目的: 开发和验证一种易于使用的评估工具,用于预测维持性血液透析患者短期生存率。方法: 回顾性分析2018年1月—2022年12月在长沙市第四医院透析中心行规律透析的维持性血液透析（maintenance hemodialysis,MHD）患者的临床资料。共纳入543名MHD患者,按照7∶3的比例随机分为训练队列（380名）和验证队列（163名）。使用最小绝对收缩和选择算子（least absolute shrinkage and selection operator,LASSO）回归和Cox多因素回归分析确定预测因素,并构建列线图。利用C指数、受试者工作曲线（receiver operating characteristic,ROC）、ROC曲线下面积（area under the curve,AUC）、校准曲线评估列线图性能。结果: Cox多因素回归分析结果显示年龄（HR=1.03,95%CI：1.01~1.05）、透析龄（HR=0.95,95%CI：0.93~0.96）、血清铁蛋白<21.8 ng/mL（HR=0.68,95%CI：0.09~4.98）、274.66 ng/mL<血清铁蛋白≤500 ng/mL（HR=1.72,95%CI：0.94~3.12）、血清铁蛋白>500 ng/mL（HR=2.36,95%CI：1.50~3.72）有统计学意义。对列线图进行交叉验证,结果显示训练队列的C指数为0.802,验证队列的C指数为0.838,这说明该模型具有较好的区分度。训练队列和验证队列中的辨别能力较好（在训练队列和验证队列1年AUC分别为0.909和0.948;3年AUC分别为0.899和0.928）。训练队列和验证队列的校准曲线显示校准良好,表明该模型具有较好的预测性能。结论: 基于年龄、透析龄、血清铁蛋白这3个常见的临床指标,我们构建了一个可以预测MHD患者1年和3年生存率的列线图,具有潜在的临床价值,可用于临床实践。
Abstract
Objective To develop and validate an easy-to-use assessment tool for predicting short-term survival in maintenance hemodialysis (MHD) patients. Method The clinical data of MHD patients undergoing regular dialysis at the Dialysis Center of the Fourth Hospital of Changsha City from January 2018 to December 2022 were retrospectively analyzed. A total of 543 MHD patients were enrolled and randomly allocated into the training cohort (380 patients) and the validation cohort (163 patients) at a 7: 3 ratio. Least absolute shrinkage and selection operator (LASSO) regression and Cox multifactor regression analysis were employed to identify predictors and construct a nomogram. The nomogram's performance was assessed through the C-index, receiver operating curve (ROC), area under the curve (AUC), and calibration curve. Results Cox multivariate regression analysis revealed that age (HR=1.03, 95% CI 1.01-1.05), duration of dialysis (HR=0.95, 95% CI 0.93-0.96), serum ferritin<21.8 ng/mL (HR=0.68, 95% CI 0.09-4.98), 274.66 ng/mL<serum ferritin ≤500 ng/mL (HR=1.72, 95% CI 0.94-3.12), and serum ferritin>500 ng/mL (HR=2.36, 95% CI 1.50-3.72) were statistically significant. The cross-validation on the nomogram demonstrated a C-index of 0.802 for the training cohort and 0.838 for the validation cohort, indicating a strong discriminatory capability. Discrimination ability in both cohorts was favorable (the 1-year AUC in the training cohort and the validation cohort were 0.909 and 0.948, respectively; The 3-year AUC was 0.899 and 0.928, respectively). Calibration curves for both training and validation cohorts exhibited excellent alignment, underscoring the model's robust predictive performance. Conclusion Utilizing three widely employed clinical indicators—age, duration of dialysis, and serum ferritin. We developed a nomogram capable of predicting the 1-year and 3-year survival rates of MHD patients. This nomogram holds significant clinical potential and can be effectively applied in clinical practice.
关键词
维持性血液透析 /
列线图 /
预测模型 /
全因死亡率 /
生存率
Key words
maintenance hemodialysis (MHD) /
nomogram /
prediction model /
all-cause mortality /
survival rate
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参考文献
[1] CREWS D C, BELLO A K, SAADI G, et al.Burden, access, and disparities in kidney disease[J]. Journal of Nephrology, 2019, 32(1) : 1-8.
[2] 李晓勤, 陈天浩, 李琳. 影响终末期肾病老年患者血液透析内瘘功能不良的危险因素及应对策略分析[J]. 湖南师范大学学报 (医学版) , 2022, 19(3) : 16-19.
[3] HIMMELFARB J, VANHOLDER R, MEHROTRA R, et al.The current and future landscape of dialysis[J]. Nat Rev Nephrol, 2020, 16(10): 573-585.
[4] LIU J, ZHANG H, DIAO Z, et al.Epidemiological analysis of death among patients on maintenance hemodialysis: results from the beijing blood purification quality Control and Improvement Center[J]. BMC Nephrology, 2023, 24: 236.
[5] CHAN K E, MADDUX F W, TOLKOFF-RUBIN N, et al.Early Outcomes among Those Initiating Chronic Dialysis in the United States[J]. Clin J Am Soc Nephrol, 2011, 6(11) : 2642-2649.
[6] 汪丽, 吴海洋, 张欢, 等. 2019—2021年单中心血液透析死亡患者流行病学特点分析[J]. 肾脏病与透析肾移植杂志, 2022, 31(06) : 519-524.
[7] 赵新菊, 甘良英, 牛庆雨, 等. 中国血液透析患者死亡原因及特点分析-DOPPS研究的启示[J]. 中国血液净化, 2022, 21(02) : 89-93.
[8] KRAMER A, BOENINK R, STEL V S, et al.The ERA-EDTA Registry Annual Report 2018: a summary[J]. Clinical Kidney Journal, 2020, 14(1): 107-123.
[9] WANG X, LU J, SONG Z, et al.From past to future: Bibliometric analysis of global research productivity on nomogram (2000-2021)[J]. Frontiers in Public Health, 2022, 10: 997713.
[10] XUE B, HE Y, JING F, et al.Dynamic coarse-to-fine ISAR image blind denoising using active joint prior learning[J]. International Journal of Intelligent Systems, 2021, 36(8) : 4143-4166.
[11] PUPPO P, PERACHINO M.Clinical stage, prostate-specific antigen and Gleason grade to predict extracapsular disease or nodal metastasis in men with newly diagnosed, previously untreated prostate cancer. A multicenter study. A. Ur. O. Cooperative Group[J]. European Urology, 1997, 32(3) : 273-279.
[12] FLOEGE J, GILLESPIE I A, KRONENBERG F, et al.Development and validation of a predictive mortality risk score from a European hemodialysis cohort[J]. Kidney Int, 2015, 87(5) : 996-1008.
[13] WAGNER M, ANSELL D, KENT D M, et al.Predicting Mortality in Incident Dialysis Patients: An Analysis of the United Kingdom Renal Registry[J]. Am J Kidney Dis, 2011, 57(6) : 894-902.
[14] OUYANG H, SHI Q, ZHU J, et al.Nomogram for predicting 1-, 5-, and 10-year survival in hemodialysis (HD) patients: a single center retrospective study[J]. Renal Failure, 43(1) : 1508-1519.
[15] YANG M, YANG Y, XU Y, et al.Development and Validation of Prediction Models for All-Cause Mortality and Cardiovascular Mortality in Patients on Hemodialysis: A Retrospective Cohort Study in China[J]. Clin Interv Aging, 2023, 18: 1175-1190.
[16] YOU X, GU B, CHEN T, et al.Development of long-term cardiovascular disease risk prediction model for hemodialysis patients with end-stage renal disease based on nomogram[J]. Ann Palliat Med, 2021, 10(3): 3142-3153.
[17] 罗琼, 王湘川, 杨海蓉, 等. 中国维持性血液透析尿毒症患者死亡危险因素的Meta分析[J]. 湖南师范大学学报 (医学版) , 2022, 19(2): 81-84.
[18] HAZARA A M, BHANDARI S.Age, Gender and Diabetes as Risk Factors for Early Mortality in Dialysis Patients: A Systematic Review[J]. Clin Med Res, 2021, 19(2) : 54-63.
[19] VICENTINI C A de A, PONCE D. Comparative analysis of patients' survival on hemodialysis vs. peritoneal dialysis and identification of factors associated with death[J]. J Bras Nefrol, 2022, 45: 8-16.
[20] YAO W, SHEN Y, HUANG H, et al. A Retrospective Study from a Single Center in China to Develop a Nomogram to Predict One-Year Mortality in Patients with End-Stage Renal Disease Who Are Receiving Hemodialysis[J]. Med Sci Monit, 2022, 28: e936092-1-e936092-14.
[21] YEUNG S M H, VAN LONDEN M, NAKSHBANDI U, et al. Pretransplant NT-proBNP, Dialysis Vintage, and Posttransplant Mortality in Kidney Transplant Recipients[J]. Transplantation, 2020, 104(10) : 2158-2165.
[22] SUMIDA K, YAMAGATA K, ISEKI K, et al.Different impact of hemodialysis vintage on cause-specific mortality in long-term hemodialysis patients[J]. Nephrol Dial Transplant, 2016, 31(2) : 298-305.
[23] OKECHUKWU C N, LOPES A A, STACK A G, et al.Impact of years of dialysis therapy on mortality risk and the characteristics of longer term dialysis survivors[J]. Am J Kidney Dis, 2002, 39(3) : 533-538.
[24] FISHBANE S, COYNE D W.How I treat renal anemia[J]. Blood, 2020, 136(7) : 783-789.
[25] MCCULLOUGH K, BOLISETTY S.Ferritins in kidney disease[J]. Semin Nephrol, 2020, 40(2) : 160-172.
[26] KIM T, STREJA E, SOOHOO M, et al.Serum Ferritin Variations and Mortality in Incident Hemodialysis Patients[J]. Am J Nephrol, 2017, 46(2) : 120-130.
[27] KARABOYAS A, MORGENSTERN H, PISONI R L, et al.Association between serum ferritin and mortality: findings from the USA, Japan and European Dialysis Outcomes and Practice Patterns Study[J]. Nephrol Dial Transplant, 2018, 33(12) : 2234-2244.
[28] KANG S H, KIM B Y, SON E J, et al.Association between Iron Status and Survival in Patients on Chronic Hemodialysis[J]. Nutrients, 2023, 15(11) : 2577.
基金
湖南省卫生健康委科研计划项目“NLRP3炎症小体信号通路在腹膜透析患者腹膜间皮细胞上皮-间质转化中的作用机制研究”（D202303058217）
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