☆ 4.7 Article

Assessment of hydration status using bioelectrical impedance vector analysis in critical patients with acute kidney injury

CLINICAL NUTRITION (2018)

Journal

CLINICAL NUTRITION

Volume 37, Issue 2, Pages 695-700

Publisher

CHURCHILL LIVINGSTONE

DOI: 10.1016/j.clnu.2017.02.016

Keywords

Acute kidney injury; Critical patient; Bioelectrical impedance analysis; Fluid status; Prognostic factor

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Abstract

Background & aims: The state of hyperhydration in critically ill patients with acute kidney injury (AKI) is associated with increased mortality. Bioelectrical impedance vector analysis (BIVA) appears to be a viable method to access the fluid status of critical patients but has never been evaluated in critical patients with AKI. The objective of this study is to evaluate the hydration status measured using BIVA in critical patients under intensive care at the time of AKI diagnosis and to correlate this measurement with mortality. Methods: We assessed the fluid status measured using BIVA in 224 critical patients at the time of AKI diagnosis and correlated it with mortality. To interpret the results, BIVA Software 2002 was used to plot the data from the patients studied on the 95% confidence ellipses of the RXc plane for comparisons between groups (non-survivors, survivors). Variables such as mechanical ventilation, vasoactive drug, and sepsis, among others, were collected. Results: The impedance vector analysis conducted using BIVA Software 2002 indicated changes in the body compositions of patients according to the 95% confidence ellipse between the vectors R/H and X-c/H of the group of survivors and the group of deceased patients. Hotelling's test (T-2 = 21.2) and the F test (F = 10.6) revealed significant differences (p < 0.001) between the two groups. These results demonstrate that patients who died presented with a greater hydration volume at the time of AKI diagnosis compared with those who survived. In addition to the hydration status measured using BIVA, the following were also correlated with death: diagnosis at hospitalization, APACHE II score, length of hospital stay, RIFLE score, maximum organ failure, sepsis type, hemoglobin, and AF. Conclusions: The fluid status assessment measured using BIVA significantly demonstrated the difference in hydration between survivors and non-survivors among critically ill patients with AKI. (C) 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

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Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach

Kyung Don Yoo, Junhyug Noh, Wonho Bae, Jung Nam An, Hyung Jung Oh, Harin Rhee, Eun Young Seong, Seon Ha Baek, Shin Young Ahn, Jang-Hee Cho, Dong Ki Kim, Dong-Ryeol Ryu, Sejoong Kim, Chun Soo Lim, Jung Pyo Lee, Sung Gyun Kim, Gang Jee Ko, Jung Tak Park, Tae Ik Chang, Sungjin Chung, Jung Pyo Lee, Dong Ki Kim, Sang Ho Lee, Bum Soon Choi, Jin Seok Jeon, Sangheon Song, Dae Eun Choi, Dong-Ryeol Ryu, Woo Kyung Jung

Summary: This study evaluated the impact of repeated fluid balance monitoring on the prognosis of patients with severe acute kidney injury. The findings suggest that a deep-learning model using a recurrent neural network with fluid balance monitoring can effectively predict prognosis in patients receiving continuous renal replacement therapy. This highlights the importance of fluid balance monitoring in this clinical population.

SCIENTIFIC REPORTS (2023)