Journal of Advanced in Medicinal, Pharmaceutical and Biomedical Research (JAMPBR)

Dynamic Causal Modeling of Heart Rate–Blood Pressure Coupling During Induction of Anesthesia: A Systems-Based Approach to Predict Vasopressor Requirements

Document Type : Original Article

Authors

Robab Azizi 1
Rana Mohammad Yousef 2

1 Department of Anesthesiology and Critical Care, School of Medicine, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Anesthesiology and Critical Care, School of Medicine, Children's Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran, Assistant professor of Anesthesiology, Tehran University of Medical

10.5281/zenodo.21969633
Abstract
Background: Induction of general anesthesia frequently induces hemodynamic instability characterized by hypotension, necessitating timely administration of vasopressors. The causal relationship between heart rate (HR) and blood pressure (BP) during this critical period remains incompletely understood, limiting the development of predictive models for vasopressor requirements.

Objective: This study employed dynamic causal modeling (DCM) to characterize the directional interactions between HR and BP during anesthetic induction and to develop a systems-based predictive framework for vasopressor needs.

Methods: We prospectively enrolled 120 adult patients undergoing elective major surgery under general anesthesia. Continuous HR and invasive arterial BP data were recorded from 5 minutes pre-induction to 10 minutes post-intubation. A time-varying Granger causality analysis was applied to quantify the feedforward (HR→BP) and feedback (BP→HR) causal pathways. A dynamic Bayesian network integrating hemodynamic, pharmacodynamic, and patient-specific parameters was constructed to predict vasopressor requirements.

Results: Propofol induction significantly attenuated the feedback pathway (BP→HR) from baseline (causal coefficient: 0.42±0.11 vs. 0.18±0.09, p<0.001) while preserving the feedforward pathway (HR→BP). The magnitude of feedback attenuation correlated strongly with subsequent vasopressor dose (r=0.73, p<0.001). The DCM-based prediction model achieved an AUC of 0.89 (95% CI: 0.83-0.94) for predicting vasopressor requirements >50 µg phenylephrine equivalent.

Conclusion: Dynamic causal modeling of HR-BP coupling during anesthetic induction provides mechanistic insights into hemodynamic instability and enables accurate, individualized prediction of vasopressor requirements, offering a promising framework for precision hemodynamic management.

Graphical Abstract

Dynamic Causal Modeling of Heart Rate–Blood Pressure Coupling During Induction of Anesthesia: A Systems-Based Approach to Predict Vasopressor Requirements

Keywords

dynamic causal modeling
Granger causality
anesthesia induction
vasopressor prediction
hemodynamic monitoring
Subjects
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Journal of Advanced in Medicinal, Pharmaceutical and Biomedical Research (JAMPBR)
Volume 3, Issue 1
January and February 2027
Pages 17-28

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