A Prediction Model using Machine-Learning Algorithm for Assessing Optimal Intrathecal Hyperbaric Bupivacaine Dose during Cesarean Section

Background: The optimal intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. The aim of this study was to develop a decision-support model using a machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose based on physical variables during cesarean section. Methods: Term parturients presenting for elective cesarean section under spinal anaesthesia were enrolled. Spinal anesthesia was performed at the L3/4 interspace with 0.5% hyperbaric bupivacaine at dosages determined by the anesthesiologist. A spinal spread level between T4-T6 was considered the appropriate block level. We used a machine-learning algorithm to identify relevant parameters. The dataset was split into derivation (80%) and validation (20%) cohorts. An optimal decision-support model was developed for obtaining the regression equation between optimized intrathecal 0.5% hyperbaric bupivacaine volume and physical variables. Results: A total of 684 parturients were included, of whom 516 (75.44%) and 168 (24.56%) had block levels between T4 and T6, and less than T6 or higher than T4, respectively. The appropriate block level rate was 75.44%. In lasso regression, based on the principle of predicting a reasonable dose of intrathecal bupivacaine with fewer physical variables, the optimal model is “Y=0.5922+ 0.055117* X1-0.017599*X2” (Y: bupivacaine volume; X1: vertebral column length; X2: abdominal girth), with λ 0.055, MSE(mean square error) 0.0087, and R 2 0.807. Conclusion: After applying a machine-learning algorithm, we developed a decision model with R 2 0.8070 and MSE due to error 0.0087 using abdominal girth and vertebral column length for predicting the optimized intrathecal 0.5% hyperbaric bupivacaine dosage during term cesarean sections.