https://orcid.org/0000-0001-9742-6662
Abstract
This paper presents a virtual patient generator (VPG) intended to be used for preclinical in silico evaluation of autonomous vasopressor administration algorithms in the setting of experimentally induced vasoplegia. Our VPG consists of two main components: (i) a mathematical model that replicates physiological responses to experimental vasoplegia (induced by sodium nitroprusside (SNP)) and vasopressor resuscitation via phenylephrine (PHP) and (ii) a parameter vector sample generator in the form of a multidimensional probability density function (PDF) using which the parameters characterizing the mathematical model can be sampled. We developed and validated a mathematical model capable of predicting physiological responses to the administration of SNP and PHP. Then, we developed a parameter vector sample generator using a collective variational inference method. In a blind testing, the VPG developed by combining the two could generate a large number of realistic virtual patients (VPs), which could simulate physiological responses observed in all the experiments: on the average, 98.1% and 74.3% of the randomly generated VPs were physiologically legitimate and adequately replicated the test subjects, respectively, and 92.4% of the experimentally observed responses could be covered by the envelope formed by the subject-replicating VPs. In sum, the VPG developed in this paper may be useful for preclinical in silico evaluation of autonomous vasopressor administration algorithms.
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