A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section.
| Autor: | Wei CN; Department of Anesthesia, Jiaxing University Affiliated Women and Children Hospital, Jiaxing, Zhejiang Province, China., Wang LY; Department of Anesthesia, Jiaxing University Affiliated Women and Children Hospital, Jiaxing, Zhejiang Province, China., Chang XY; Department of Anesthesia, Jiaxing University Affiliated Women and Children Hospital, Jiaxing, Zhejiang Province, China., Zhou QH; Department of Anesthesia, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang Province, China. jxxmxy@163.com. |
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| Jazyk: | angličtina |
| Zdroj: | BMC anesthesiology [BMC Anesthesiol] 2021 Apr 14; Vol. 21 (1), pp. 116. Date of Electronic Publication: 2021 Apr 14. |
| DOI: | 10.1186/s12871-021-01331-8 |
| Abstrakt: | 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-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%, 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 Conclusions: 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. |
| Databáze: | MEDLINE |
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