Objective To systematically evaluate the risk prediction model for catheter-related bloodstream infection in hemodialysis patients and to provide references for selecting appropriate prediction models or developing new models. Methods We searched related literature in CINAHL, PubMed, Web of Science, Cochrane Library, Embase, Wanfang database, CNKI, VIP website, China and Chinese biomedical literature database from establishing the database to October 1, 2023. Two investigators independently screened the literature, extracted data, and analyzed the risk of bias and applicability of included literature using the prediction model risk of bias assessment tool (PROBAST). Results A total of seven articles were included. Age, combined diabetes, serum albumin level, hand hygiene and catheter retention time were the major predictors for catheter-related bloodstream infection. The area under the curve was 0.734 to 0.889, from which four models were calibrated. All studies were well applicable but had a risk of bias. Conclusion The risk prediction models of catheter-related bloodstream infection in hemodialysis patients have better prediction performance, but with higher risks of bias caused by methodological defects, such as improper processing of missing data, variable selection without appropriate methods, no mention of blindness, and others. Therefore, they cannot be directly applied in clinical practice yet. In the future, the existed models should be verified in-depth and extensively by prospective studies using large sample and multiple population to develop prediction models with excellent predictive performance and simple use.
ZHANG Wei
,
ZHAO Ruo-Bing
,
ZHOU Yan
,
HE Jian-Qiang
,
PEI Kun
,
LIU Xi-Yang
. Risk predictive models for catheter-associated bloodstream infection in hemodialysis patients: a systematic review[J]. Chinese Journal of Blood Purification, 2024
, 23(03)
: 204
-208
.
DOI: 10.3969/j.issn.1671-4091.2024.03.011
[1] Fisher M, Golestaneh L, Allon M, et al. Prevention of Bloodstream Infections in Patients Undergoing Hemodialysis[J]. Clin J Am Soc Nephrol, 2020,15(1):132-151.
[2] 金其庄, 王玉柱, 叶朝阳, 等. 中国血液透析用血管通路专家共识(第2版)[J]. 中国血液净化, 2019,18(06):365-381.
[3] 王瑞敏, 苗金红, 岳晓红. 基于Donabedian质量理论的护理干预在行血管通路术的血液净化患者中的应用效果分析[J]. 中华现代护理杂志, 2020,26(36):5090-5094.
[4] Schwanke A A, Danski M, Pontes L, et al. Central venous catheter for hemodialysis: incidence of infection and risk factors[J]. Rev Bras Enferm, 2018,71(3):1115-1121.
[5] Mielke D, Wittig A, Teichgraber U. Peripherally inserted central venous catheter (PICC) in outpatient and inpatient oncological treatment[J]. Support Care Cancer, 2020,28(10):4753-4760.
[6] 马曦立, 刘霞, 吴鑫, 等. 慢性肾功能衰竭血液透析导管相关性血流感染病原菌耐药性及其影响因素[J]. 中华医院感染学杂志, 2021,31(3):385-389.
[7] Grant S W, Collins G S, Nashef S. Statistical Primer: developing and validating a risk prediction model[J]. Eur J Cardiothorac Surg, 2018,54(2):203-208.
[8] Moons K G, de Groot J A, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist[J]. PLoS Med, 2014,11(10):e1001744.
[9] Moons K, Wolff R F, Riley R D, et al. PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration[J]. Ann Intern Med, 2019,170(1):W1-W33.
[10] Demirci R, Sahtiyanci B, Bakan A, et al. The predictors of catheter-related bloodstream infections in patients undergoing hemodialysis: A single center experience[J]. J Vasc Access, 2023,24(1):76-81.
[11] 王姗, 张志媛, 姚佳, 等. 慢性肾功能衰竭血液透析患者导管相关性血流感染预测模型的构建[J]. 中华现代护理杂志, 2022,28(23):3123-3127.
[12] 袁祥萍, 程振田, 刘桂香, 等. 长期血液透析导管相关性血流感染风险预测模型构建[J]. 中华医院感染学杂志, 2022,32(01):61-65.
[13] 李道新, 熊飞, 李红兵, 等. 血液透析患者导管相关性血流感染的危险因素及其预测模型构建[J]. 中华医院感染学杂志, 2023,33(03):368-371.
[14] 刘莉莉, 刘玉秋, 仰欣, 等. 血液透析患者带隧道带涤纶套导管相关性血流感染危险因素分析及预测模型构建与验证[J]. 中国血液净化, 2021,20(11):773-777.
[15] 施国婵, 马川寅, 孔曼丽, 等. 血透患者中心静脉导管相关血流感染风险评分系统的建立及验证[J]. 中国中西医结合肾病杂志, 2019,20(05):447-449.
[16] 刘亚敏, 赵培翔, 王宇飞, 等. 透析导管相关血流感染的危险因素及风险预测模型[J]. 中华肾脏病杂志, 2022,38(1):23-28.
[17] Debray T P, Damen J A, Snell K I, et al. A guide to systematic review and meta-analysis of prediction model performance[J]. BMJ, 2017,356:i6460.
[18] 冯沁祺, 彭博雅, 李雅儒, 等. 基于机器学习的2型糖尿病视网膜病变预测模型研究[J]. 中国中医药信息杂志, 2021,28(06):22-28.
[19] Su L T, Huang H C, Liu Y C, et al. The appropriate frequency of dressing for percutaneous central venous catheters in preventing catheter-related blood stream infection in NICU - A randomized controlled trial[J]. Pediatr Neonatol, 2021,62(3):292-297.
[20] 高志华. 血液透析患者中心静脉导管相关性血流感染的危险因素及耐药性分析[D]. 吉林大学, 2015.
[21] 赵茜芸, 丛静静, 鲁业芳, 等. 血液透析患者中心静脉导管相关性血流感染的病原菌分布与耐药性及相关因素分析[J]. 中华医院感染学杂志, 2017,27(21):4891-4894.
[22] Hu Y, Ling Y, Ye Y, et al. Analysis of risk factors of PICC-related bloodstream infection in newborns: implications for nursing care[J]. Eur J Med Res, 2021,26(1):80.
[23] 刘亚敏. 透析导管相关血流感染危险因素分析及风险预测模型[D]. 郑州大学, 2021.
