How far is the era of artificial intelligence for continuous renal replacement therapy?_West China Medical Journal

Authors：

LIU Tong ^1,2 ,  BAI Ming ¹ ,  SUN Shiren ¹

1. Department of Nephrology, the First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi 710032, P. R. China;
2. Medical School of Yan’an University, Yan’an, Shaanxi 716000, P. R. China;

Corresponding author：

BAI Ming, Email: mingbai1983@126.com; SUN Shiren, Email: sunshiren@medmail.com.cn

Keywords：

Continuous renal replacement therapy; artificial intelligence; acute kidney injury

DOI：

10.7507/1002-0179.202406096

Video：

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Continuous renal replacement therapy (CRRT) is one of the important therapeutic techniques for critically ill patients. In recent years, the field of artificial intelligence has developed rapidly and has been widely applied in manufacturing, automotive, and even daily life. The development and application of artificial intelligence in the medical field are also advancing rapidly, and artificial intelligence radiographic imaging result judgment, pathological result judgment, patient prognosis prediction are gradually being used in clinical practice. The development of artificial intelligence in the field of CRRT has also made rapid progress. Therefore, this article will elaborate on the current application status of artificial intelligence in CRRT, as well as its future prospects in CRRT, so as to provide a reference for understanding the application of artificial intelligence in CRRT.

Citation： LIU Tong, BAI Ming, SUN Shiren. How far is the era of artificial intelligence for continuous renal replacement therapy?. West China Medical Journal, 2024, 39(7): 1019-1022. doi: 10.7507/1002-0179.202406096 Copy

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2.	Samoni S, Husain-Syed F, Villa G, et al. Continuous renal replacement therapy in the critically ill patient: from garage technology to artificial intelligence. J Clin Med, 2021, 11(1): 172.
3.	Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl, 2012, 2(1): 1-138.
4.	Hoste EA, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med, 2015, 41(8): 1411-1423.
5.	Luo X, Jiang L, Du B, et al. A comparison of different diagnostic criteria of acute kidney injury in critically ill patients. Crit Care, 2014, 18(4): R144.
6.	沈文玮. 论当代人工智能的技术特点及其对劳动者的影响. 当代经济研究, 2018(4): 63-69.
7.	余乃忠. 理解为自然历史过程的人工智能. 中州学刊, 2020(10): 122-129.
8.	全耀. 浅谈人工智能的发展史. 现代信息科技, 2019, 3(6): 80-81, 84.
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10.	Zarbock A, Kellum JA, Schmidt C, et al. Effect of early vs delayed initiation of renal replacement therapy on mortality in critically ill patients with acute kidney injury: the ELAIN randomized clinical trial. JAMA, 2016, 315(20): 2190-2199.
11.	Guru PK, Singh TD, Passe M, et al. Derivation and validation of a search algorithm to retrospectively identify CRRT initiation in the ECMO patients. Appl Clin Inform, 2016, 7(2): 596-603.
12.	Roy S, Mincu D, Loreaux E, et al. Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing. J Am Med Inform Assoc, 2021, 28(9): 1936-1946.
13.	张娅峰. 基于机器学习的 ICU 连续肾脏替代治疗干预预测模型研究. 广州: 华南理工大学, 2020.
14.	Kang MW, Kim S, Kim YC, et al. Machine learning model to predict hypotension after starting continuous renal replacement therapy. Sci Rep, 2021, 11(1): 17169.
15.	肖桂林. 基于机器学习算法预测行连续性肾脏替代治疗的急性肾损伤患者死亡率. 南昌: 南昌大学医学部, 2023.
16.	Yoo KD, Noh J, Bae W, et al. Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach. Sci Rep, 2023, 13(1): 4605.
17.	Suppadungsuk S, Thongprayoon C, Miao J, et al. Exploring the potential of chatbots in critical care nephrology. Medicines (Basel), 2023, 10(10): 58.
18.	Zhang L, Baldwin I, Zhu G, et al. Automated electronic monitoring of circuit pressures during continuous renal replacement therapy: a technical report. Crit Care Resusc, 2015, 17(1): 51-54.
19.	唐雪, 李森淼, 张凌, 等. 连续性肾脏替代治疗护理信息化系统的构建及应用. 中国血液净化, 2022, 21(4): 300-304.
20.	Chen H, Ma Y, Hong N, et al. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Med Inform Decis Mak, 2021, 21(Suppl 2): 126.
21.	李墨奇, 伍薇, 何文昌, 等. 构建急性肾损伤患者连续性肾脏替代治疗剂量达成模型. 中国卫生质量管理, 2022, 29(1): 74-81,90.
22.	郑洁皎, 高文. 数字医疗带给老年康复的挑战. 华西医学, 2023, 38(6): 810-814.
23.	姚鹏, 唐时元, 蒋耀文, 等. 人工智能在急诊医学中的应用现状与展望. 华西医学, 2022, 37(11): 1601-1606.
24.	Hammouda N, Neyra JA. Can artificial intelligence assist in delivering continuous renal replacement therapy?. Adv Chronic Kidney Dis, 2022, 29(5): 439-449.
25.	Liu LJ, Takeuchi T, Chen J, et al. Artificial intelligence in continuous kidney replacement therapy. Clin J Am Soc Nephrol, 2023, 18(5): 671-674.

1. 陈香美. 血液净化标准操作规程. 北京: 人民卫生出版社, 2021.
2. Samoni S, Husain-Syed F, Villa G, et al. Continuous renal replacement therapy in the critically ill patient: from garage technology to artificial intelligence. J Clin Med, 2021, 11(1): 172.
3. Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl, 2012, 2(1): 1-138.
4. Hoste EA, Bagshaw SM, Bellomo R, et al. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med, 2015, 41(8): 1411-1423.
5. Luo X, Jiang L, Du B, et al. A comparison of different diagnostic criteria of acute kidney injury in critically ill patients. Crit Care, 2014, 18(4): R144.
6. 沈文玮. 论当代人工智能的技术特点及其对劳动者的影响. 当代经济研究, 2018(4): 63-69.
7. 余乃忠. 理解为自然历史过程的人工智能. 中州学刊, 2020(10): 122-129.
8. 全耀. 浅谈人工智能的发展史. 现代信息科技, 2019, 3(6): 80-81, 84.
9. Wald R, Bagshaw SM, STARRT-AKI Investigators. Timing of initiation of renal-replacement therapy in acute kidney injury. Reply. N Engl J Med, 2020, 383(18): 1797-1798.
10. Zarbock A, Kellum JA, Schmidt C, et al. Effect of early vs delayed initiation of renal replacement therapy on mortality in critically ill patients with acute kidney injury: the ELAIN randomized clinical trial. JAMA, 2016, 315(20): 2190-2199.
11. Guru PK, Singh TD, Passe M, et al. Derivation and validation of a search algorithm to retrospectively identify CRRT initiation in the ECMO patients. Appl Clin Inform, 2016, 7(2): 596-603.
12. Roy S, Mincu D, Loreaux E, et al. Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing. J Am Med Inform Assoc, 2021, 28(9): 1936-1946.
13. 张娅峰. 基于机器学习的 ICU 连续肾脏替代治疗干预预测模型研究. 广州: 华南理工大学, 2020.
14. Kang MW, Kim S, Kim YC, et al. Machine learning model to predict hypotension after starting continuous renal replacement therapy. Sci Rep, 2021, 11(1): 17169.
15. 肖桂林. 基于机器学习算法预测行连续性肾脏替代治疗的急性肾损伤患者死亡率. 南昌: 南昌大学医学部, 2023.
16. Yoo KD, Noh J, Bae W, et al. Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach. Sci Rep, 2023, 13(1): 4605.
17. Suppadungsuk S, Thongprayoon C, Miao J, et al. Exploring the potential of chatbots in critical care nephrology. Medicines (Basel), 2023, 10(10): 58.
18. Zhang L, Baldwin I, Zhu G, et al. Automated electronic monitoring of circuit pressures during continuous renal replacement therapy: a technical report. Crit Care Resusc, 2015, 17(1): 51-54.
19. 唐雪, 李森淼, 张凌, 等. 连续性肾脏替代治疗护理信息化系统的构建及应用. 中国血液净化, 2022, 21(4): 300-304.
20. Chen H, Ma Y, Hong N, et al. Early warning of citric acid overdose and timely adjustment of regional citrate anticoagulation based on machine learning methods. BMC Med Inform Decis Mak, 2021, 21(Suppl 2): 126.
21. 李墨奇, 伍薇, 何文昌, 等. 构建急性肾损伤患者连续性肾脏替代治疗剂量达成模型. 中国卫生质量管理, 2022, 29(1): 74-81,90.
22. 郑洁皎, 高文. 数字医疗带给老年康复的挑战. 华西医学, 2023, 38(6): 810-814.
23. 姚鹏, 唐时元, 蒋耀文, 等. 人工智能在急诊医学中的应用现状与展望. 华西医学, 2022, 37(11): 1601-1606.
24. Hammouda N, Neyra JA. Can artificial intelligence assist in delivering continuous renal replacement therapy?. Adv Chronic Kidney Dis, 2022, 29(5): 439-449.
25. Liu LJ, Takeuchi T, Chen J, et al. Artificial intelligence in continuous kidney replacement therapy. Clin J Am Soc Nephrol, 2023, 18(5): 671-674.

West China Medical Journal

How far is the era of artificial intelligence for continuous renal replacement therapy?

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