{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:32:22Z","timestamp":1784043142703,"version":"3.55.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T00:00:00Z","timestamp":1618358400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T00:00:00Z","timestamp":1618358400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technology Bureau of Jiaxing, Zhejiang Provience and Jiaxing Key Subject of Anesthesiology","award":["2015C23023"],"award-info":[{"award-number":["2015C23023"]}]},{"name":"the Key Discipline established with Zhejiang Province and Jiaxing City jointly","award":["2019-ss-ttyx"],"award-info":[{"award-number":["2019-ss-ttyx"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Anesthesiol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 \u201cY=0.5922+ 0.055117* X\n                      <jats:sub>1<\/jats:sub>\n                      -0.017599*X\n                      <jats:sub>2<\/jats:sub>\n                      \u201d (Y: bupivacaine volume; X\n                      <jats:sub>1<\/jats:sub>\n                      : vertebral column length; X\n                      <jats:sub>2<\/jats:sub>\n                      : abdominal girth), with \u03bb 0.055, MSE 0.0087, and R\n                      <jats:sup>2<\/jats:sup>\n                      0.807.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      After applying a machine-learning algorithm, we developed a decision model with R\n                      <jats:sup>2<\/jats:sup>\n                      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.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12871-021-01331-8","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T04:02:57Z","timestamp":1618372977000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section"],"prefix":"10.1186","volume":"21","author":[{"given":"Chang-na","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang-yang","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0731-6302","authenticated-orcid":false,"given":"Qing-he","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,14]]},"reference":[{"issue":"3","key":"1331_CR1","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1097\/00000542-199003000-00015","volume":"72","author":"MC Norris","year":"1990","unstructured":"Norris MC. 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The signed informed consent was obtained from all participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"116"}}