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Establishment and Validation of a Prediction Equation to Estimate Risk of Intraoperative Hypothermia in Patients Receiving General Anesthesia
Inadvertent intraoperative hypothermia (core temperature
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| Published in: | Scientific reports 2017-10, Vol.7 (1), p.13927-8, Article 13927 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| cites | cdi_FETCH-LOGICAL-c474t-3c4b6fde09bc8333207906b0db4fe4d5181d2a55b51d759343b20b2c6c6b50ef3 |
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| creator | Yi, Jie Zhan, Lujing Lei, Yongjing Xu, Shiyuan Si, Yongyu Li, Shiyang Xia, Zhongyuan Shi, Yisa Gu, Xiaoping Yu, Jianshe Xu, Guohai Gu, Erwei Yu, Yonghao Chen, Yanqing Jia, Hequn Wang, Yinglin Wang, Xiuli Chai, Xiaoqing Jin, Xiaoju Chen, Junping Xu, Meiying Xiong, Junyu Wang, Guonian Lu, Kaizhi Yu, Wenli Lei, Weifu Qin, Zaisheng Xiang, Jingguo Li, Longyun Yao, Min Huang, Yuguang |
| description | Inadvertent intraoperative hypothermia (core temperature |
| doi_str_mv | 10.1038/s41598-017-12997-x |
| format | article |
| fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5654776</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1955033881</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-3c4b6fde09bc8333207906b0db4fe4d5181d2a55b51d759343b20b2c6c6b50ef3</originalsourceid><addsrcrecordid>eNp1kc1q3DAUhUVpacI0L9BFEXTTjRv9-mdTCGGaBAIJoe1WSPL1jFJbmkj2kDxE37macRqmhWpjyfe7597DQeg9JZ8p4fVpElQ2dUFoVVDWNFXx-AodMyJkwThjrw_uR-gkpXuSj2SNoM1bdMQaUpYVrY7Rr2UateldWg_gR6x9i3_o3rV6dMHj0GGNbyO0zu7fy4dpLowB50Y36BHwnUs_d-SVH6MOG4gZ2QK-fNqEcQ1xcBo7j2_z3zwh4Tuw4LbOr_AF-Az3-MxDymRy-h160-k-wcnzd4G-f11-O78srm8urs7PrgsrKjEW3ApTdi2Qxtiac85IlQ0Z0hrRgWglrWnLtJRG0raSDRfcMGKYLW1pJIGOL9CXWXczmQFaC7vVe7WJ2VF8UkE79XfFu7Vaha2SpRRVVWaBT88CMTxMeX01uGSh77WHMCVFGylLVss8fIE-_oPehyn6bG9PEc7rmmaKzZSNIaUI3csylKhd4GoOXOXA1T5w9ZibPhzaeGn5E28G-AykXPIriAez_y_7G66vugg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1955033881</pqid></control><display><type>article</type><title>Establishment and Validation of a Prediction Equation to Estimate Risk of Intraoperative Hypothermia in Patients Receiving General Anesthesia</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><source>Full-Text Journals in Chemistry (Open access)</source><source>PubMed Central</source><source>Springer Nature - nature.com Journals - Fully Open Access</source><creator>Yi, Jie ; Zhan, Lujing ; Lei, Yongjing ; Xu, Shiyuan ; Si, Yongyu ; Li, Shiyang ; Xia, Zhongyuan ; Shi, Yisa ; Gu, Xiaoping ; Yu, Jianshe ; Xu, Guohai ; Gu, Erwei ; Yu, Yonghao ; Chen, Yanqing ; Jia, Hequn ; Wang, Yinglin ; Wang, Xiuli ; Chai, Xiaoqing ; Jin, Xiaoju ; Chen, Junping ; Xu, Meiying ; Xiong, Junyu ; Wang, Guonian ; Lu, Kaizhi ; Yu, Wenli ; Lei, Weifu ; Qin, Zaisheng ; Xiang, Jingguo ; Li, Longyun ; Yao, Min ; Huang, Yuguang</creator><creatorcontrib>Yi, Jie ; Zhan, Lujing ; Lei, Yongjing ; Xu, Shiyuan ; Si, Yongyu ; Li, Shiyang ; Xia, Zhongyuan ; Shi, Yisa ; Gu, Xiaoping ; Yu, Jianshe ; Xu, Guohai ; Gu, Erwei ; Yu, Yonghao ; Chen, Yanqing ; Jia, Hequn ; Wang, Yinglin ; Wang, Xiuli ; Chai, Xiaoqing ; Jin, Xiaoju ; Chen, Junping ; Xu, Meiying ; Xiong, Junyu ; Wang, Guonian ; Lu, Kaizhi ; Yu, Wenli ; Lei, Weifu ; Qin, Zaisheng ; Xiang, Jingguo ; Li, Longyun ; Yao, Min ; Huang, Yuguang</creatorcontrib><description>Inadvertent intraoperative hypothermia (core temperature <36 °C) is a frequent but preventable complication of general anesthesia. Accurate risk assessment of individual patients may help physicians identify patients at risk for hypothermia and apply preventive approaches, which include active intraoperative warming. This study aimed to develop and validate a risk-prediction model for intraoperative hypothermia. Two independent observational studies in China, the Beijing Regional Survey and the China National Survey, were conducted in 2013 and 2014, respectively, to determine the incidence of hypothermia and its underlying risk factors. In this study, using data from these two studies, we first derived a risk calculation equation, estimating the predictive risk of hypothermia using National Survey data (3132 patients), then validated the equation using the Beijing Regional Survey data (830 patients). Measures of accuracy, discrimination and calibration were calculated in the validation data set. Through validation, this model, named Predictors Score, had sound overall accuracy (Brier Score = 0.211), good discrimination (C-Statistic = 0.759) and excellent calibration (Hosmer-Lemeshow, P = 0.5611). We conclude that the Predictors Score is a valid predictor of the risk of operative hypothermia and can be used in deciding whether intraoperative warming is a cost-effective measure in preventing the hypothermia.</description><identifier>ISSN: 2045-2322</identifier><identifier>EISSN: 2045-2322</identifier><identifier>DOI: 10.1038/s41598-017-12997-x</identifier><identifier>PMID: 29066717</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>692/308/174 ; 692/308/409 ; Accuracy ; Anesthesia ; Body temperature ; Bone surgery ; Calibration ; General anesthesia ; Humanities and Social Sciences ; Hypothermia ; multidisciplinary ; Observational studies ; Patients ; Polls & surveys ; Prediction models ; Risk assessment ; Risk factors ; Science ; Science (multidisciplinary) ; Thoracic surgery</subject><ispartof>Scientific reports, 2017-10, Vol.7 (1), p.13927-8, Article 13927</ispartof><rights>The Author(s) 2017</rights><rights>2017. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-3c4b6fde09bc8333207906b0db4fe4d5181d2a55b51d759343b20b2c6c6b50ef3</citedby><cites>FETCH-LOGICAL-c474t-3c4b6fde09bc8333207906b0db4fe4d5181d2a55b51d759343b20b2c6c6b50ef3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1955033881/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1955033881?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29066717$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yi, Jie</creatorcontrib><creatorcontrib>Zhan, Lujing</creatorcontrib><creatorcontrib>Lei, Yongjing</creatorcontrib><creatorcontrib>Xu, Shiyuan</creatorcontrib><creatorcontrib>Si, Yongyu</creatorcontrib><creatorcontrib>Li, Shiyang</creatorcontrib><creatorcontrib>Xia, Zhongyuan</creatorcontrib><creatorcontrib>Shi, Yisa</creatorcontrib><creatorcontrib>Gu, Xiaoping</creatorcontrib><creatorcontrib>Yu, Jianshe</creatorcontrib><creatorcontrib>Xu, Guohai</creatorcontrib><creatorcontrib>Gu, Erwei</creatorcontrib><creatorcontrib>Yu, Yonghao</creatorcontrib><creatorcontrib>Chen, Yanqing</creatorcontrib><creatorcontrib>Jia, Hequn</creatorcontrib><creatorcontrib>Wang, Yinglin</creatorcontrib><creatorcontrib>Wang, Xiuli</creatorcontrib><creatorcontrib>Chai, Xiaoqing</creatorcontrib><creatorcontrib>Jin, Xiaoju</creatorcontrib><creatorcontrib>Chen, Junping</creatorcontrib><creatorcontrib>Xu, Meiying</creatorcontrib><creatorcontrib>Xiong, Junyu</creatorcontrib><creatorcontrib>Wang, Guonian</creatorcontrib><creatorcontrib>Lu, Kaizhi</creatorcontrib><creatorcontrib>Yu, Wenli</creatorcontrib><creatorcontrib>Lei, Weifu</creatorcontrib><creatorcontrib>Qin, Zaisheng</creatorcontrib><creatorcontrib>Xiang, Jingguo</creatorcontrib><creatorcontrib>Li, Longyun</creatorcontrib><creatorcontrib>Yao, Min</creatorcontrib><creatorcontrib>Huang, Yuguang</creatorcontrib><title>Establishment and Validation of a Prediction Equation to Estimate Risk of Intraoperative Hypothermia in Patients Receiving General Anesthesia</title><title>Scientific reports</title><addtitle>Sci Rep</addtitle><addtitle>Sci Rep</addtitle><description>Inadvertent intraoperative hypothermia (core temperature <36 °C) is a frequent but preventable complication of general anesthesia. Accurate risk assessment of individual patients may help physicians identify patients at risk for hypothermia and apply preventive approaches, which include active intraoperative warming. This study aimed to develop and validate a risk-prediction model for intraoperative hypothermia. Two independent observational studies in China, the Beijing Regional Survey and the China National Survey, were conducted in 2013 and 2014, respectively, to determine the incidence of hypothermia and its underlying risk factors. In this study, using data from these two studies, we first derived a risk calculation equation, estimating the predictive risk of hypothermia using National Survey data (3132 patients), then validated the equation using the Beijing Regional Survey data (830 patients). Measures of accuracy, discrimination and calibration were calculated in the validation data set. Through validation, this model, named Predictors Score, had sound overall accuracy (Brier Score = 0.211), good discrimination (C-Statistic = 0.759) and excellent calibration (Hosmer-Lemeshow, P = 0.5611). We conclude that the Predictors Score is a valid predictor of the risk of operative hypothermia and can be used in deciding whether intraoperative warming is a cost-effective measure in preventing the hypothermia.</description><subject>692/308/174</subject><subject>692/308/409</subject><subject>Accuracy</subject><subject>Anesthesia</subject><subject>Body temperature</subject><subject>Bone surgery</subject><subject>Calibration</subject><subject>General anesthesia</subject><subject>Humanities and Social Sciences</subject><subject>Hypothermia</subject><subject>multidisciplinary</subject><subject>Observational studies</subject><subject>Patients</subject><subject>Polls & surveys</subject><subject>Prediction models</subject><subject>Risk assessment</subject><subject>Risk factors</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Thoracic 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Zhan, Lujing ; Lei, Yongjing ; Xu, Shiyuan ; Si, Yongyu ; Li, Shiyang ; Xia, Zhongyuan ; Shi, Yisa ; Gu, Xiaoping ; Yu, Jianshe ; Xu, Guohai ; Gu, Erwei ; Yu, Yonghao ; Chen, Yanqing ; Jia, Hequn ; Wang, Yinglin ; Wang, Xiuli ; Chai, Xiaoqing ; Jin, Xiaoju ; Chen, Junping ; Xu, Meiying ; Xiong, Junyu ; Wang, Guonian ; Lu, Kaizhi ; Yu, Wenli ; Lei, Weifu ; Qin, Zaisheng ; Xiang, Jingguo ; Li, Longyun ; Yao, Min ; Huang, Yuguang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-3c4b6fde09bc8333207906b0db4fe4d5181d2a55b51d759343b20b2c6c6b50ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>692/308/174</topic><topic>692/308/409</topic><topic>Accuracy</topic><topic>Anesthesia</topic><topic>Body temperature</topic><topic>Bone surgery</topic><topic>Calibration</topic><topic>General anesthesia</topic><topic>Humanities and Social 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Anesthesia</atitle><jtitle>Scientific reports</jtitle><stitle>Sci Rep</stitle><addtitle>Sci Rep</addtitle><date>2017-10-24</date><risdate>2017</risdate><volume>7</volume><issue>1</issue><spage>13927</spage><epage>8</epage><pages>13927-8</pages><artnum>13927</artnum><issn>2045-2322</issn><eissn>2045-2322</eissn><abstract>Inadvertent intraoperative hypothermia (core temperature <36 °C) is a frequent but preventable complication of general anesthesia. Accurate risk assessment of individual patients may help physicians identify patients at risk for hypothermia and apply preventive approaches, which include active intraoperative warming. This study aimed to develop and validate a risk-prediction model for intraoperative hypothermia. Two independent observational studies in China, the Beijing Regional Survey and the China National Survey, were conducted in 2013 and 2014, respectively, to determine the incidence of hypothermia and its underlying risk factors. In this study, using data from these two studies, we first derived a risk calculation equation, estimating the predictive risk of hypothermia using National Survey data (3132 patients), then validated the equation using the Beijing Regional Survey data (830 patients). Measures of accuracy, discrimination and calibration were calculated in the validation data set. Through validation, this model, named Predictors Score, had sound overall accuracy (Brier Score = 0.211), good discrimination (C-Statistic = 0.759) and excellent calibration (Hosmer-Lemeshow, P = 0.5611). We conclude that the Predictors Score is a valid predictor of the risk of operative hypothermia and can be used in deciding whether intraoperative warming is a cost-effective measure in preventing the hypothermia.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>29066717</pmid><doi>10.1038/s41598-017-12997-x</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | 692/308/174 692/308/409 Accuracy Anesthesia Body temperature Bone surgery Calibration General anesthesia Humanities and Social Sciences Hypothermia multidisciplinary Observational studies Patients Polls & surveys Prediction models Risk assessment Risk factors Science Science (multidisciplinary) Thoracic surgery |
| title | Establishment and Validation of a Prediction Equation to Estimate Risk of Intraoperative Hypothermia in Patients Receiving General Anesthesia |
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