[an error occurred while processing this directive]
综述

维持性血液透析患者心血管事件风险预测模型的研究进展

  • 凌敏 ,
  • 王霄一 ,
  • 章晋 ,
  • 王婷
展开
  • 313002 湖州,1湖州师范学院护理学院
    313099 湖州,2湖州市第一人民医院肾内科

收稿日期: 2023-06-05

  修回日期: 2023-07-17

  网络出版日期: 2023-09-28

基金资助

浙江省医药卫生科技计划项目(2021KY1093);  湖州师范学院研究生科研创新项目(2023KYCX75)

Research progresses in the prediction models for risks of cardiovascular events in maintenance hemodialysis patients

  • LING Min ,
  • WANG Xiao-Yi ,
  • ZHANG Jin ,
  • WANG Ting
Expand

Received date: 2023-06-05

  Revised date: 2023-07-17

  Online published: 2023-09-28

摘要

通过对维持性血液透析患者心血管事件风险预测模型进行综述,总结现存风险预测模型的评估内容、预测效能,分析其科学性与局限性,为制定科学、精准的维持性血液透析心血管事件风险筛查工具提供参考依据。

本文引用格式

凌敏 , 王霄一 , 章晋 , 王婷 . 维持性血液透析患者心血管事件风险预测模型的研究进展[J]. 中国血液净化, 2023 , 22(10) : 772 -775 . DOI: 10.3969/j.issn.1671-4091.2023.10.012

Abstract

In this article, we review the prediction models for risks of cardiovascular events in maintenance hemodialysis patients, and summarize the evaluation content, prediction efficiency, scientific bases and limitations of the currently used risk prediction models, so as to provide references for the development of scientific and accurate prediction tools for risks of cardiovascular events in maintenance hemodialysis patients. 

参考文献

[1] GREENBERG K I, CHOI M J. Hemodialysis Emergencies: Core Curriculum 2021[J]. Am J Kidney Dis, 2021,77(5): 796-809.
[2] 陈香美. 中国肾脏病学发展的现状与未来[J]. 中华医学信息导报, 2021,36(5): 19.
[3] SARAN R, ROBINSON B, ABBOTT K C, et al. US Renal Data System 2019 Annual Data Report: Epidemiology of Kidney Disease in the United States[J]. Am J Kidney Dis, 2020,75(1 Suppl 1): A6-A7.
[4] KASISKE B L, ZEIER M G, CHAPMAN J R, et al. KDIGO clinical practice guideline for the care of kidney transplant recipients: a summary[J]. Kidney Int, 2010,77(4): 299-311.
[5] PIEPOLI M F, HOES A W, AGEWALL S, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR)[J]. Eur Heart J, 2016,37(29): 2315-2381.
[6] ARNETT D K, BLUMENTHAL R S, ALBERT M A, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines[J]. Circulation, 2019,140(11): e563-e595.
[7] 中国心血管病风险评估和管理指南[J]. 中国循环杂志, 2019,34(01): 4-28.
[8] 邱洁净, 唐雯桢, 莫新少. 术后肺部并发症风险预测模型的研究进展[J]. 护理研究, 2020,34(22): 4011-4014.
[9] MOONS K G, KENGNE A P, WOODWARD M, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker[J]. Heart, 2012,98(9): 683-690.
[10] TOLL D B, JANSSEN K J, VERGOUWE Y, et al. Validation, updating and impact of clinical prediction rules: a review[J]. J Clin Epidemiol, 2008,61(11): 1085-1094.
[11] YANG H, BAE S H, NAM H, et al. A risk prediction model for hepatocellular carcinoma after hepatitis B surface antigen seroclearance[J]. J Hepatol, 2022,77(3): 632-641.
[12] PARK J H, KIM E, SEOL E M, et al. Prediction Model for Screening Patients at Risk of Malnutrition After Gastric Cancer Surgery[J]. Ann Surg Oncol, 2021,28(8): 4471-4481.
[13] CHEN S, MA X, ZHOU X, et al. An updated clinical prediction model of protein-energy wasting for hemodialysis patients[J]. Front Nutr, 2022,9: 933745.
[14] GU Y, WU W, KONG C, et al. Development of prognostic prediction model to estimate mortality for frail oldest old: prospective cohort study[J]. J Gerontol A Biol Sci Med Sci, 2022.
[15] MARTINEZ-JAIMEZ P, ARMORA V M, FORERO C G, et al. Breast cancer-related lymphoedema: Risk factors and prediction model[J]. J Adv Nurs, 2022,78(3): 765-775.
[16] DENG X, HOU H, WANG X, et al. Development and validation of a nomogram to better predict hypertension based on a 10-year retrospective cohort study in China[J]. Elife, 2021,10.
[17] YANG H, BAE S H, NAM H, et al. A risk prediction model for hepatocellular carcinoma after hepatitis B surface antigen seroclearance[J]. J Hepatol, 2022,77(3): 632-641.
[18] ANDAUR N C, DAMEN J, TAKADA T, et al. Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques[J]. BMJ Open, 2020,10(11): e38832.
[19] YOUNG A J, HARE A, SUBRAMANIAN M, et al. Using Machine Learning to Make Predictions in Patients Who Fall[J]. J Surg Res, 2021,257: 118-127.
[20] ALBA A C, AGORITSAS T, WALSH M, et al. Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature[J]. JAMA, 2017,318(14): 1377-1384.
[21] BONNETT L J, SNELL K, COLLINS G S, et al. Guide to presenting clinical prediction models for use in clinical settings[J]. BMJ, 2019,365: l737.
[22] WILSON P W, D'AGOSTINO R B, LEVY D, et al. Prediction of coronary heart disease using risk factor categories[J]. Circulation, 1998,97(18): 1837-1847.
[23] DAWBER T R, MEADORS G F, MOORE F J. Epidemiological approaches to heart disease: the Framingham Study[J]. Am J Public Health Nations Health, 1951,41(3): 279-281.
[24] D'AGOSTINO R S, VASAN R S, PENCINA M J, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study[J]. Circulation, 2008,117(6): 743-753.
[25] MATSUBARA Y, KIMACHI M, FUKUMA S, et al. Development of a new risk model for predicting cardiovascular events among hemodialysis patients: Population-based hemodialysis patients from the Japan Dialysis Outcome and Practice Patterns Study (J-DOPPS)[J]. PLoS One, 2017,12(3): e173468.
[26] SUN L, JI Y, WANG Y, et al. Development and Internal Validation of a Prediction Model to Estimate the Probability of Left-Ventricular Diastolic Dysfunction in Stable Maintenance Hemodialysis Patients without Clinical Heart Failure[J]. Nephron, 2019,142(4): 301-310.
[27] 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.
[28] WANG Y, MIAO X, XIAO G, et al. Clinical Prediction of Heart Failure in Hemodialysis Patients: Based on the Extreme Gradient Boosting Method[J]. Front Genet, 2022,13: 889378.
[29] KAGIYAMA N, SHRESTHA S, FARJO P D, et al. Artificial Intelligence: Practical Primer for Clinical Research in Cardiovascular Disease[J]. J Am Heart Assoc, 2019,8(17): e12788.
文章导航

/

[an error occurred while processing this directive]