Key result
A quantitative mathematical grey-box model successfully extracted features describing myogenic and tubuloglomerular feedback responses, separating data from normotensive and hypertensive Dahl rats.
A novel mathematical 'grey-box' model can characterize renal autoregulatory dynamics and differentiate between normotensive and hypertensive rat models.
Hypothesis-generating for renal autoregulation modeling in hypertension; leaves open human validation and clinical translation.
A method is proposed in this paper which allows characterization of renal autoregulatory dynamics and efficiency using quantitative mathematical methods. Based on data from rat experiments, where arterial blood pressure and renal blood flow are measured, a quantitative model for renal blood flow dynamics is constructed. The mathematical structure for the dynamics is chosen as a "grey-box model," i.e. the model structure is inspired from physiology, but the actual parameters is found by numerical methods. Based on a number of experiments, features are extracted from the estimated parameters, which describe myogenic responses and tubuloglomerular feedback responses separately. The method is applied to data from normo- and hypertensive Dahl rats, and a discriminator that separates data from normotensive Dahl R rats and hypertensive Dahl S rats is constructed.
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Knudsen et al. (2004) studied Hypertension. Hypertension (Dahl S rats) vs. Normotension (Dahl R rats) was evaluated on Characterization of renal autoregulatory dynamics (myogenic and tubuloglomerular feedback responses). A quantitative mathematical grey-box model successfully extracted features describing myogenic and tubuloglomerular feedback responses, separating data from normotensive and hypertensive Dahl rats.
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