Post Induction Hypotension prediction during general anesthesia using Machine Learning Techniques

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.