A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section.

ABSTRACT:

Background

The intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. This study aimed 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. A decision-suppor

SUBMITTER: Wei CN 

PROVIDER: S-EPMC8045295 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature