Informatization and artificial intelligence in continuous renal replacement therapy_West China Medical Journal

Authors：

ZHAO Yuliang , WEI Wei , ZHANG Ling ,  FU Ping

Department of Nephrology / Kidney Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

FU Ping, Email: fupinghx@163.com

Keywords：

Continuous renal replacement therapy; acute kidney injury; informatization; artificial intelligence

DOI：

10.7507/1002-0179.202206031

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Continuous renal replacement therapy (CRRT) is one of the major treatments for critically ill patients. With the development of information technology, the informatization and artificial intelligent of CRRT has received wide attention, which has promoted the optimization of CRRT in terms of workflow, teaching method as well as scientific research. Benefiting from the big data generated, artificial intelligence is expected to be applied in the precision treatment, quality control, timing of intervention, as well as prognosis assessment in severe AKI, so as to ultimately improve the therapeutic effect of CRRT among critically ill patients. This paper summarizes the information construction of CRRT and the research progress of artificial intelligence, which can be used as a reference for practitioners in kidney disease, critical medicine, emergency medicine and other related fields.

Citation： ZHAO Yuliang, WEI Wei, ZHANG Ling, FU Ping. Informatization and artificial intelligence in continuous renal replacement therapy. West China Medical Journal, 2022, 37(7): 1066-1069. doi: 10.7507/1002-0179.202206031 Copy

1.	Soranno DE, Deep A, Gist KM, et al. Editorial: acute kidney injury: it’s not just acute, and it’s not just the kidneys. Front Pediatr, 2021, 9: 792210.
2.	Samoni S, Husain-Syed F, Villa G, Ronco C. Continuous renal replacement therapy in the critically ill patient: from garage technology to artificial intelligence. J Clin Med, 2021, 11(1): 172.
3.	Loftus TJ, Shickel B, Ozrazgat-Baslanti T, et al. Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol, 2022, 18(7): 452-465.
4.	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.
5.	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.
6.	李玲, 刘俪芩, 赵宇亮, 等. 四川大学华西医院连续性肾脏替代治疗亚专业进修医师培养举措探讨. 华西医学, 2020, 35(8): 979-982.
7.	代明金, 王芳, 陈志文, 等. 信息化的巡回教学模式在连续性肾脏替代治疗进修护士教学中的应用效果研究. 华西医学, 2020, 35(6): 701-704.
8.	Clark WR, Villa G, Neri M, et al. Advances in machine technology. Contrib Nephrol, 2018, 194: 80-89.
9.	Bagshaw SM, Chakravarthi MR, Ricci Z, et al. Precision continuous renal replacement therapy and solute control. Blood Purif, 2016, 42(3): 238-247.
10.	See E, Ronco C, Bellomo R. The future of continuous renal replacement therapy. Semin Dial, 2021, 34(6): 576-585.
11.	Neyra JA, Tolwani A. CRRT prescription and delivery of dose. Semin Dial, 2021, 34(6): 432-439.
12.	Mottes TA, Goldstein SL, Basu RK. Process based quality improvement using a continuous renal replacement therapy dashboard. BMC Nephrol, 2019, 20(1): 17.
13.	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.
14.	赵宇亮, 张凌, 付平. 提高肾脏病整体预后工作组急性肾损伤临床实践指南热点解读. 中华内科杂志, 2012, 51(12): 935-939.
15.	赵宇亮, 买红霞, 付平. 连续性肾脏替代治疗应用于急性肾损伤的时机选择. 华西医学, 2018, 33(7): 806-809.
16.	唐瑞, 唐雯, 王导新. 机器学习对创伤合并急性呼吸窘迫综合征患者院内死亡的预测价值. 中华危重病急救医学, 2022, 34(3): 260-264.
17.	Clark WR, Garzotto F, Neri M, et al. Data analytics for continuous renal replacement therapy: historical limitations and recent technology advances. Int J Artif Organs, 2016, 39(8): 399-406.
18.	汤陈琪, 李骏强, 徐达圆, 等. 机器学习和logistic回归模型预测严重烧伤患者发生急性肾损伤的比较分析. 中华烧伤杂志, 2018, 34(6): 343-348.
19.	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.
20.	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.
21.	Zhao Y, Yang L, Zhang L, et al. A combined biomarker of urinary neutrophil gelatinase-associated lipocalin and serum creatinine for the prediction of acute kidney injury: what else can we know?. J Crit Care, 2019, 54: 280-281.
22.	Yang T, Sun S, Zhao Y, et al. Biomarkers upon discontinuation of renal replacement therapy predict 60-day survival and renal recovery in critically ill patients with acute kidney injury. Hemodial Int, 2018, 22(1): 56-65.
23.	Kang MW, Kim J, Kim DK, et al. Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit Care, 2020, 24(1): 42.
24.	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.
25.	Pattharanitima P, Vaid A, Jaladanki SK, et al. Comparison of approaches for prediction of renal replacement therapy-free survival in patients with acute kidney injury. Blood Purif, 2021, 50(4/5): 621-627.
26.	Soranno DE, Bihorac A, Goldstein SL, et al. Artificial intelligence for AKI! Now: let’s not await plato’s utopian republic. Kidney360, 2021, 3(2): 376-381.

1. Soranno DE, Deep A, Gist KM, et al. Editorial: acute kidney injury: it’s not just acute, and it’s not just the kidneys. Front Pediatr, 2021, 9: 792210.
2. Samoni S, Husain-Syed F, Villa G, Ronco C. Continuous renal replacement therapy in the critically ill patient: from garage technology to artificial intelligence. J Clin Med, 2021, 11(1): 172.
3. Loftus TJ, Shickel B, Ozrazgat-Baslanti T, et al. Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol, 2022, 18(7): 452-465.
4. 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.
5. 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.
6. 李玲, 刘俪芩, 赵宇亮, 等. 四川大学华西医院连续性肾脏替代治疗亚专业进修医师培养举措探讨. 华西医学, 2020, 35(8): 979-982.
7. 代明金, 王芳, 陈志文, 等. 信息化的巡回教学模式在连续性肾脏替代治疗进修护士教学中的应用效果研究. 华西医学, 2020, 35(6): 701-704.
8. Clark WR, Villa G, Neri M, et al. Advances in machine technology. Contrib Nephrol, 2018, 194: 80-89.
9. Bagshaw SM, Chakravarthi MR, Ricci Z, et al. Precision continuous renal replacement therapy and solute control. Blood Purif, 2016, 42(3): 238-247.
10. See E, Ronco C, Bellomo R. The future of continuous renal replacement therapy. Semin Dial, 2021, 34(6): 576-585.
11. Neyra JA, Tolwani A. CRRT prescription and delivery of dose. Semin Dial, 2021, 34(6): 432-439.
12. Mottes TA, Goldstein SL, Basu RK. Process based quality improvement using a continuous renal replacement therapy dashboard. BMC Nephrol, 2019, 20(1): 17.
13. 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.
14. 赵宇亮, 张凌, 付平. 提高肾脏病整体预后工作组急性肾损伤临床实践指南热点解读. 中华内科杂志, 2012, 51(12): 935-939.
15. 赵宇亮, 买红霞, 付平. 连续性肾脏替代治疗应用于急性肾损伤的时机选择. 华西医学, 2018, 33(7): 806-809.
16. 唐瑞, 唐雯, 王导新. 机器学习对创伤合并急性呼吸窘迫综合征患者院内死亡的预测价值. 中华危重病急救医学, 2022, 34(3): 260-264.
17. Clark WR, Garzotto F, Neri M, et al. Data analytics for continuous renal replacement therapy: historical limitations and recent technology advances. Int J Artif Organs, 2016, 39(8): 399-406.
18. 汤陈琪, 李骏强, 徐达圆, 等. 机器学习和logistic回归模型预测严重烧伤患者发生急性肾损伤的比较分析. 中华烧伤杂志, 2018, 34(6): 343-348.
19. 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.
20. 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.
21. Zhao Y, Yang L, Zhang L, et al. A combined biomarker of urinary neutrophil gelatinase-associated lipocalin and serum creatinine for the prediction of acute kidney injury: what else can we know?. J Crit Care, 2019, 54: 280-281.
22. Yang T, Sun S, Zhao Y, et al. Biomarkers upon discontinuation of renal replacement therapy predict 60-day survival and renal recovery in critically ill patients with acute kidney injury. Hemodial Int, 2018, 22(1): 56-65.
23. Kang MW, Kim J, Kim DK, et al. Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit Care, 2020, 24(1): 42.
24. 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.
25. Pattharanitima P, Vaid A, Jaladanki SK, et al. Comparison of approaches for prediction of renal replacement therapy-free survival in patients with acute kidney injury. Blood Purif, 2021, 50(4/5): 621-627.
26. Soranno DE, Bihorac A, Goldstein SL, et al. Artificial intelligence for AKI! Now: let’s not await plato’s utopian republic. Kidney360, 2021, 3(2): 376-381.

Previous Article
Repetitive transcranial magnetic stimulation for insomnia: an overview of systematic reviews
Next Article
Management throughout the whole course of acute kidney injury

West China Medical Journal

Informatization and artificial intelligence in continuous renal replacement therapy

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content