、体重、体重指数(body mass index,BMI)、血压、平均动脉压(mean artery pressure,MAP)。(2)疾病信息。肿瘤类型、疾病史(包括心脑血管疾病、糖尿病、高血压、甲状腺功能亢进、甲状腺功能减退、肝硬化)、吸烟史。(3)用药信息。PD-1抑制剂种类、治疗期间是否联合应用其他肾毒性药物[化疗药物、靶向治疗药物、质子泵抑制剂(proton pump inhibitors,PPIs)、血管紧张素转换酶抑制剂(angiotensin converting enzyme inhibitors,ACEI)/血管紧张素Ⅱ受体拮抗剂(angiotensin receptor blocker Ⅱ,ARB)、非甾体抗炎药(non-steroidal anti-inflammatory drugs,NSAIDs)、抗生素、造影剂等]。(4)实验室指标。血白细胞计数(WBC)、血红蛋白(Hb)、血小板计数(PLT)、中性粒细胞计数(NE)、淋巴细胞计数(Lym)、血尿素氮(BUN)、血清肌酐(SCr)、估算肾小球滤过率(eGFR,采用eGFR-EPI公式计算)、胱抑素C(Cys C)、尿酸(UA)、血钾、血钠、血氯、血钙、血磷、C反应蛋白(CRP)、乳酸脱氢酶(LDH)、血清白蛋白(ALB)、空腹血糖(FBG)、谷丙转氨酶(ALT)、谷草转氨酶(AST)、总胆红素(TBIL)、直接胆红素(DBIL)、胆固醇(CHOL)、三酰甘油(TG)。基线数据为患者首次使用PD-1抑制剂前3个月内最接近用药日期录得的数据。随访内容包括随访期间患者是否发生AKD、AKD的严重程度及临床表现,以及随访期间发生的所有肾外irAEs及其发生日期、发生肾外irAEs时的同期实验室数据。
肾损伤定义为AKD。AKD的诊断标准采用改善全球肾脏病组织(KDIGO)-AKD标准,即肾损伤发生在3个月内,包括AKI(7 d内SCr升高≥50%,或48 h内SCr升高≥26.5μmol/L,或少尿),或eGFR<60 ml/(min.1.73m2)未满3个月,或eGFR下降≥35%未满3个月,或SCr上升>50%未满3个月。
比较两组患者的基本信息、疾病信息、用药情况、实验室指标,以及随访期间肾外irAEs的发生情况。采用单因素和多因素logistic回归分析筛选PD-1抑制剂相关AKD的独立危险因素。将所有样本按照7:3的比例随机分为训练集(n=313)和验证集(n=134),基于筛选出的独立危险因素及专家建议在训练集中构建列线图模型,在训练集和验证集中分别绘制受试者工作特征(ROC)曲线,计算ROC曲线下面积(AUC)值来评估列线图的区分度,绘制列线图模型的校准曲线来评估模型预测结果与实际发生率的一致性,采用临床决策曲线分析(DCA)探讨模型的临床有效性与获益率。
采用Excel软件进行数据录入和整理,采用SPSS 25.0软件和R 4.2.2软件进行统计分析。计量资料应用Shapiro-Wilk检验确定是否服从正态分布,服从正态分布者以$\bar{x}±s$表示,两组间比较采用独立样本t检验,偏态分布者以M(Q1,Q3)表示,两组间比较采用Mann-Whitney U检验。计数资料以例(%)表示,组间比较采用χ2检验或Fisher确切概率法。采用R 4.2.2软件进行列线图模型的构建与评价,通过“rms”软件包构建列线图预测模型并绘制校准曲线,使用“pROC”软件包绘制ROC曲线并计算AUC,使用“rmda”及“ggDCA”软件包绘制DCA曲线。P<0.05为差异有统计学意义。
447例患者中,肺恶性肿瘤208例(46.5%),消化系统肿瘤181例(40.5%),黑色素瘤15例(3.4%),其他肿瘤43例(9.6%);310例(69.4%)患者在使用PD-1抑制剂的同时合并使用了化疗药和(或)靶向药。其他可能导致肾损伤的合并用药中最常见的是质子泵抑制剂,共314例(70.2%)。AKD组与非AKD组患者在性别、年龄、肿瘤类型、使用PD-1抑制剂种类、合并使用化疗药物和(或)靶向药物等方面差异均无统计学意义(P>0.05);AKD组患者联用抗生素、糖尿病、高血压、合并肾外irAEs的比例及Cys C水平明显高于非AKD组(P<0.05),而Hb水平明显低于非AKD组(P<0.05)(表1)。
发生PD-1抑制剂相关AKD的中位时间为150(41,302) d,有12例(16.9%)在用药开始后1年以上发生AKD。71例AKD中,有64例(90.1%)同时合并了肾外irAEs,其中发生率较高的有肝损伤(32例,45.1%)、糖尿病(25例,35.2%)、血液系统损伤(23例,32.4%)、肺损伤(18例,25.4%)和甲状腺功能异常(18例,25.4%)。此外,在这71例AKD患者中,29例(40.8%)同时出现尿隐血(+)及以上,23例(32.4%)同时出现脓尿(≥5个白细胞/高倍视野),29例(40.8%)同时出现尿蛋白(+)及以上,38例(53.5%)同时出现管型尿。
单因素logistic回归分析显示,联用抗生素,合并糖尿病、高血压、肾外irAEs,较低的Hb、eGFR,较高的BUN、SCr、Cys C、FBG、ALT是PD-1抑制剂相关AKD的危险因素(P<0.05)。结合单因素分析中P<0.1的变量及国内外文献与专家意见,将年龄、性别、肿瘤类型、联用抗生素,合并糖尿病、高血压、肾外irAEs及Hb、SCr、ALT、DBIL纳入多因素logistic回归分析,结果显示,较低的Hb,较高的SCr、DBIL及合并肾外irAEs是PD-1抑制剂相关AKD的独立危险因素(P<0.05)(表2)。
基于多因素logistic回归分析筛选出的独立危险因素及专家意见,将性别、Hb、SCr、DBIL、合并肾外irAEs纳入预测模型的构建,在训练集中建立了接受PD-1抑制剂治疗的肿瘤患者AKD发生风险的列线图预测模型(图1)。
在训练集与验证集中分别评价所建立的列线图模型的预测性能,绘制ROC曲线并计算AUC,结果显示,在训练集中模型的AUC为0.703(95%CI 0.628~0.777),验证集中模型的AUC为0.791(95%CI 0.671~0.911),表明该列线图模型具有良好的预测能力(图2)。在训练集和验证集中分别绘制校准曲线,结果显示在两个数据集中AKD的预测概率与实际发生率十分接近,均徘徊在45°的理想线附近,提示预测模型的校准度良好(图3)。DCA分析结果显示,模型曲线远离两条极端线(净获益为0的参考线和所有样本均为阳性的参考线),提示构建的列线图模型具有良好的临床应用价值(图4)。
PD-1抑制剂的出现使很多肿瘤患者获益,但与此同时,PD-1抑制剂相关irAEs的报道也越来越多,其中肾损伤的发生率虽然相对较低,但仍有可能对患者的生命造成威胁,或影响患者的抗肿瘤治疗方案及长期生活质量[17-19]。因此,如何评估患者使用PD-1抑制剂后发生肾损伤的风险,以及对肾损伤进行早期诊断和治疗极其重要。相较于既往报道,本研究将肾损伤范围从AKI扩展到了AKD,并探讨了PD-1抑制剂相关AKD的临床特点和独立危险因素,同时进一步建立了接受PD-1抑制剂治疗的肿瘤患者AKD发生风险的列线图预测模型。
既往ICIs相关肾损伤的研究主要集中于AKI,ICIs相关AKI的发生率相差较大,为2%~18%[20-22],PD-1抑制剂相关AKI的发生率为1.4%~1.9%[13],而本研究将肾损伤的范围扩大至AKD后,AKD的发生率(15.9%)较既往文献明显增高。患者用药后发生AKD的时间差异性较大,中位时间为150(41,302) d,与既往研究[23]一致,提示肾损伤的出现时间可能较晚,临床医师应时刻保持警惕。
本研究发现,合并肾外irAEs、较低的Hb、较高的SCr和DBIL是PD-1抑制剂相关AKD的独立危险因素。既往有多项研究对ICIs相关AKI的危险因素进行了分析。Cortazar等[24]发现,较低的基线eGFR、联用PPIs及ICIs治疗是ICIs相关AKI的独立危险因素;Gupta等[23]发现,基线肾功能、联用PPIs及合并肾外irAEs是ICIs相关AKI的独立危险因素;Meraz-Muñoz等[21]发现,合并肾外irAEs、高血压与ICIs相关AKI风险增加相关。本研究与既往研究的结果相似,并对其进行了扩展。较高的基线SCr水平是公认的肾损伤危险因素。目前尚不能明确在基线SCr较高的患者中PD-1抑制剂对肾脏的免疫损伤风险是否增高,因其肾损伤可能是肾功能储备减少而非免疫损伤所致。但即使不能确定,在PD-1抑制剂使用前仍应关注患者的基线SCr水平,并在使用过程中密切监测SCr的变化情况。贫血是肾损伤发生的常见原因之一,肾脏对缺血缺氧尤其敏感,急性失血、血容量减少、血液稀释等原因所致的缺血缺氧均可能引起肾损伤,故贫血患者应谨慎使用PD-1抑制剂。
基线DBIL水平与肾脏损伤的关系在既往同类型研究中未见报道。胆红素是一种内源性抗氧化剂,在生理浓度范围内可发挥抑制炎症反应、减少氧化应激等作用[25-26]。然而,当胆红素浓度超过生理范围时会对各个器官带来不同程度的损害,包括肾脏损伤,后者在病理上被称为“胆红素肾病”[27]。目前关于高胆红素血症对肾脏影响的研究较少,主要集中在新生儿高胆红素血症相关肾损伤[27-28]及心脏手术后高胆红素血症并发AKI[29]等方面。高胆红素血症引发肾损伤的可能机制包括:(1)胆红素可在肾小球和肾小管内沉积并形成结晶,造成管腔阻塞,从而使肾血流量减少,引发肾前性肾损伤[30];(2)未结合胆红素为脂溶性,可通过细胞膜进入肾小管细胞内并沉积于线粒体,从而影响细胞代谢及肾功能[31-32];(3)高胆红素血症可能通过产生自由基、增强氧化应激的方式造成肾损伤[33]
本研究中319例(90.1%)患者在用药过程中出现了肾外irAEs,且与肾损伤的相关度较高。因此,当患者使用PD-1抑制剂后出现肾外irAEs时,应高度警惕肾损伤的发生。同时,出现肾损伤的患者若合并肾外irAEs,则应加大对免疫相关肾损伤的怀疑。本研究中发生肾损伤的患者未进行肾活检以进一步明确病因,因此无法确定是否所有的肾损伤均归因于PD-1抑制剂。这或许是因为恶性肿瘤患者一般情况往往较差,进行肾活检的风险较大,且患者的治疗意愿不高。但如果可能的话,PD-1抑制剂相关肾损伤应该通过肾活检来确认,因肾活检不仅可帮助诊断肾损伤及确定后续的治疗方案,还有助于临床医师决定是否继续PD-1抑制剂治疗及评估患者肾功能的预后。对于拒绝行肾活检或无法行肾活检的患者,应由经验丰富的肾内科医师进行病情评估。美国临床肿瘤学会建议对发生肾损伤的患者进行经验性的早期类固醇治疗[34],但肿瘤患者一般状况差且存在多种合并症,在未获得明确诊断的情况下,使用皮质类固醇对发生肾损伤的患者进行经验性治疗可能会使非免疫介导病变的患者面临类固醇不良反应的风险,并可能因不恰当地停用PD-1抑制剂而延误对肿瘤的治疗。因此,建议加快肿瘤肾脏病学这一专科的建立,以帮助诊断ICIs相关肾损伤,综合评估患者肾损伤情况及ICIs对肿瘤的治疗效果,并对随后的治疗方案提出建议,达到对每一位患者精准治疗的目的[35]
列线图作为一种新型的临床预测模型,相比传统预后分析模型拥有很多优点,包括准确性高、灵活性强、结果易于读取等,目前已广泛应用于临床研究。本研究在多因素logistic回归分析的基础上,将筛选出的独立危险因素进行整合,建立了接受PD-1抑制剂治疗的肿瘤患者AKD发生风险的列线图预测模型,并绘制ROC曲线及校正曲线,结果显示本研究所构建的列线图模型具有良好的区分度和校准度。在临床效用方面,列线图模型的曲线远离两条极端线,说明构建的预测模型在临床决策方面也具有良好的应用价值。模型中的线段长度反映了各危险因素对AKD发生的贡献程度,临床医师可根据接受PD-1抑制剂治疗的肿瘤患者的各项AKD评分指标在列线图模型上找到对应线段端点,向上对得分轴作垂直线得到单项指标得分,然后对各项得分进行求和得出总分,在总分轴找到对应分数,向下对风险轴作垂直线即可得出AKD的预测发生率。研究人员可通过本研究构建的列线图模型个体化预测患者AKD的发生率,从而更好地预防AKD的发生。
综上所述,本研究探讨了接受PD-1抑制剂治疗的肿瘤患者AKD的发生概率、临床特征及其独立危险因素,并进一步建立了接受PD-1抑制剂治疗的肿瘤患者AKD发生风险的列线图预测模型。但本研究仍存在不足:(1)为单中心回顾性研究,样本量偏小,结果可能存在一定偏倚;(2)未对患者的预后、AKD后续治疗及转归、PD-1停药及再使用情况进行进一步跟踪随访;(3)模型仅进行了内部验证,数据代表性不足。未来仍需通过更大样本量的多中心研究来验证,并对患者的预后及转归进行进一步随访,以期对肿瘤患者的临床治疗提供更大的帮助。
  • 常州市科技局科技支撑(社会发展)项目(CE20215024)
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2023年第48卷第11期
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doi: 10.11855/j.issn.0577-7402.1725.2023.0407
  • 接收时间:2022-08-16
  • 首发时间:2025-11-24
  • 出版时间:2023-11-28
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  • 收稿日期:2022-08-16
  • 录用日期:2023-02-08
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Science and Technology Support (Social Development) Project of Bureau of Science and Technology of Changzhou(CE20215024)
常州市科技局科技支撑(社会发展)项目(CE20215024)
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    苏州大学附属第三医院肾内科,江苏常州 213004

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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