Background Intraoperative hypotension burden not equally distributed during various periods of a general anesthetic. Post-induction hypotension usually has an iatrogenic cause and is related to the combination of drug doses used for induction of anesthesia. Predicting post-induction blood pressures, prior to induction, may inform anesthesiologists on the most judicious selection of anesthetic drugs and dosages. Our objective was to explore patterns of post-induction hypotension, apply machine learning (ML) to predict the mean blood pressure (MBP) immediately after induction, and develop a prototype of a clinical tool that can implement the ML model.

Materials and Methods We extracted data from preoperative and intraoperative anesthetic episodes over an 8-year period at the University of Pittsburgh Medical Center. The dataset consisted of 93,037 anesthetic episodes, which was partitioned into training, validation, and test sets. Preoperative and pre-induction predictor variables included demographics, comorbidities, laboratory test values, pre-induction MBP, and administrations of induction drugs. The target consisted of 15 minutes of MBP values immediately after induction.

Results The best-performing model was extreme-gradient boosted trees (XGB), with an R-squared value of 0.31 and a mean absolute error of 11.96, which is a moderately good performance. The performance of the model decreased over each minute post-induction.

Conclusion ML modeling to predict post-induction MBP is feasible, and a clinical tool that incorporates ML can potentially aid in preventing hypotension in the operating room.

Shyam Visweswaran and Harikesh Subramanian have an invention disclosure related to the work described in this manuscript

This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483.

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