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Session: Poster Session 1 (Even numbers)
Session starts: Thursday 26 January, 16:00
Presentation starts: 16:00

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Roy van Mierlo (Eindhoven University of Technology)
Natal van Riel (Eindhoven University of Technology)

Abstract:
Due to the limited time available in intensive care units (ICUs), physicians often have difficulty deriving an unstable patient’s underlying pathology. Regularly, reactive treatments are started for these patients, whereas timely functional examination can give more insight into the underlying pathology of instability. Therefore, predicting instability would allow for proactive and more specialized interventions and could even prevent deterioration in risk patients altogether. Previous attempts at data-based prediction of hemodynamic instability have been made.[1] However, incorporating physical laws can allow for more accurate instability prediction and, in addition, can help with determining underlying pathologies and their correct treatment strategies. Arterial wall mechanics and arterial blood pressure directly characterize the functionality of the cardiovascular system and thus give clear insights into a patient's hemodynamic status.[2] Although measuring such quantities in vivo gives highly accurate results, placing additional probes in patients in life-threatening situations is not ideal. Deriving these quantities in a non-invasive manner has motivated the use of computational models for in silico prediction. The goal of this study is to leverage hybrid modeling techniques (combining mechanistic modeling and machine learning) to model hemodynamic changes over a patient’s ICU stay. We use an arterial blood pressure waveform over a short period (seconds) and consider a two-stage Windkessel model to derive the model parameters (resistances and compliances for the large and small vessels). Next, by deriving these parameters at several stages of a patient’s ICU stay, we can find a model for the long-term behavior (days) of these Windkessel parameters using machine learning.[3] Patient-specific model parameters of this long-term behavior model will have good prognostic value for predicting instability and certain hemodynamic complications. References 1. Rahman, A., Chang, Y., Dong, J., Conroy, B., Natarajan, A., Kinoshita, T., ... & Xu-Wilson, M. (2021). Early prediction of hemodynamic interventions in the intensive care unit using machine learning. Critical Care, 25(1), 1-9. 2. O’Rourke, M. (1995). Mechanical principles in arterial disease. Hypertension, 26(1), 2-9. 3. Regazzoni, F., Chapelle, D., & Moireau, P. (2021). Combining data assimilation and machine learning to build data‐driven models for unknown long time dynamics—Applications in cardiovascular modeling. International Journal for Numerical Methods in Biomedical Engineering, 37(7), e3471.