目的 探讨早期血钾水平联合超敏C反应蛋白与白蛋白比值(hypersensitive C-reactive protein to albumin ratio,CAR)对持续不卧床腹膜透析(continuous ambulatory peritoneal dialysis,CAPD)患者全因死亡风险的预测价值。 方法 采用单中心回顾性队列研究，收集2016年1月—2024年1月于徐州医科大学附属徐州市立医院腹膜透析中心置管的CAPD患者资料。定义早期血钾为进入腹膜透析前3个月血钾的平均值，根据血钾水平分组：低钾组(血钾＜3.5 mmol/L)、正常血钾组(3.5≤血钾≤5.5 mmol/L)和高钾组(血钾＞5.5 mmol/L)，比较各组患者的一般情况及实验室指标。使用多因素COX比例风险回归模型分析患者全因死亡的相关因素，采用限制性立方样条(restricted cubic spline,RCS)分析早期血钾及CAR水平与全因死亡的关系，通过受试者工作特征曲线(receiver operating characteristic curve,ROC)评估早期血钾联合CAR对全因死亡的预测价值。 结果 共纳入462例CAPD患者，其中低钾组93例(20.1%)、正常血钾组345例(74.7%)、高钾组24例(5.2%)。回顾性随访中位时间为40.0(22.0,63.0)个月，死亡96例(20.8%)。多因素COX回归分析显示：年龄(HR=1.059,95%CI:1.037～1.082,P＜0.001)、血红蛋白(HR=0.987,95%CI:0.974～1.000,P=0.046)、血钾(HR=0.718,95%CI:0.523～0.985,P=0.040)、CAR(HR=2.867,95%CI:1.742～4.720,P＜0.001)均是CAPD患者全因死亡的影响因素。RCS分析表明早期血钾水平与患者全因死亡风险呈“U”型关联，风险最低时血钾为4.397 mmol/L；CAR与全因死亡风险呈“L”型关联，其最佳截断值为0.323 mg/g。血钾联合CAR分析显示：低钾高CAR组(HR=2.361,95%CI:1.214～4.591,P=0.011)、低钾低CAR组(HR=1.808,95%CI:1.059～3.085,P=0.030)及高钾高CAR组(HR=3.191,95% CI:1.149～8.465,P=0.026)患者死亡风险显著上升。ROC曲线下面积(area under the curve,AUC)显示血钾联合CAR对全因死亡的预测价值(AUC=0.737,95%CI:0.678～0.797,P＜0.001)优于单一血钾(AUC=0.694,95%CI:0.626～0.761,P＜0.001)或CAR(AUC=0.709,95%CI:0.654～0.765,P＜0.001)。 结论 早期血钾、CAR水平是CAPD患者全因死亡的影响因素，血钾联合CAR可进一步提高对患者死亡风险的预测价值。
Abstract
Objective To investigate the predictive value of early serum potassium (sK) level combined with the hypersensitive C-reactive protein to albumin ratio (CAR) for all-cause mortality in patients undergoing continuous ambulatory peritoneal dialysis (CAPD). Methods A single-center retrospective cohort study was conducted. Data were collected from CAPD patients who underwent catheterization at the Peritoneal Dialysis Center of Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University from January 2016 to January 2024. Early sK was defined as the mean value of sK within the first three months after initiating peritoneal dialysis. Patients were divided into three groups according to sK level: hypokalemia group (sK＜3.5 mmol/L), normokalemia group (3.5≤sK≤5.5 mmol/L), and hyperkalemia group (sK>5.5 mmol/L). General characteristics and laboratory indicators were compared among the groups. Multivariate Cox proportional hazards regression model was used to analyze factors associated with all-cause mortality. Restricted cubic spline (RCS) analysis was applied to examine the relationship between early sK, CAR and all-cause mortality. Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of early sK combined with CAR for all-cause mortality. Results A total of 462 CAPD patients were enrolled, including 93(20.1%) in the hypokalemia group, 345(74.7%) in the normokalemia group, and 24(5.2%) in the hyperkalemia group. The median follow-up time was 40.0(22.0,63.0) months, and 96 patients (20.8%) died. Multivariate Cox regression analysis showed that age (HR=1.059, 95% CI: 1.037～1.082, P＜0.001), hemoglobin (HR=0.987, 95% CI: 0.974～1.000, P=0.046), sK(HR=0.718, 95% CI: 0.523～0.985, P=0.040), and CAR (HR=2.867, 95% CI: 1.742～4.720, P＜0.001) were independent factors influencing all-cause mortality in CAPD patients. RCS analysis revealed a U-shaped association between early sK level and the risk of all-cause mortality, with the lowest risk at a sK level of 4.397 mmol/L. The association between CAR and all-cause mortality was L-shaped, with an optimal cut-off value of 0.323 mg/g. Combined analysis of sK and CAR showed significantly increased mortality risk in the hypokalemia + high CAR group (HR=2.361, 95% CI:1.214～4.591, P=0.011), hypokalemia+low CAR group (HR=1.808, 95%CI:1.059～3.085,P=0.030), and hyperkalemia + high CAR group (HR=3.191, 95% CI: 1.149～8.465, P=0.026). The area under the ROC curve (AUC) for sK combined with CAR (AUC=0.737, 95% CI:0.678～0.797, P＜0.001) was superior to that of sK alone (AUC=0.694, 95% CI:0.626～0.761,P＜0.001) or CAR alone (AUC=0.709, 95% CI:0.654～0.765, P＜0.001) in predicting all‑cause mortality. Conclusion Early sK level and CAR are influencing factors for all-cause mortality in CAPD patients. The combination of sK and CAR further improves the predictive value for mortality risk.
关键词
持续不卧床腹膜透析 /
血钾 /
超敏C反应蛋白与白蛋白比值 /
全因死亡
Key words
Continuous ambulatory peritoneal dialysis /
Serum potassium /
Hypersensitive C-reactive protein to albumin ratio /
All-cause mortality
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Zhang J, Lu X, Li H, et al. Risk factors for mortality in patients undergoing peritoneal dialysis: a systematic review and meta-analysis[J]. Ren Fail, 2021,43(1):743-753.
[2] Elliott AB, Soliman K, Ullian ME. Hyperkalemia in chronic peritoneal dialysis patients[J]. Ren Fail, 2022,44(1):217-223.
[3] Goncalves FA, de Jesus JS, Cordeiro L, et al. Hypokalemia and hyperkalemia in patients on peritoneal dialysis: incidence and associated factors[J]. Int Urol Nephrol, 2020,52(2):393-398.
[4] Huang N, Liu Y, Ai Z, et al. Mediation of serum albumin in the association of serum potassium with mortality in Chinese dialysis patients: a prospective cohort study[J]. Chin Med J (Engl), 2023,136(2):213-220.
[5] Huang Z, Fang J, Song A, et al. The association between self-management ability and?malnutrition-inflammation-atherosclerosis syndrome in peritoneal dialysis patients: a cross-sectional study[J]. BMC Nephrol, 2021,22(1):13.
[6] 林如, 马雅楠, 张金金, 等. 基线全身炎症反应指数对腹膜透析相关性腹膜炎发生和预后的预测价值[J]. 中国血液净化, 2024,23(02):91-96.
[7] Xu Z, Yang S, Tan Y, et al. Inflammation in cardiovascular-kidney-metabolic syndrome: key roles and underlying mechanisms-a comprehensive review[J]. Mol Cell Biochem, 2025,480(12):6039-6075.
[8] Rashid I, Sahu G, Tiwari P, et al. Malnutrition as a potential predictor of mortality in chronic kidney disease patients on dialysis: A systematic review and meta-analysis[J]. Clin Nutr, 2024,43(7):1760-1769.
[9] Luo G, Feng F, Xu H, et al. Prognostic significance of the hs-CRP/Alb ratio for cardiovascular events in patients with end-stage renal disease undergoing maintenance hemodialysis[J]. Am J Transl Res, 2024,16(7):3108-3116.
[10] Yang C, Hu X, Ling X, et al. Hypokalemia in Peritoneal Dialysis: A Systematic Review and Meta-analysis of Prevalence, Treatment, and Outcomes[J]. Kidney Med, 2024,6(12):100923.
[11] Yu HL, Lu XH, Su CY, et al. Potassium metabolism in continuous ambulatory peritoneal dialysis patients[J]. Ren Fail, 2014,36(5):748-54.
[12] Pan J, Xu X, Wang Z, et al. Dietary Potassium and Clinical Outcomes among Patients on Peritoneal Dialysis[J]. Nutrients, 2023,15(19):4271.
[13] Davies SJ, Zhao J, Morgenstern H, et al. Low Serum Potassium Levels and Clinical Outcomes in Peritoneal Dialysis-International Results from PDOPPS[J]. Kidney Int Rep, 2021,6(2):313-324.
[14] Kwan BC, Szeto C. Dialysis: Hypokalaemia and cardiac risk in peritoneal dialysis patients[J]. Nat Rev Nephrol, 2012,8(9):501-3.
[15] Merker M, Felder M, Gueissaz L, et al. Association of Baseline Inflammation With Effectiveness of Nutritional Support Among Patients With Disease-Related Malnutrition: A Secondary Analysis of a Randomized Clinical Trial[J]. JAMA Netw Open, 2020,3(3):e200663.
[16] Hori S, Tomizawa M, Inoue K, et al. Impact of nutrition and inflammation indicators on peritoneal dialysis initiation and management[J]. Clin Exp Nephrol, 2024,28(4):349-358.
[17] Liu S, Qiu P, Luo L, et al. Serum C-reactive protein to albumin ratio and mortality associated with peritoneal dialysis[J]. Ren Fail, 2020,42(1):600-606.
[18] Sahib A, Choudhury C, Wani IA, et al. Evaluation of Inflammatory Status in Chronic Kidney Disease Patients and a Comparison Between Hemodialysis and Peritoneal Dialysis Patients[J]. Cureus, 2024,16(9):e69443.
[19] Graterol Torres F, Molina M, Soler-Majoral J, et al. Evolving Concepts on Inflammatory Biomarkers and Malnutrition in Chronic Kidney Disease[J]. Nutrients, 2022,14(20):4297.
[20] Pun PH, Goldstein BA, Gallis JA, et al. Serum Potassium Levels and Risk of Sudden Cardiac Death Among Patients With Chronic Kidney Disease and Significant Coronary Artery Disease[J]. Kidney Int Rep, 2017,2(6):1122-1131.
[21] Jiang C, Zheng Q. Outcomes of peritoneal dialysis in elderly vs non-elderly patients: A systemic review and meta-analysis[J]. PLoS One, 2022,17(2):e0263534.
基金
徐州市科技局临床前沿技术项目(KC23143)
