Acute circulatory failure (ACF) is a clinical syndrome when the heart and circulatory
circulation cannot provide adequate blood supply to meet metabolic needs of the organs.
ACF affects 30%- 50% of intensive care unit (ICU) patients. Fluid resuscitation is
the primary treatment of ACF. However, it fails in a significant proportion (about
50%) of cases due to lack of clinically feasible non-invasive perfusion markers to
assess the efficacy of the fluid therapy. Unfortunately, unsuccessful fluid therapy
negatively affects patient outcome, increasing ICU length of stay and costs. Recent
studies show identifying Stressed Blood Volume (SBV) of the cardiovascular system
can be used to assess the potential efficacy of fluid therapy. The development of
the diagnostic method requires the identification of the central arterial pressure
curve based on the femoral arterial pressure, which is clinically available. This
central arterial pressure curve can be used to identify the cardiovascular system
parameters. In this study, the main goal was to develop a parameter-identification
method for the Tube-load model-based transfer function connecting the femoral and
central arterial pressure curve by using the so-called Physics-informed Neural Network
methodology, namely the Neural ODE method. The study presents the adaptation of the
Neural ODE method to the given parameter identification problem and the validation
of the developed identification method. The robustness of the developed identification
method was tested and used on a series of measurement data recorded in animal experiments.
Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND
license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
