Objective To investigate the risk factors for the development of dialysis disequilibrium syndrome (DDS) in patients on maintenance hemodialysis (MHD) and to construct a predictive model. Methods A total of 321 patients with MHD in our hospital from August 2019 to August 2022 were selected and patients were randomly divided into the training group (n=225) and the internal verification group (n=96) according to the proportion of 7:3. The two groups were divided into the DDS subgroup and the non-DDS subgroup according to the occurrence of DDS. The incidence of imbalance syndrome, demographic characteristics and biochemical indicators of the two groups were analyzed. The logistic regression model and random forest model were constructed based on the data of the training group. Then Parallel internal and external validation was performed in the groups. Results Univariate analysis showed that in the training and internal validation groups, age (t=32.154, 24.618, both P＜0.001), number of dialysis sessions per week (t=10.632, 8.211, both P＜0.001), epilepsy (χ2=4.647, 7.248, P=0.031, 0.007), hemoglobin (t= 21.366, 15.476, all P＜0.001), cognitive impairment (χ2=4.644, 5.403, P=0.031, 0.020), urea nitrogen (t=21.284, 13.058, all P＜0.001), and albumin (t=13.094, 9.018, all P＜0.001) between the DDS subgroups and the non-DDS subgroup were significant differences (P＜0.05). Logistic regression analysis showed that the number of dialysis sessions per week (OR=6.360, 95% CI: 1.968 to 20.554, P＜0.001), cognitive impairment (OR=8.404, 95% CI: 2.446 to 28.877, P＜0.001), hemoglobin (OR=4.889, 95% CI: 1.436 to 16.645, P＜0.001), albumin (OR=0.596, 95% CI: 0.447 to 0.794, P＜0.001), and urea nitrogen (OR=4.429, 95% CI: 1.879 to 10.441, P＜0.001) were factors influencing the occurrence of DDS in patients (P＜0.05). The top 5 influencing factors for the occurrence of DDS were obtained in the following order: urea nitrogen, cognitive impairment, hemoglobin, albumin, and number of dialysis sessions per week. Based on the above factors, logistic regression models and random forest models for the occurrence of DDS in patients were constructed, and internal validation showed that there was no significant difference between the two models in predicting the AUC of DDS in patients, and external validation showed that there was no significant difference between the two models and the actual results. Conclusion The influence of DDS in MHD patients is due to urea nitrogen, cognitive impairment, hemoglobin, albumin, and weekly dialysis times. The prediction model built based on the above factors is reliable and provides a certain reference for clinical treatment identification of DDS.
[1] Hargrove N,El Tobgy N,Zhou O,et al.Effect of aerobic exercise on dialysis-related symptoms in individuals undergoing maintenance hemodialysis: a systematic review and meta-analysis of clinical trials[J].Clin J Am Soc Nephrol,2021,16(4):560-574. DOI: 10.2215/CJN.15080920.
[2] Vachharajani TJ,Taliercio JJ,Anvari E.New devices and technologies for hemodialysis vascular access: a review[J].Am J Kidney Dis,2021,78(1):116-124. DOI: 10.1053/j.ajkd.2020.11.027.
[3] Dai L,Lu C,Liu J,et al.Impact of twice-or three-times-weekly maintenance hemodialysis on patient outcomes: a multicenter randomized trial[J].Medicine (Baltimore),2020,99(20):e20202. DOI: 10.1097/MD.0000000000020202.
[4] Bellamkonda A,Chowdhury T,Shankar M,et al.Dialysis disequilibrium syndrome: a red flag to check post hemodialysis[J].Cureus,2022,14(4):e24619. DOI: 10.7759/cureus.24619.
[5] Saha M,Allon M.Diagnosis, treatment, and prevention of hemodialysis emergencies[J].Clin J Am Soc Nephrol,2017,12(2):357-369. DOI: 10.2215/CJN.05260516.
[6] Wirawan C,Sumada IK,Yuliani D,et al.Dialysis disequilibrium syndrome induced seizure following hemodialysis[J].Cureus,2021,13(9):e17821. DOI: 10.7759/cureus.17821.
[7] Yao D,Zhan X,Zhan X,et al.A random forest based computational model for predicting novel lncRNA-disease associations[J].BMC Bioinformatics,2020,21(1):126. DOI: 10.1186/s12859-020-3458-1.
[8] 上海慢性肾脏病早发现及规范化诊治与示范项目专家组,高翔,梅长林.慢性肾脏病筛查诊断及防治指南[J].中国实用内科杂志,2017,37(1):28-34. DOI:10.19538/j.nk2017010108.
[9] 罗艳虹,李治,余红梅,等.基于代价敏感性和概率校准的先天性心脏病概率预测模型研究[J].中国卫生统计,2019,36(1):36-39. DOI: CNKI:SUN:ZGWT.0.2019-01-008.
[10] Pudlo P,Marin JM Estoup A,et al.Reliable ABC model choice via random forests[J].Bioinformatics,2016,32(6):859-66. DOI: 10.1093/bioinformatics/btv684.
[11] 谈军涛,许晓梅,何雨芯,等.基于机器学习算法的肝硬化相关肝性脑病预测模型的构建[J].解放军医学杂志,2021,46(4):354-360. DOI: 10.11855/j.issn.0577-7402.2021.04.06.
[12] Ali M,Bakhsh U.A vanishing complication of haemodialysis: dialysis disequilibrium syndrome[J].J Intensive Care Soc,2020,21(1):92-95. DOI: 10.1177/1751143718798585.
[13] Adapa S,Konala VM,Aeddula NR,et al.Dialysis disequilibrium syndrome: rare serious complication of hemodialysis and effective management[J].Cureus,2019,11(6):e5000. DOI: 10.7759/cureus.5000.
[14] Sanada H,Yamaguchi K,Miyake T.Dialysis disequilibrium syndrome in a patient with acute kidney injury on chronic kidney disease[J].Cureus,2021,13(6):e15608. DOI: 10.7759/cureus.15608.
[15] Raina R,Davenport A,Warady B,et al.Dialysis disequilibrium syndrome (DDS) in pediatric patients on dialysis: systematic review and clinical practice recommendations[J].Pediatr Nephrol,2022,37(2):263-274. DOI: 10.1007/s00467-021-05242-1.
[16] 郑金花,韦泽丰,王自强.血清BUN、S-100B蛋白、MBP水平与维持性血液透析患者透析失衡综合征发生风险间的关系及预测价值[J].国际检验医学杂志,2022,43(21):2612-2617. DOI: 10.3969/j.issn.1673-4130.2022.21.012.
[17] 蓝潞杭,蒋炫东,王茂峰,等.随机森林模型预测急性心肌梗死后急性肾损伤[J].中华急诊医学杂志,2021,30(4):491-495. DOI: 10.3760/cma.j.issn.1671-0282.2021.04.021.
[18] Bagshaw SM,Peets AD,Hameed M,et al.Dialysis disequilibrium syndrome: brain death following hemodialysis for metabolic acidosis and acute renal failure-a case report[J].BMC Nephrol,2004,5(8):9. DOI: 10.1186/1471-2369-5-9.
[19] Stahl JL,Whelan RS,Symons JM.Dialysis disequilibrium on CKRT: avoiding the steep slippery slope[J].Pediatr Nephrol,2021,36(9):2697-2702. DOI: 10.1007/s00467-021-05026-7.
[20] Genena KH,Ahmed S,Szerlip HM,et al.Half the V by 120: a practical approach to the prevention of the dialysis disequilibrium syndrome[J].Hemodial Int,2021,25(4):424-432. DOI: 10.1111/hdi.12938.
[21] Akizawa T,Nangaku M,Yonekawa T,et al.Efficacy and safety of daprodustat compared with darbepoetin alfa in japanese hemodialysis patients with anemia: a randomized, double-blind, phase 3 trial[J].Clin J Am Soc Nephrol,2020,15(8):1155-1165. DOI: 10.2215/CJN.16011219.
[22] 高丽华,邓春颖,孙妍,等.维持性血液透析的终末期肾病患者认知障碍及相关因素分析[J]. 中国煤炭工业医学杂志,2021,24(3):307-311. DOI: 10.11723/mtgyyx 1007-9564 202103018.
[23] Zhou Y,Chen XX,Zhang YF,et al.Roxadustat for dialysis patients with erythropoietin hypo-responsiveness: a single-center, prospective investigation[J].Intern Emerg Med,2021,16(8):2193-2199. DOI: 10.1007/s11739-021-02738-4.
[24] 李琳,王志宏,肖佳,等.维持性血液透析患者血红蛋白动态变化与预后关系的回顾性队列研究[J].中国实用内科杂志,2021,41(4):321-327. DOI: 10.19538/j.nk2021040112.
[25] 郭一丹,张春霞,田茹,等.中老年维持性血液透析患者认知功能损伤与全因死亡相关性的前瞻性队列研究[J].中华肾脏病杂志,2021,37(11):896-903. DOI: 10.3760/cma.j.cn441217-20201119-00136.
[26] 税灵,达静静,何平红,等.血小板/淋巴细胞比值与糖尿病合并维持性血液透析患者认知功能障碍的相关性[J].中华医学杂志,2021,101(10):722-726. DOI: 10.3760/cma.j.cn112137-20201202-03244.
