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
Construction and evaluation of risk prediction model for renal injury in tumor patients receiving PD-1 inhibitor treatment
- 解放军医学杂志 2023年48卷第11期 页码：1328-1337
- 作者机构：
1.苏州大学附属第三医院肾内科，江苏常州 213004
- 作者简介：
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
杨敏，E-mail：yangmin1516@suda.edu.cn
- 基金信息：常州市科技局科技支撑(社会发展)项目(CE20215024)
DOI：10.11855/j.issn.0577-7402.1725.2023.0407
中图分类号：
扫 描 看 全 文
卢珮宇, 杨艳, 周华, 等. 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[J]. 解放军医学杂志, 2023, 48(11): 1328-1337.
Lu Pei-Yu,Yang Yan,Zhou Hua,et al.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.
卢珮宇, 杨艳, 周华, 等. 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[J]. 解放军医学杂志, 2023, 48(11): 1328-1337. DOI： 10.11855/j.issn.0577-7402.1725.2023.0407.
Lu Pei-Yu,Yang Yan,Zhou Hua,et al.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.
摘要
目的,2,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,2,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,2,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,2,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。
Abstract
Objective,2,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,2,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,2,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,2,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.
关键词
程序性死亡受体-1抑制剂肾损伤发病率危险因素Nomogram模型
Keywords
programmed cell death protein-1 inhibitorskidney injuryincidence raterisk factorsnomogram model
references
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249.
Robert C. A decade of immune-checkpoint inhibitors in cancer therapy[J]. Nat Commun, 2020, 11(1): 3801.
Gao L, Yu Y, Li XM, et al. Research progress on the effect of chemotherapeutic drugs on immune checkpoints and immunotherapy[J]. Med J Chin PLA, 2022, 47(11): 1152-1158.
高磊, 俞阳, 李晓梅, 等. 化疗与免疫检查点抑制剂联用对肿瘤的作用研究进展[J]. 解放军医学杂志, 2022, 47(11): 1152-1158.
Hamid O, Robert C, Daud A, et al. Five-year survival outcomes for patients with advanced melanoma treated with pembrolizumab in KEYNOTE-001[J]. Ann Oncol, 2019, 30(4): 582-588.
Gu HY, Lin LL, Zhang C, et al. The potential of five immune-related prognostic genes to predict survival and response to immune checkpoint inhibitors for soft tissue sarcomas based on multi-omic study[J]. Front Oncol, 2020, 10: 1317.
Baumeister SH, Freeman GJ, Dranoff G, et al. Coinhibitory pathways in immunotherapy for cancer[J]. Annu Rev Immunol, 2016, 34: 539-573.
Kroemer G, Zitvogel L. Immune checkpoint inhibitors[J]. J Exp Med, 2021, 218(3): e20201979.
Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy[J]. Nat Rev Cancer, 2012, 12(4): 252-264.
Motz GT, Coukos G. Deciphering and reversing tumor immune suppression[J]. Immunity, 2013, 39(1): 61-73.
Luo JM, Yang ZC, Pan L, et al. Research progress of treatment process on endocrine adverse reactions caused by immune checkpoint inhibitors[J]. Med J Chin PLA, 2021, 46(2): 200-206.
罗景梅, 杨中策, 潘俐, 等. 免疫检查点抑制剂所致内分泌不良反应处理流程研究进展[J]. 解放军医学杂志, 2021, 46(2): 200-206.
Postow MA, Sidlow R, Hellmann MD. Immune-related adverse events associated with immune checkpoint blockade[J]. N Engl J Med, 2018, 378(2): 158-168.
Michot JM, Bigenwald C, Champiat S, et al. Immune-related adverse events with immune checkpoint blockade: a comprehensive review[J]. Eur J Cancer, 2016, 54: 139-148.
Cortazar FB, Marrone KA, Troxell ML, et al. Clinicopathological features of acute kidney injury associated with immune checkpoint inhibitors[J]. Kidney Int, 2016, 90(3): 638-647.
Wanchoo R, Karam S, Uppal NN, et al. Adverse renal effects of immune checkpoint inhibitors: a narrative review[J]. Am J Nephrol, 2017, 45(2): 160-169.
Oleas D, Bolufer M, Agraz I, et al. Acute interstitial nephritis associated with immune checkpoint inhibitors: a single-centre experience[J]. Clin Kidney J, 2020, 14(5): 1364-1370.
Shirali AC, Perazella MA, Gettinger S. Association of acute interstitial nephritis with programmed cell death 1 inhibitor therapy in lung cancer patients[J]. Am J Kidney Dis, 2016, 68(2): 287-291.
Antonia SJ, Villegas A, Daniel D, et al. Durvalumab after chemoradiotherapy in stage III non-small-cell lung cancer[J]. N Engl J Med, 2017, 377(20): 1919-1929.
Perazella MA, Shirali AC. Immune checkpoint inhibitor nephrotoxicity: what do we know and what should we do?[J]. Kidney Int, 2020, 97(1): 62-74.
García-Carro C, Bolufer M, Bury R, et al. Acute kidney injury as a risk factor for mortality in oncological patients receiving checkpoint inhibitors[J]. Nephrol Dial Transplant, 2022, 37(5): 887-894.
Champiat S, Lambotte O, Barreau E, et al. Management of immune checkpoint blockade dysimmune toxicities: a collaborative position paper[J]. Ann Oncol, 2016, 27(4): 559-574.
Meraz-Muñoz A, Amir E, Ng P, et al. Acute kidney injury associated with immune checkpoint inhibitor therapy: incidence, risk factors and outcomes[J]. J Immunother Cancer, 2020, 8(1): e000467.
Seethapathy H, Zhao S, Chute DF, et al. The incidence, causes, and risk factors of acute kidney injury in patients receiving immune checkpoint inhibitors[J]. Clin J Am Soc Nephrol, 2019, 14(12): 1692-1700.
Gupta S, Short SAP, Sise ME, et al. Acute kidney injury in patients treated with immune checkpoint inhibitors[J]. J Immunother Cancer, 2021, 9(10): e003467.
Cortazar FB, Kibbelaar ZA, Glezerman IG, et al. Clinical features and outcomes of immune checkpoint inhibitor-associated AKI: a multicenter study[J]. J Am Soc Nephrol, 2020, 31(2): 435-446.
Ziberna L, Martelanc M, Franko M, et al. Bilirubin is an endogenous antioxidant in human vascular endothelial cells[J]. Sci Rep, 2016, 6: 29240.
Qie HJ, Liu JT, Wen Y, et al. Effects and mechanism of exposure to high altitude environment on liver function in rats[J]. Med J Chin PLA, 2022, 47(10): 961-967.
且华吉, 刘江涛, 文艺, 等. 高原高海拔环境暴露对大鼠肝脏功能的影响及其机制[J]. 解放军医学杂志, 2022, 47(10): 961-967.
Zhang XL, Xiao X. Neonatal hyperbilirubinemia and organ damage[J]. Chin J Neonatol, 2007, 22(4): 254-256.
张新利, 肖昕. 新生儿高胆红素血症与脏器损害[J]. 中国新生儿科杂志, 2007, 22(4): 254-256.
Du L, Ma X, Shen X, et al. Neonatal hyperbilirubinemia management: clinical assessment of bilirubin production[J]. Semin Perinatol, 2021, 45(1): 151351.
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]. J Thorac Dis, 2015, 7(8): 1385-1390.
Cheng B, Jin Y, Liu G, et al. Urinary N-acetyl-beta-D-glucosaminidase as an early marker for acute kidney injury in full-term newborns with neonatal hyperbilirubinemia[J]. Dis Markers, 2014, 2014: 315843.
Betjes MGH, Bajema I. The pathology of jaundice-related renal insufficiency: cholemic nephrosis revisited[J]. J Nephrol, 2006, 19(2): 229-233.
Leclerc M, Lanot A, Béchade C, et al. Bile salt nephropathy/cholemic nephrosis[J]. Nephrol Ther, 2016, 12(6): 460-462.
Billings FT 4th, Pretorius M, Schildcrout JS, et al. Obesity and oxidative stress predict AKI after cardiac surgery[J]. J Am Soc Nephrol, 2012, 23(7): 1221-1228.
Brahmer JR, Lacchetti C, Schneider BJ, et al. Management of immune-related adverse events in patients treated with immune checkpoint inhibitor therapy: American society of clinical oncology clinical practice guideline[J]. J Clin Oncol, 2018, 36(17): 1714-1768.
Capasso A, Benigni A, Capitanio U, et al. Summary of the International Conference on Onco-Nephrology: an emerging field in medicine[J]. Kidney Int, 2019, 96(3): 555-567.
0
浏览量
0
下载量
0
CSCD
0
CNKI被引量
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies