Empirical pharmacodynamic model of phenylephrine and intrathecal bupivacaine for mean arterial pressure prediction in obstetric patients presenting for elective cesarean delivery under spinal anesthesia.

Abstract

Cesarean delivery under spinal anesthesia may be complicated by hypotension in up to 80% of the patients. The response to standard-of-care prophylactic phenylephrine infusion varies, and there is little guidance on achieving optimal blood pressure control. In this work, we developed a data-driven pharmacodynamic relationship between intravenous phenylephrine, intrathecal bupivacaine, and maternal mean arterial pressure (MAP) in patients presenting for cesarean delivery. In this single-center cohort study, secondary use data were available for normotensive patients presenting for cesarean delivery. Intraoperative MAP, intrathecal bupivacaine, and intravenous phenylephrine doses were recorded prospectively every minute. The recorded data were used to identify and confirm a time series (Autoregressive with Exogenous Input (ARX)) model for predicting the MAP using MATLAB 2021a System Identification Toolbox and the Prediction Error Method. An independent model validation was conducted using a second dataset collected after the model fitting stage. Model identification was performed on 172 patients, using 70% for model fitting and 30% for testing. The final ARX model, which takes the past three data points to make predictions, performed 48.9% better than a mean constant model for one-minute ahead MAP predictions with a root mean square error (RMSE) of 3.6 ± 1.3 mmHg. Similar performance was observed on independent validation using a second dataset (N = 84), yielding an RMSE of 4.2 ± 1.6 mmHg for one-minute ahead MAP predictions. Our ARX model showed good performance at up to a three-minute prediction horizon and could be used for future decision support applications to guide phenylephrine dose titration.