NDLI: Prediction of intradialytic morbid events in hemodialysis patients by monitoring the second derivative of relative blood volume

Content Provider	IEEE Xplore Digital Library
Author	Fansan Zhu Kotanko, P. Thijssen, S. Levin, N.W.
Copyright Year	2012
Description	Author affiliation: Renal Res. Inst., New York, NY, USA (Fansan Zhu; Kotanko, P.; Thijssen, S.; Levin, N.W.)
Abstract	Intradialytic morbid events (IME) are a major issue affecting hemodialysis (HD) patients. Change in relative blood volume (RBV) reflects change in plasma refilling rate (PRR) and variability of ultrafiltration rate (UFR). However, degree of fluid overload influences this relationship. We propose a method using a second derivative of RBV (SDRBV) model to detect decrease in PRR during HD which is a major factor associated with IME. Forty-five HD patients (age 55.7±14 years, height 167.7±10 cm, Pre HD weight 79.3±16.6 kg) were studied with a total of 245 treatments, with IME occurring in 124 treatments. Predictions of IME with the SDRBV model showed 92 % sensitivity for 124 IME and 43 % specificity of 121 non IME treatments respectively. The average time between prediction and IMEs was -30.8±36 minutes. A high positive predictive value was due to 1) using a low-pass filter to reduce possible interferences in the RBV curve and 2) use of 5 minutes sample frequency to apply the SDRBV model. These results indicate that the SDRBV model is a useful tool in clinical practice to predict and prevent IME.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	1226
Ending Page	1229
File Size	808154
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6346158
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blood High definition video Monitoring Plasmas Biomedical monitoring Sensitivity Mathematical model
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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