Cardiac output estimated from an uncalibrated radial blood pressure waveform: validation in an in-silico-generated population - Bikia, Vasiliki; Rovas, Georgios; Stergiopulos, Nikolaos (Frontiers in Bioengineering and Biotechnology, 2023)
Background: Cardiac output (CO) is essential for patient management in critically ill patients. The state-of-the-art for CO monitoring bears limitations that pertain to the invasive nature of the method, high costs and associated complications. Hence, the determination of CO in a non-invasive, accurate, and reliable way remains an unmet need. The advent of wearable technologies has directed research towards the exploitation of wearable-sensed data to improve hemodynamical monitoring. Methods: We developed an artificial neural networks (ANN)-enabled modelling approach to estimate CO from radial blood pressure (BP) waveform. In silico data including a variety of arterial pulse waves and cardiovascular parameters from 3’818 virtual subjects were used for the analysis. Of particular interest was to investigate whether the uncalibrated, namely normalized between 0 and 1, radial BP waveform contains sufficient information to derive CO accurately in an in silico population. Specifically, a training/testing pipeline was adopted for the development of two ANN models using as input: the calibrated radial BP waveform (ANNcalradBP), or the uncalibrated radial BP waveform (ANNuncalradBP). Results: ANN models provided precise CO estimations across the extensive range of cardiovascular profiles, with accuracy being higher for the ANNcalradBP. Pearson’s correlation coefficient and limits of agreement were found to be equal to (0.98 and [-0.44, 0.53] L/min) and (0.95 and [-0.84, 0.73] L/min) for ANNcalradBP and ANNuncalradBP, respectively. The method’s sensitivity to major cardiovascular parameters, such as heart rate, aortic blood pressure, and total arterial compliance was evaluated. Discussion: The uncalibrated radial BP waveform contains sufficient information for accurately deriving CO in an in silico population of virtual subjects. Validation of our results using in vivo human data will verify the clinical utility of the proposed model, while it will enable research applications for the integration of the model in wearable sensing systems, such as smartwatches or other consumer devices.