Literature DB >> 24813681

Time-series analysis in the medical domain: a study of Tacrolimus administration and influence on kidney graft function.

Vladimir Kurbalija1, Miloš Radovanović2, Mirjana Ivanović3, Danilo Schmidt4, Gabriela Lindemann von Trzebiatowski5, Hans-Dieter Burkhard6, Carl Hinrichs7.   

Abstract

There exists a major concern regarding toxic effects of immunosuppressive medication on the kidney graft during post-transplant care, with observed variation in individual susceptibility to adverse drug effects amongst patients. To date, there has been no possibility to identify susceptible patients prospectively. This study analyzes medical data which includes time series of measures of renal function and trough levels of immunosuppressive drug Tacrolimus, with the main aim of identifying patients susceptible to drug toxicity. We evaluate a plethora of time-series distance measures, determining their appropriateness to the domain based on two criteria: (1) preserving the expected correlations between distances, and (2) ability to detect the expected patterns of interaction between immunosuppressive drug levels and renal function. Besides identifying the most suitable time-series distance measures, we observed that the majority of patients do not exhibit an association between impaired graft function and higher Tacrolimus dosing. On the other hand, the minority of patients determined most sensitive to varying Tacrolimus levels showed a strong tendency to prefer low Tacrolimus dosing.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Creatinine; Distance measures; Glomerular filtration rate; Kidney transplantation; Tacrolimus; Time series

Mesh:

Substances:

Year:  2014        PMID: 24813681     DOI: 10.1016/j.compbiomed.2014.04.007

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  3 in total

1.  Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression.

Authors:  Li-Chen Cheng; Ya-Han Hu; Shr-Han Chiou
Journal:  J Med Syst       Date:  2017-04-11       Impact factor: 4.460

2.  Estimation of COVID-19 prevalence in Italy, Spain, and France.

Authors:  Zeynep Ceylan
Journal:  Sci Total Environ       Date:  2020-04-22       Impact factor: 7.963

3.  Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections.

Authors:  Maher Ala'raj; Munir Majdalawieh; Nishara Nizamuddin
Journal:  Infect Dis Model       Date:  2020-12-03
  3 in total

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