Preoperative echocardiography predictive analytics for postinduction hypotension prediction
Hypotension is a risk factor for adverse perioperative outcomes. Preoperative transthoracic echocardiography has been extended for preoperative risk assessment before noncardiac surgery. This study aimed to develop a machine learning model to predict postinduction hypotension risk using preoperative...
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| Veröffentlicht in: | PloS one 2022-11, Vol.17 (11), p.e0278140-e0278140 |
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| creator | Yoshimura, Manabu Shiramoto, Hiroko Koga, Mami Morimoto, Yasuhiro |
| description | Hypotension is a risk factor for adverse perioperative outcomes. Preoperative transthoracic echocardiography has been extended for preoperative risk assessment before noncardiac surgery. This study aimed to develop a machine learning model to predict postinduction hypotension risk using preoperative echocardiographic data and compared it with conventional statistic models. We also aimed to identify preoperative echocardiographic factors that cause postinduction hypotension.
In this retrospective observational study, we extracted data from electronic health records of patients aged >18 years who underwent general anesthesia at a single tertiary care center between April 2014 and September 2019. Multiple supervised machine learning classification techniques were used, with postinduction hypotension (mean arterial pressure |
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In this retrospective observational study, we extracted data from electronic health records of patients aged >18 years who underwent general anesthesia at a single tertiary care center between April 2014 and September 2019. Multiple supervised machine learning classification techniques were used, with postinduction hypotension (mean arterial pressure <55 mmHg from intubation to the start of the procedure) as the primary outcome and 95 transthoracic echocardiography measurements as factors influencing the primary outcome. Based on the mean cross-validation performance, we used 10-fold cross-validation with the training set (70%) to select the optimal hyperparameters and architecture, assessed ten times using a separate test set (30%).
Of 1,956 patients, 670 (34%) had postinduction hypotension. The area under the receiver operating characteristic curve using the deep neural network was 0.72 (95% confidence interval (CI) = 0.67-0.76), gradient boosting machine was 0.54 (95% CI = 0.51-0.59), linear discriminant analysis was 0.56 (95% CI = 0.51-0.61), and logistic regression was 0.56 (95% CI = 0.51-0.61). Variables of high importance included the ascending aorta diameter, transmitral flow A wave, heart rate, pulmonary venous flow S wave, tricuspid regurgitation pressure gradient, inferior vena cava expiratory diameter, fractional shortening, left ventricular mass index, and end-systolic volume.
We have created developing models that can predict postinduction hypotension using preoperative echocardiographic data, thereby demonstrating the feasibility of using machine learning models of preoperative echocardiographic data for produce higher accuracy than the conventional model.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0278140</identifier><identifier>PMID: 36441797</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Anesthesia ; Anesthesia, General - adverse effects ; Aorta ; Artificial neural networks ; Blood pressure ; Care and treatment ; Complications and side effects ; Computer and Information Sciences ; Confidence intervals ; Diagnosis ; Diameters ; Discriminant analysis ; Echocardiography ; Electronic health records ; Electronic medical records ; General anesthesia ; Health aspects ; Health risk assessment ; Heart beat ; Heart rate ; Hospitals ; Humans ; Hypotension ; Hypotension - diagnostic imaging ; Hypotension - etiology ; Intubation ; Learning algorithms ; Machine Learning ; Measurement ; Medical records ; Medicine and Health Sciences ; Methods ; Missing data ; Modelling ; Neural networks ; Observational studies ; Patients ; Physical Sciences ; Predictions ; Predictive analytics ; Pressure gradients ; Regurgitation ; Research and Analysis Methods ; Review boards ; Risk analysis ; Risk assessment ; Risk factors ; S waves ; Statistical analysis ; Surgery ; Surgical outcomes ; Tertiary ; Tricuspid Valve Insufficiency ; Ventricle</subject><ispartof>PloS one, 2022-11, Vol.17 (11), p.e0278140-e0278140</ispartof><rights>Copyright: © 2022 Yoshimura et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Yoshimura et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Yoshimura et al 2022 Yoshimura et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-e0c3b3fd4b556c7a275fb6e8ba4373eb280a45daff8a0f4a87aa3854c963fbf43</citedby><cites>FETCH-LOGICAL-c692t-e0c3b3fd4b556c7a275fb6e8ba4373eb280a45daff8a0f4a87aa3854c963fbf43</cites><orcidid>0000-0002-7729-5084</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704611/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9704611/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36441797$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Crivellari, Martina</contributor><creatorcontrib>Yoshimura, Manabu</creatorcontrib><creatorcontrib>Shiramoto, Hiroko</creatorcontrib><creatorcontrib>Koga, Mami</creatorcontrib><creatorcontrib>Morimoto, Yasuhiro</creatorcontrib><title>Preoperative echocardiography predictive analytics for postinduction hypotension prediction</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Hypotension is a risk factor for adverse perioperative outcomes. Preoperative transthoracic echocardiography has been extended for preoperative risk assessment before noncardiac surgery. This study aimed to develop a machine learning model to predict postinduction hypotension risk using preoperative echocardiographic data and compared it with conventional statistic models. We also aimed to identify preoperative echocardiographic factors that cause postinduction hypotension.
In this retrospective observational study, we extracted data from electronic health records of patients aged >18 years who underwent general anesthesia at a single tertiary care center between April 2014 and September 2019. Multiple supervised machine learning classification techniques were used, with postinduction hypotension (mean arterial pressure <55 mmHg from intubation to the start of the procedure) as the primary outcome and 95 transthoracic echocardiography measurements as factors influencing the primary outcome. Based on the mean cross-validation performance, we used 10-fold cross-validation with the training set (70%) to select the optimal hyperparameters and architecture, assessed ten times using a separate test set (30%).
Of 1,956 patients, 670 (34%) had postinduction hypotension. The area under the receiver operating characteristic curve using the deep neural network was 0.72 (95% confidence interval (CI) = 0.67-0.76), gradient boosting machine was 0.54 (95% CI = 0.51-0.59), linear discriminant analysis was 0.56 (95% CI = 0.51-0.61), and logistic regression was 0.56 (95% CI = 0.51-0.61). Variables of high importance included the ascending aorta diameter, transmitral flow A wave, heart rate, pulmonary venous flow S wave, tricuspid regurgitation pressure gradient, inferior vena cava expiratory diameter, fractional shortening, left ventricular mass index, and end-systolic volume.
We have created developing models that can predict postinduction hypotension using preoperative echocardiographic data, thereby demonstrating the feasibility of using machine learning models of preoperative echocardiographic data for produce higher accuracy than the conventional model.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Anesthesia</subject><subject>Anesthesia, General - adverse effects</subject><subject>Aorta</subject><subject>Artificial neural networks</subject><subject>Blood pressure</subject><subject>Care and treatment</subject><subject>Complications and side effects</subject><subject>Computer and Information Sciences</subject><subject>Confidence intervals</subject><subject>Diagnosis</subject><subject>Diameters</subject><subject>Discriminant analysis</subject><subject>Echocardiography</subject><subject>Electronic health records</subject><subject>Electronic medical records</subject><subject>General anesthesia</subject><subject>Health aspects</subject><subject>Health risk assessment</subject><subject>Heart beat</subject><subject>Heart rate</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Hypotension</subject><subject>Hypotension - diagnostic imaging</subject><subject>Hypotension - etiology</subject><subject>Intubation</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Measurement</subject><subject>Medical records</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Missing data</subject><subject>Modelling</subject><subject>Neural networks</subject><subject>Observational studies</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Predictive analytics</subject><subject>Pressure gradients</subject><subject>Regurgitation</subject><subject>Research and Analysis Methods</subject><subject>Review boards</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Risk factors</subject><subject>S waves</subject><subject>Statistical analysis</subject><subject>Surgery</subject><subject>Surgical outcomes</subject><subject>Tertiary</subject><subject>Tricuspid Valve 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echocardiography predictive analytics for postinduction hypotension prediction</title><author>Yoshimura, Manabu ; Shiramoto, Hiroko ; Koga, Mami ; Morimoto, Yasuhiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-e0c3b3fd4b556c7a275fb6e8ba4373eb280a45daff8a0f4a87aa3854c963fbf43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Anesthesia</topic><topic>Anesthesia, General - adverse effects</topic><topic>Aorta</topic><topic>Artificial neural networks</topic><topic>Blood pressure</topic><topic>Care and treatment</topic><topic>Complications and side effects</topic><topic>Computer and Information Sciences</topic><topic>Confidence intervals</topic><topic>Diagnosis</topic><topic>Diameters</topic><topic>Discriminant analysis</topic><topic>Echocardiography</topic><topic>Electronic health records</topic><topic>Electronic medical 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waves</topic><topic>Statistical analysis</topic><topic>Surgery</topic><topic>Surgical outcomes</topic><topic>Tertiary</topic><topic>Tricuspid Valve Insufficiency</topic><topic>Ventricle</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yoshimura, Manabu</creatorcontrib><creatorcontrib>Shiramoto, Hiroko</creatorcontrib><creatorcontrib>Koga, Mami</creatorcontrib><creatorcontrib>Morimoto, Yasuhiro</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research 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titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yoshimura, Manabu</au><au>Shiramoto, Hiroko</au><au>Koga, Mami</au><au>Morimoto, Yasuhiro</au><au>Crivellari, Martina</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Preoperative echocardiography predictive analytics for postinduction hypotension prediction</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-11-28</date><risdate>2022</risdate><volume>17</volume><issue>11</issue><spage>e0278140</spage><epage>e0278140</epage><pages>e0278140-e0278140</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Hypotension is a risk factor for adverse perioperative outcomes. Preoperative transthoracic echocardiography has been extended for preoperative risk assessment before noncardiac surgery. This study aimed to develop a machine learning model to predict postinduction hypotension risk using preoperative echocardiographic data and compared it with conventional statistic models. We also aimed to identify preoperative echocardiographic factors that cause postinduction hypotension.
In this retrospective observational study, we extracted data from electronic health records of patients aged >18 years who underwent general anesthesia at a single tertiary care center between April 2014 and September 2019. Multiple supervised machine learning classification techniques were used, with postinduction hypotension (mean arterial pressure <55 mmHg from intubation to the start of the procedure) as the primary outcome and 95 transthoracic echocardiography measurements as factors influencing the primary outcome. Based on the mean cross-validation performance, we used 10-fold cross-validation with the training set (70%) to select the optimal hyperparameters and architecture, assessed ten times using a separate test set (30%).
Of 1,956 patients, 670 (34%) had postinduction hypotension. The area under the receiver operating characteristic curve using the deep neural network was 0.72 (95% confidence interval (CI) = 0.67-0.76), gradient boosting machine was 0.54 (95% CI = 0.51-0.59), linear discriminant analysis was 0.56 (95% CI = 0.51-0.61), and logistic regression was 0.56 (95% CI = 0.51-0.61). Variables of high importance included the ascending aorta diameter, transmitral flow A wave, heart rate, pulmonary venous flow S wave, tricuspid regurgitation pressure gradient, inferior vena cava expiratory diameter, fractional shortening, left ventricular mass index, and end-systolic volume.
We have created developing models that can predict postinduction hypotension using preoperative echocardiographic data, thereby demonstrating the feasibility of using machine learning models of preoperative echocardiographic data for produce higher accuracy than the conventional model.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36441797</pmid><doi>10.1371/journal.pone.0278140</doi><tpages>e0278140</tpages><orcidid>https://orcid.org/0000-0002-7729-5084</orcidid><oa>free_for_read</oa></addata></record> |
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| issn | 1932-6203 1932-6203 |
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| subjects | Algorithms Analysis Anesthesia Anesthesia, General - adverse effects Aorta Artificial neural networks Blood pressure Care and treatment Complications and side effects Computer and Information Sciences Confidence intervals Diagnosis Diameters Discriminant analysis Echocardiography Electronic health records Electronic medical records General anesthesia Health aspects Health risk assessment Heart beat Heart rate Hospitals Humans Hypotension Hypotension - diagnostic imaging Hypotension - etiology Intubation Learning algorithms Machine Learning Measurement Medical records Medicine and Health Sciences Methods Missing data Modelling Neural networks Observational studies Patients Physical Sciences Predictions Predictive analytics Pressure gradients Regurgitation Research and Analysis Methods Review boards Risk analysis Risk assessment Risk factors S waves Statistical analysis Surgery Surgical outcomes Tertiary Tricuspid Valve Insufficiency Ventricle |
| title | Preoperative echocardiography predictive analytics for postinduction hypotension prediction |
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