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：

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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, 張凌, 等. 連續性腎臟替代治療護理信息化系統的構建及應用. 中國血液凈化, 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, 劉儷芩, 趙宇亮, 等. 四川大學華西醫院連續性腎臟替代治療亞專業進修醫師培養舉措探討. 華西醫學, 2020, 35(8): 979-982.
7.	代明金, 王芳, 陳志文, 等. 信息化的巡回教學模式在連續性腎臟替代治療進修護士教學中的應用效果研究. 華西醫學, 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. 構建急性腎損傷患者連續性腎臟替代治療劑量達成模型. 中國衛生質量管理, 2022, 29(1): 74-81, 90.
14.	趙宇亮, 張凌, 付平. 提高腎臟病整體預后工作組急性腎損傷臨床實踐指南熱點解讀. 中華內科雜志, 2012, 51(12): 935-939.
15.	趙宇亮, 買紅霞, 付平. 連續性腎臟替代治療應用于急性腎損傷的時機選擇. 華西醫學, 2018, 33(7): 806-809.
16.	唐瑞, 唐雯, 王導新. 機器學習對創傷合并急性呼吸窘迫綜合征患者院內死亡的預測價值. 中華危重病急救醫學, 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.	湯陳琪, 李駿強, 徐達圓, 等. 機器學習和logistic回歸模型預測嚴重燒傷患者發生急性腎損傷的比較分析. 中華燒傷雜志, 2018, 34(6): 343-348.
19.	張婭峰. 基于機器學習的ICU連續腎臟替代治療干預預測模型研究. 廣州: 華南理工大學, 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, 張凌, 等. 連續性腎臟替代治療護理信息化系統的構建及應用. 中國血液凈化, 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, 劉儷芩, 趙宇亮, 等. 四川大學華西醫院連續性腎臟替代治療亞專業進修醫師培養舉措探討. 華西醫學, 2020, 35(8): 979-982.
7. 代明金, 王芳, 陳志文, 等. 信息化的巡回教學模式在連續性腎臟替代治療進修護士教學中的應用效果研究. 華西醫學, 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. 構建急性腎損傷患者連續性腎臟替代治療劑量達成模型. 中國衛生質量管理, 2022, 29(1): 74-81, 90.
14. 趙宇亮, 張凌, 付平. 提高腎臟病整體預后工作組急性腎損傷臨床實踐指南熱點解讀. 中華內科雜志, 2012, 51(12): 935-939.
15. 趙宇亮, 買紅霞, 付平. 連續性腎臟替代治療應用于急性腎損傷的時機選擇. 華西醫學, 2018, 33(7): 806-809.
16. 唐瑞, 唐雯, 王導新. 機器學習對創傷合并急性呼吸窘迫綜合征患者院內死亡的預測價值. 中華危重病急救醫學, 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. 湯陳琪, 李駿強, 徐達圓, 等. 機器學習和logistic回歸模型預測嚴重燒傷患者發生急性腎損傷的比較分析. 中華燒傷雜志, 2018, 34(6): 343-348.
19. 張婭峰. 基于機器學習的ICU連續腎臟替代治療干預預測模型研究. 廣州: 華南理工大學, 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.

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