Abstract
Following renal transplantation a patient has to be monitored closely in order to detect the onset of a rejection episode as soon as possible. The decision whether to enhance immuno-suppressive therapy - with its severe side effects - to suppress the rejection episode is often difficult, especially for the less experienced clinician. Therefore, objective methods of decision support are needed. The decision is based on observing the time course of clinical parameters like serum creatinine, urea, urine volume, urinary sodium, osmolarity, and body weight.
Our contribution deals with three questions:
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(1)
What is the distribution of rejection episodes over time elapsed since transplantation?
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(2)
How can current values of clinical parameters be predicted from past observations and the discrepancies between prediction and observation be used for the diagnosis of an episode?
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(3)
Which of the variables have the highest diagnostic value?
For (1) we use a nonparametric kernel method with automatic bandwidth choice by cross-validation. For (2) we developed a nonparametric kernel regression estimator with one-sided kernels. For (3) we employ logistic regression and other multivariate discriminant methods. Future prospects are to implement the developed Fortran programs on a microcomputer in order to supply online decision support.
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Müller, HG., Müller, T., Lohrengel, C., Lange, H. (1987). Computer Aided Decision Support for Post-Transplant Renal Allograft Rejection. In: Janßen, R., Opelz, G. (eds) Acquisition, Analysis and Use of Clinical Transplant Data. Lecture Notes in Medical Informatics, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-51003-8_13
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DOI: https://doi.org/10.1007/978-3-642-51003-8_13
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