Department of Anesthesia, Jiaxing University Affiliated Women and Children Hospital, Jiaxing, Zhejiang Province, China.
Department of Anesthesia, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang Province, China.
BMC Anesthesiol. 2021 Apr 14;21(1):116. doi: 10.1186/s12871-021-01331-8.
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.
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-support model was developed for obtaining the regression equation between optimized intrathecal 0.5% hyperbaric bupivacaine volume and physical variables.
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%, with the mean bupivacaine volume [1.965, 95%CI (1.945,1.984)]ml. In lasso regression, based on the principle of predicting a reasonable dose of intrathecal bupivacaine with fewer physical variables, the model is "Y=0.5922+ 0.055117* X-0.017599*X" (Y: bupivacaine volume; X: vertebral column length; X: abdominal girth), with λ 0.055, MSE 0.0087, and R 0.807.
After applying a machine-learning algorithm, we developed a decision model with R 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.
剖宫产术中鞘内使用超高压布比卡因的剂量难以预先确定。本研究旨在开发一种使用机器学习算法的决策支持模型，根据剖宫产期间的身体变量评估鞘内超高压布比卡因剂量。
纳入择期行脊髓麻醉下剖宫产的足月产妇。在 L3/4 间隙进行脊髓麻醉，麻醉师根据 0.5%的布比卡因进行硬膜外麻醉。T4-T6 之间的脊髓扩散水平被认为是合适的阻滞水平。我们使用机器学习算法来识别相关参数。数据集分为推导（80%）和验证（20%）队列。为获得优化的鞘内 0.5%布比卡因体积与身体变量之间的回归方程，建立决策支持模型。
共纳入 684 例产妇，其中阻滞水平在 T4-T6 之间的有 516 例（75.44%），阻滞水平低于 T4 或高于 T6 的分别有 168 例（24.56%）。合适的阻滞水平率为 75.44%，布比卡因平均体积[1.965，95%CI（1.945，1.984）]ml。在套索回归中，根据用较少的身体变量预测合理的鞘内布比卡因剂量的原则，模型为“Y=0.5922+0.055117X-0.017599X”（Y：布比卡因体积；X：脊柱长度；X：腹围），λ=0.055，MSE=0.0087，R=0.807。
应用机器学习算法后，我们开发了一个决策模型，R=0.8070，MSE 为 0.0087，使用腹围和脊柱长度预测足月剖宫产术中优化的鞘内 0.5%布比卡因剂量。
