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A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section
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 secti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045295/ https://www.ncbi.nlm.nih.gov/pubmed/33853548 http://dx.doi.org/10.1186/s12871-021-01331-8 |
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author | Wei, Chang-na Wang, Li-ying Chang, Xiang-yang Zhou, Qing-he |
author_facet | Wei, Chang-na Wang, Li-ying Chang, Xiang-yang Zhou, Qing-he |
author_sort | Wei, Chang-na |
collection | PubMed |
description | 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(1)-0.017599*X(2)” (Y: bupivacaine volume; X(1): vertebral column length; X(2): abdominal girth), with λ 0.055, MSE 0.0087, and R(2) 0.807. 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-021-01331-8. |
format | Online Article Text |
id | pubmed-8045295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80452952021-04-14 A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section Wei, Chang-na Wang, Li-ying Chang, Xiang-yang Zhou, Qing-he BMC Anesthesiol Research Article 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(1)-0.017599*X(2)” (Y: bupivacaine volume; X(1): vertebral column length; X(2): abdominal girth), with λ 0.055, MSE 0.0087, and R(2) 0.807. 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-021-01331-8. BioMed Central 2021-04-14 /pmc/articles/PMC8045295/ /pubmed/33853548 http://dx.doi.org/10.1186/s12871-021-01331-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Wei, Chang-na Wang, Li-ying Chang, Xiang-yang Zhou, Qing-he A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
title | A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
title_full | A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
title_fullStr | A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
title_full_unstemmed | A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
title_short | A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
title_sort | prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8045295/ https://www.ncbi.nlm.nih.gov/pubmed/33853548 http://dx.doi.org/10.1186/s12871-021-01331-8 |
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