中枢神经系统淋巴瘤患者大剂量甲氨蝶呤排泄延迟风险预测列线图模型的构建与验证

doi:10.5246/jcps.2026.03.018

摘要/Abstract

摘要：

大剂量甲氨蝶呤(HD-MTX)是治疗中枢神经系统淋巴瘤(CNSL)的关键药物, 但其临床应用常因MTX排泄延迟导致的毒副反应风险增加而受到限制。为解决这一难题, 我们开发了一种针对 CNSL 患者MTX排泄延迟的风险预测模型, 旨在提高患者用药安全。本研究分析了2020年7月至2024年7月期间34名中枢神经系统淋巴瘤患者接受的 104个HD-MTX治疗周期的数据。将单变量分析中, P < 0.05的变量纳入多元素逻辑回归模型中, 并基于赤池信息准则(AIC)进行逐步选择, 最终根据最小AIC原则和变量显著性确定白蛋白水平(OR = 0.834)和患者年龄大于60岁(OR = 4.166)为独立风险因素并构建模型。模型验证显示其性能较好, 受试者工作特征曲线下面积(AUC)为0.717(95% CI: 0.605–0.830), Hosmer-Lemeshow拟合优度检验和校准曲线结果表明(χ² = 6.229, P = 0.622), 模型具有良好的校准能力。决策曲线分析进一步验证了该模型在临床实践中的应用价值。自助重抽样法的结果也显示模型具有较好的准确性(AUC = 0.717, 95% CI: 0.602–0.832)。此外, 研究还发现MTX排泄延迟与急性肾损伤的发生显著相关(P < 0.001)。本研究建立的列线图预测模型可有效预测CNSL患者接受HD-MTX治疗后发生排泄延迟的风险, 为临床决策提供了有价值的参考, 同时有助于提高患者的用药安全性。

关键词: 列线图, 大剂量甲氨蝶呤, 中枢神经系统淋巴瘤, 急性肾损伤

Abstract:

High-dose methotrexate (HD-MTX) remains a cornerstone in the treatment of central nervous system lymphoma (CNSL). However, delayed MTX clearance markedly increases the risk of toxic adverse events, thereby restricting its clinical use. To address this challenge, we developed a risk prediction model for delayed MTX clearance in patients with CNSL, aiming to enhance medication safety. This study analyzed data from 34 CNSL patients who underwent 104 HD-MTX treatment cycles between July 2020 and July 2024. Variables with P < 0.05 in univariate analysis were incorporated into a multivariate logistic regression model, and stepwise selection was performed based on the Akaike Information Criterion (AIC). Based on the principle of the minimum AIC value and further considering the statistical significance of the variables, two independent risk factors were identified: serum albumin level (OR = 0.834) and age > 60 years (OR = 4.166), which were used to construct the model. Model validation demonstrated robust performance, with an AUC of 0.717 (95% CI: 0.605–0.830). The Hosmer-Lemeshow goodness-of-fit test (χ² = 6.229, P = 0.622) and calibration curve analysis confirmed good calibration. Decision curve analysis (DCA) further supported the model’s clinical utility, while bootstrap validation yielded consistent accuracy (AUC = 0.717, 95% CI: 0.602–0.832). Additionally, delayed MTX clearance was significantly associated with acute kidney injury (P < 0.001). In summary, the risk prediction model developed in this study provided reliable estimates of delayed MTX clearance in CNSL patients undergoing HD-MTX therapy, offering a valuable reference to guide clinical decision-making and improve patient safety.

Key words: Nomogram, High-dose methotrexate, Central nervous system lymphoma, Acute kidney injury

Supporting:

吴迪, 李萍, 马妍妮, 杨小英, 王欣瑜. 中枢神经系统淋巴瘤患者大剂量甲氨蝶呤排泄延迟风险预测列线图模型的构建与验证[J]. 中国药学(英文版), 2026, 35(3): 264-274.

Di Wu, Ping Li, Yanni Ma, Xiaoying Yang, Xinyu Wang. Development and validation of a nomogram for predicting the risk of delayed HD-MTX clearance in patients with central nervous system lymphoma[J]. Journal of Chinese Pharmaceutical Sciences, 2026, 35(3): 264-274.

图/表 8

Table 1. Correlation between variables and delayed clearance.

Table 2. Predictors for delayed MTX clearance in CNSL patients.

Figure 1. Nomogram for predicting the risk of MTX delayed clearance.

Figure 2. ROC curve of the nomogram for predicting delayed MTX clearance.

Figure 3. Calibration curve of the nomogram.

Figure 4. The DCA shows the clinical usefulness of the nomogram.

Figure 5. ROC curve for bootstrap validation.

Table 3. Univariate analysis for predicting the AKI.

参考文献 36

[1]	Zhang, Y.; Ye, J.J.; Chen, H.; Zhou, D.B.; Ji, C.Y. Efficacy and safety of BTKis in central nervous system lymphoma: a systematic review and meta-analysis. Cancers. 2024, 16, 860.
[2]	Mendez, J.S.; Ostrom, Q.T.; Gittleman, H.; Kruchko, C.; DeAngelis, L.M.; Barnholtz-Sloan, J.S.; Grommes, C. The elderly left behind: changes in survival trends of primary central nervous system lymphoma over the past 4 decades. Neuro Oncol. 2018, 20, 687–694.
[3]	Bobillo, S.; Khwaja, J.; Ferreri, A.J.; Cwynarski, K. Prevention and management of secondary central nervous system lymphoma. Haematologica. 2023, 108, 673–689.
[4]	Schaff, L.R.; Grommes, C. Primary central nervous system lymphoma. Blood. 2022, 140, 971–979.
[5]	Orellana-Noia, V.; Abousaud, A. Secondary central nervous system lymphoma: updates in treatment and prophylaxis strategies. Curr. Treat. Options Oncol. 2022, 23, 1443–1456.
[6]	Rubenstein, J.L.; Gupta, N.K.; Mannis, G.N.; LaMarre, A.K.; Treseler, P. How I treat CNS lymphomas. Blood. 2013, 122, 2318–2330.
[7]	Howard, S.C.; McCormick, J.; Pui, C.H.; Buddington, R.K.; Harvey, R.D. Preventing and managing toxicities of high-dose methotrexate. Oncologist. 2016, 21, 1471–1482.
[8]	Yang, Y.Y.; Wang, C.Y.; Chen, Y.T.; Wang, X.B.; Jiao, Z.; Wang, Z. External evaluation and systematic review of population pharmacokinetic models for high-dose methotrexate in cancer patients. Eur. J. Pharm. Sci. 2023, 186, 106416.
[9]	Song, Z.W.; Hu, Y.; Liu, S.; Wang, G.R.; Zhai, S.D.; Zhang, X.L.; Li, Y.P.; Du, G.H.; Shi, Y.K.; Chen, Y.L.; Dong, M.; Guo, R.C.; Guo, W.; Huang, H.B.; Huang, X.J.; Jing, H.M.; Ke, X.Y.; Li, G.H.; Miao, L.Y.; Niu, X.H.; Qiu, F.; Shen, J.N.; Tang, J.Y.; Wang, T.Y.; Wang, X.L.; Wang, Z.; Wu, J.H.; Zhan, S.Y.; Zhang, B.K.; Zhang, L.L.; Zhang, Y.H.; Zhang, W.; Zhao, L.M.; Zhao, L.B.; Zhen, J.C.; Zheng, H.Y.; Zhu, Z.; Jiang, D.; Huang, Z.C.; Tan, Z.Y.; Lin, Q.N.; Zhao, R.S. Medication therapy of high-dose methotrexate: an evidence-based practice guideline of the Division of Therapeutic Drug Monitoring, Chinese Pharmacological Society. Br. J. Clin. Pharmacol. 2022, 88, 2456–2472.
[10]	Yu, L.T.; Shen, J.Y.; Li, H.N.; Zhang, M.; Wang, Z.; Gao, Y.J.; Chen, J.H.; Li, J.Y. Factors influencing delayed high-dose methotrexate excretion and its correlation with adverse reactions after treatment in children with malignant hematological tumors. Transl. Pediatr. 2024, 13, 300–309.
[11]	Sun, K.; Tao, H.W.; Ding, T.L.; Li, Z.R.; Qiu, X.Y.; Zhong, M.K.; Wu, Z. Risk factors for high-dose methotrexate associated toxicities in patients with primary central nervous system lymphoma. J. Clin. Pharm. Ther. 2022, 47, 2196–2204.
[12]	Ikeda, D.; Isezaki, T.; Narita, K.; Yuyama, S.; Oura, M.; Uehara, A.; Tabata, R.; Takeuchi, M.; Matsue, K. Development of a novel nomogram for predicting delayed methotrexate excretion following high-dose methotrexate in adult patients with hematologic malignancies. Cancer Chemother. Pharmacol. 2024, 94, 397–406.
[13]	Nakano, T.; Kobayashi, R.; Matsushima, S.; Hori, D.; Yanagi, M.; Suzuki, D.; Kobayashi, K. Risk factors for delayed elimination of high-dose methotrexate in childhood acute lymphoblastic leukemia and lymphoma. Int. J. Hematol. 2021, 113, 744–750.
[14]	Li, M.; Kong, X.Y.; Wang, S.M. Risk factors for delayed methotrexate elimination in pediatric patients with hematological malignancies: a retrospective analysis. J. Chin. Pharm. Sci. 2022, 31, 746–754.
[15]	Zhan, M.; Chen, Z.B.; Ding, C.C.; Qu, Q.; Wang, G.Q.; Liu, S.X.; Wen, F.Q. Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning. Int. J. Hematol. 2021, 114, 483–493.
[16]	He, X.; Yao, P.L.; Li, M.T.; Liang, H.; Liu, Y.L.; Du, S.; Zhang, M.; Sun, W.Z.; Wang, Z.Y.; Hao, X.; Yu, Z.; Gao, F.; Liu, X.X.; Tong, R.S. A risk scoring model for high-dose methotrexate-induced liver injury in children with acute lymphoblastic leukemia based on gene polymorphism study. Front. Pharmacol. 2021, 12, 726229.
[17]	Rice, M.L.; Barreto, E.F.; Rule, A.D.; Martin, C.E.; Truong, H.L.; Mara, K.C.; Kashani, K.B.; Thompson, C.A.; Witzig, T.E.; Barreto, J.N. Development and validation of a model to predict acute kidney injury following high-dose methotrexate in patients with lymphoma. Pharmacotherapy. 2024, 44, 4–12.
[18]	Jian, C.; Chen, S.Q.; Wang, Z.C.; Zhou, Y.; Zhang, Y.; Li, Z.Y.; Jian, J.; Wang, T.T.; Xiang, T.Y.; Wang, X.; Jia, Y.T.; Wang, H.L.; Gong, J. Predicting delayed methotrexate elimination in pediatric acute lymphoblastic leukemia patients: an innovative web-based machine learning tool developed through a multicenter, retrospective analysis. BMC Med. Inform. Decis. Mak. 2023, 23, 148.
[19]	Khwaja, A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin. Pract. 2012, 120, c179–c184.
[20]	Van Calster, B.; Wynants, L.; Verbeek, J.F.M.; Verbakel, J.Y.; Christodoulou, E.; Vickers, A.J.; Roobol, M.J.; Steyerberg, E.W. Reporting and interpreting decision curve analysis: a guide for investigators. Eur. Urol. 2018, 74, 796–804.
[21]	Gros, L.; Roldán, A.; Cabero-Martínez, A.; Domínguez-Pinilla, N.; de la Fuente, A.; González-Barca, E.; Tasso, M.; Torrent, M.; Gallardo, E.; del Cerro, I.; Giró-Perafita, A.; Badia, X. Incidence and management of patients with methotrexate delayed elimination in the clinical practice: a Delphi study. J. Oncol. Pharm. Pract. 2023, 29, 794–801.
[22]	Kataoka, T.; Sakurashita, H.; Kajikawa, K.; Saeki, Y.; Taogoshi, T.; Matsuo, H. Low serum albumin level is a risk factor for delayed methotrexate elimination in high-dose methotrexate treatment. Ann. Pharmacother. 2021, 55, 1195–1202.
[23]	Reiss, S.N.; Buie, L.W.; Adel, N.; Goldman, D.A.; Devlin, S.M.; Dan, D.E. Hypoalbuminemia is significantly associated with increased clearance time of high dose methotrexate in patients being treated for lymphoma or leukemia. Ann. Hematol. 2016, 95, 2009–2015.
[24]	Evans, W.E.; Pratt, C.B. Effect of pleural effusion on high-dose methotrexate kinetics. Clin. Pharmacol. Ther. 1978, 23, 68–72.
[25]	Wight, J.; Ku, M.; Garwood, M.; Carradice, D.; Lasica, M.; Keamy, L.; Hawkes, E.A.; Grigg, A. Toxicity associated with high-dose intravenous methotrexate for hematological malignancies. Leuk. Lymphoma. 2022, 63, 2375–2382.
[26]	Mei, S.H.; Li, X.G.; Jiang, X.Y.; Yu, K.F.; Lin, S.; Zhao, Z.G. Population pharmacokinetics of high-dose methotrexate in patients with primary central nervous system lymphoma. J. Pharm. Sci. 2018, 107, 1454–1460.
[27]	Mao, J.J.; Li, Q.; Li, P.; Qin, W.W.; Chen, B.B.; Zhong, M.K. Evaluation and application of population pharmacokinetic models for identifying delayed methotrexate elimination in patients with primary central nervous system lymphoma. Front. Pharmacol. 2022, 13, 817673.
[28]	Arshad, U.; Taubert, M.; Seeger-Nukpezah, T.; Ullah, S.; Spindeldreier, K.C.; Jaehde, U.; Hallek, M.; Fuhr, U.; Vehreschild, J.J.; Jakob, C. Evaluation of body-surface-area adjusted dosing of high-dose methotrexate by population pharmacokinetics in a large cohort of cancer patients. BMC Cancer. 2021, 21, 719.
[29]	Burris, J.F.; Tortorici, M.A.; Mandic, M.; Neely, M.; Reed, M.D. Dosage adjustments related to young or old age and organ impairment. J. Clin. Pharmacol. 2016, 56, 1461–1473.
[30]	Widemann, B.C.; Adamson, P.C. Understanding and managing methotrexate nephrotoxicity. Oncologist. 2006, 11, 694–703.
[31]	May, J.; Carson, K.R.; Butler, S.; Liu, W.J.; Bartlett, N.L.; Wagner-Johnston, N.D. High incidence of methotrexate associated renal toxicity in patients with lymphoma: a retrospective analysis. Leuk. Lymphoma. 2014, 55, 1345–1349.
[32]	Wiczer, T.; Dotson, E.; Tuten, A.; Phillips, G.; Maddocks, K. Evaluation of incidence and risk factors for high-dose methotrexate-induced nephrotoxicity. J. Oncol. Pharm. Pract. 2016, 22, 430–436.
[33]	Yen, P.W.; Lin, H.Y.; Wu, C.C.; Huang, T.C.; Wu, S.J.; Pan, S.Y.; Hung, K.Y. Serum methotrexate level predicts acute kidney injury after high-dose methotrexate: a case report and single-center experience. Int. J. Nephrol. Renovascular Dis. 2024, 17, 277–285.
[34]	Yin, A.Y.; de Groot, F.A.; Guchelaar, H.J.; Nijland, M.; Doorduijn, J.K.; Touw, D.J.; Munnink, T.O.; de Winter, B.C.M.; Friberg, L.E.; Vermaat, J.S.P.; Moes, D.J.A.R. Population pharmacokinetic and toxicity analysis of high-dose methotrexate in patients with central nervous system lymphoma. Clin. Pharmacokinet. 2025, 64, 79–91.
[35]	Li, W.S.; Mo, J.Y.; Yang, Z.L.; Zhao, Z.G.; Mei, S.H. Risk factors associated with high-dose methotrexate induced toxicities. Expert Opin. Drug Metab. Toxicol. 2024, 20, 263–274.
[36]	Garneau, A.P.; Riopel, J.; Isenring, P. Acute methotrexate-induced crystal nephropathy. N Engl. J. Med. 2015, 373, 2691–2693.