# Personalized pulse wave propagation modeling to improve vasopressor dosing management in patients with severe traumatic brain injury

## TL;DR

This study explores using non-invasive pulse wave data and a mathematical model to predict vasopressor dose changes in severe traumatic brain injury patients, aiming to improve treatment accuracy.

## Contribution

The novelty lies in combining non-invasive pulse wave recordings with a personalized cardiovascular model to predict vasopressor adjustments in sTBI patients.

## Key findings

- The model achieved a balanced accuracy of 0.85 in predicting norepinephrine dose changes.
- Model fits to pulse waves were strong, with R2 values of approximately 0.9.
- Despite variability, the method predicted dose direction changes with satisfactory accuracy.

## Abstract

This study investigates whether examining the shape of arterial pulse waves and fitting to them a physiology-based mathematical model of pulse wave propagation can provide additional insights into the state of the cardiovascular system in patients with severe traumatic brain injury (sTBI), potentially enhancing vasopressor dosing strategies. We conducted a longitudinal study on 25 sTBI patients in an intensive care unit. Arterial pulse waves were recorded non-invasively from wrists and ankles using an oscillometric method and were used to inform a 0-1D model of the arterial blood flow dynamics. Model-estimated, patient-specific cardiovascular parameters were then used in a statistical model to predict changes in the administered dose of vasopressor (norepinephrine) in the next 24 hours. The model fits to the recorded pulse waves were satisfactory, with the coefficients of determination (R2) of approximately 0.9 and the differences between the measured and model-estimated mean arterial pressure of 0.1 ± 1.0 mmHg (R2=0.99). Except for a few patients, we found no clear association between the model-estimated parameters and norepinephrine dose at the time of pulse wave recording. Nevertheless, our predictive model achieved a balanced accuracy of 0.85 when trained and tested on the entire dataset and 0.76 when using the leave-one-out cross-validation, with 8 misclassifications among the total of 77 observations. Thus, despite the known inter-patient variability of hemodynamic response to vasopressors, the proposed method allowed predicting the direction of norepinephrine dose changes in the next 24 hours with satisfactory accuracy. Subject to further studies and extensive validation, our approach could inform a decision-support tool for optimizing vasopressor dosing on a per-patient basis.

Hypotension is a dangerous complication in patients with severe traumatic brain injury (sTBI), making precise control of systemic blood pressure of utmost importance. To maintain blood pressure at prescribed levels, physicians often administer vasopressors (for example, norepinephrine). In current clinical practice, the vasopressor dose is frequently adjusted by trial and error, guided by continuous monitoring of the patient’s hemodynamic state. This approach is time-consuming and likely sub-optimal, highlighting the need for more efficient methods to guide vasopressor therapy. In this study, we investigated whether non-invasive peripheral pulse wave recordings, combined with mathematical modeling of cardiovascular dynamics, could help predict changes in vasopressor dosing. Our results show that by incorporating personalized cardiovascular parameters into a statistical predictive model, it is possible to predict - with satisfactory accuracy - the direction of change (or lack thereof) in the administered norepinephrine dose within the next 24 hours. With further research and rigorous validation, this approach may support more effective vasopressor management using easily available, non-invasive clinical data.