| [1] |
Wu J, Li X, Zhang H, et al. Development and validation of a prediction model for all-cause mortality in maintenance dialysis patients: a multicenter retrospective cohort study [J]. Ren Fail, 2024, 46(1): 2322039.
|
| [2] |
Fadel FI, Salah DM, Mawla MAA, et al. Assessment of volume status of pediatric hemodialysis patients [J]. Pediatr Nephrol, 2024, 39(10): 3057-3066.
|
| [3] |
许厅,辜雅静,刘俪婷,等. 血液透析患者容量评估方法的研究进展[J]. 中国临床新医学,2025, 18(4): 462-466.
|
| [4] |
Kearney DA, Reisinger N, Lohani S. Integrative volume status assessment [J]. POCUS J, 2022, 7(Kidney): 65-77.
|
| [5] |
肖佳仪,孙贝蒂,高康,等. 维持性血液透析患者干体质量管理的最佳证据总结[J]. 中国血液净化,2025, 24(11): 889-894.
|
| [6] |
Kanda E, Epureanu BI, Adachi T, et al. Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan [J]. PLoS One, 2020, 15(5): e0233491.
|
| [7] |
Huang JC, Tsai YC, Wu PY, et al. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method [J]. Comput Methods Programs Biomed, 2020, 195: 105536.
|
| [8] |
Bou-Matar R, Dell KM, Bobrowski A. Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis [J]. Sci Rep, 2023, 13(1): 19105.
|
| [9] |
Schneider E, Maimon N, Hasidim A, et al. Can dialysis patients identify and diagnose pulmonary congestion using self-lung ultrasound? [J]. J Clin Med, 2023, 12(11): 3829.
|
| [10] |
Arthur L, Prodhan P, Blaszak R, et al. Evaluation of lung ultrasound to detect volume overload in children undergoing dialysis [J]. Pediatr Nephrol, 2023, 38(7): 2165-2170.
|
| [11] |
Mongodi S, Luca DD, Colombo A, et al. Quantitative lung ultrasound: technical aspects and clinical applications [J]. Anesthesiology, 2021, 134(6): 949-965.
|
| [12] |
Tan GFL, Du T, Liu JS, et al. Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients [J]. BMC Nephrol, 2022, 23(1): 1-13.
|
| [13] |
Zhou J, An Q, Hou X. Dynamic changes and prognosis of pulmonary congestion by lung ultrasound in hemodialysis patients: a systematic review and meta-analysis [J]. Med Ultrason, 2022, 25(2): 208-215.
|
| [14] |
Demi L, Wolfram F, Klersy C, et al. New international guidelines and consensus on the use of lung ultrasound [J]. J Ultrasound Med, 2022, 42(2): 309-344.
|
| [15] |
Bellido D, García-García C, Talluri A, et al. Future lines of research on phase angle: strengths and limitations [J]. Rev Endocr Metab Disord, 2023, 24(3): 563-583.
|
| [16] |
Kristuli L, Lai S, Perrotta AM, et al. Bioelectrical impedance vector analysis and brain natriuretic peptide in the evaluation of patients with chronic kidney disease in hemodialytic treatment [J]. Kidney Blood Press Res, 2022, 48(1): 1-6.
|
| [17] |
Nieves-Anaya I, Várgas MB, García O, et al. Effect of oral nutritional supplementation combined with impedance vectors for dry weight adjustment on the nutritional status, hydration status and quality of life in patients on chronic hemodialysis: a pilot study [J]. Clin Nutr ESPEN, 2022, 54: 23-33.
|
| [18] |
Sethanant S, Sethakarun S, Sutachard P, et al. Effect of bioelectrical impedance analysis-guided dry weight adjustment, in comparison to standard clinical-guided, on the sleep quality of chronic haemodialysis patients (BEDTIME study): a randomised controlled trial [J]. BMC Nephrol, 2019, 20(1): 211.
|
| [19] |
Burkhalter DA, Cartellà A, Cozzo D, et al. Obstructive sleep apnea in the hemodialysis population: are clinicians putting existing scientific evidence into practice? [J]. Front Nephrol, 2024, 4: 1394990.
|
| [20] |
Haroon S, Tan JN, Lau T, et al. Segmental bioimpedance in pregnant end stage renal failure patient for dry weight titration and volume management (case report) [J]. BMC Nephrol, 2023, 24(1): 308.
|
| [21] |
Sandys V, Sexton DJ, O′Seaghdha CM. Artificial intelligence and digital health for volume maintenance in hemodialysis patients [J]. Hemodial Int, 2022, 26(4): 480-495.
|
| [22] |
Schoutteten MK, Vranken J, Lee S, et al. Towards personalized fluid monitoring in haemodialysis patients: thoracic bioimpedance signal shows strong correlation with fluid changes, a cohort study [J]. BMC Nephrol, 2020, 21(1): 264.
|
| [23] |
Schneditz D, Roob J, Oswald M, et al. Nature and rate of vascular refilling during hemodialysis and ultrafiltration [J]. Kidney Int, 1992, 42(6): 1425-1433.
|
| [24] |
Maeda A, Baldwin I, Spano S, et al. Relative blood volume monitoring during continuous renal replacement therapy: a prospective observational study [J]. Blood Purif, 2024, 53(11-12): 884-892.
|
| [25] |
Chaudhuri S, Han H, Monaghan C, et al. Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure [J]. BMC Nephrol, 2021, 22(1): 274.
|
| [26] |
Akl AI, Sobh MA, Enab YM, et al. Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy [J]. Am J Kidney Dis, 2001, 38(6): 1277-1283.
|
| [27] |
Nobakht E, Raru W, Dadgar S, et al. Precision dialysis: leveraging big data and artificial intelligence [J]. Kidney Med, 2024, 6(9): 100868.
|
| [28] |
Mahdavi S, Anthony NM, Sikaneta T, et al. Multiomics and artificial intelligence for personalized nutritional management of diabetes in patients undergoing peritoneal dialysis [J]. Adv Nutr, 2025, 16(3): 100378.
|
| [29] |
Hueso M, Álvarez R, Mar D, et al. Is generative artificial intelligence the next step toward a personalized hemodialysis? [J]. Rev Invest Clin, 2023, 75(6): 309-317.
|
| [30] |
Simeri A, Pezzi G, Arena R, et al. Artificial intelligence in chronic kidney diseases: methodology and potential applications [J]. Int Urol Nephrol, 2025, 57(1): 159-168.
|
| [31] |
Guo X, Zhou W, Lu Q, et al. Assessing dry weight of hemodialysis patients via sparse Laplacian regularized RVFL neural network with L2,1-norm [J]. Biomed Res Int, 2021, 2021: 6627650.
|
| [32] |
Inoue H, Oya M, Aizawa M, et al. Predicting dry weight change in hemodialysis patients using machine learning [J]. BMC Nephrol, 2023, 24(1): 196.
|
| [33] |
Lee H, Yun D, Yoo J, et al. Deep learning model for real-time prediction of intradialytic hypotension [J]. Clin J Am Soc Nephrol, 2021, 16(3): 396-406.
|
| [34] |
Li Z, Hao S, Shi S, et al. An explainable machine learning model for early warning of hypertensive and hypotensive anomalies in maintenance hemodialysis patients [J]. BMC Nephrol, 2025, 26(1): 318.
|