Peihe Wang, Meiling Lu, Yu Huang, Lu Sun, Zhen Han
{"title":"开发体外循环辅助开胸心脏手术患者住院死亡率预后提名图:一项回顾性队列研究。","authors":"Peihe Wang, Meiling Lu, Yu Huang, Lu Sun, Zhen Han","doi":"10.21037/jtd-24-24","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Extracorporeal circulation auxiliary to open cardiac surgery (ECAOCS) is one of the most complex surgical procedures and carries a very high risk of death. We developed a nomogram from a retrospective study to predict the risk of death during patient hospitalization.</p><p><strong>Methods: </strong>All clinical data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We extracted clinical variables for the first 24 hours after admission to the intensive care unit (ICU) in a total of 880 patients who underwent ECAOCS. All patients were randomly divided into training and validation cohort in a ratio of 7:3. All variables included in the study were subjected to univariate logistic regression analysis. In order to prevent overfitting and to address the problem of severe covariance, all factors with P<0.05 in the univariate logistic regression analysis were analyzed using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was developed based on the factors output from the LASSO regression and a nomogram was plotted. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated in training and validation cohort. Finally, the evaluation of the model was performed by calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) and decision curve analysis (DCA) was performed.</p><p><strong>Results: </strong>Indicators included in the nomogram were anion gap (AG), central venous pressure (CVP), glucose, creatinine (Cr), prothrombin time (PT), activated partial thromboplastin time (APTT), bicarbonate ion (HCO<sub>3</sub> <sup>-</sup>), cerebrovascular disease (CVD), peripheral vascular disease (PVD), and acute myocardial infarction (AMI).</p><p><strong>Conclusions: </strong>Our study developed a model for predicting postoperative hospital mortality in patients underwent ECAOCS by incorporating AG, CVP, glucose, Cr, APTT, HCO<sub>3</sub> <sup>-</sup>, CVD, AMI, and PVD from the first 24 hours after admission to the ICU.</p><p><strong>Keywords: </strong>Extracorporeal circulation; cardiac surgery; intensive care; nomogram; prediction model.</p>","PeriodicalId":17542,"journal":{"name":"Journal of thoracic disease","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11320247/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development of a prognostic nomogram for patients underwent extracorporeal circulation auxiliary to open cardiac surgery on hospital mortality: a retrospective cohort study.\",\"authors\":\"Peihe Wang, Meiling Lu, Yu Huang, Lu Sun, Zhen Han\",\"doi\":\"10.21037/jtd-24-24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Extracorporeal circulation auxiliary to open cardiac surgery (ECAOCS) is one of the most complex surgical procedures and carries a very high risk of death. We developed a nomogram from a retrospective study to predict the risk of death during patient hospitalization.</p><p><strong>Methods: </strong>All clinical data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We extracted clinical variables for the first 24 hours after admission to the intensive care unit (ICU) in a total of 880 patients who underwent ECAOCS. All patients were randomly divided into training and validation cohort in a ratio of 7:3. All variables included in the study were subjected to univariate logistic regression analysis. In order to prevent overfitting and to address the problem of severe covariance, all factors with P<0.05 in the univariate logistic regression analysis were analyzed using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was developed based on the factors output from the LASSO regression and a nomogram was plotted. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated in training and validation cohort. Finally, the evaluation of the model was performed by calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) and decision curve analysis (DCA) was performed.</p><p><strong>Results: </strong>Indicators included in the nomogram were anion gap (AG), central venous pressure (CVP), glucose, creatinine (Cr), prothrombin time (PT), activated partial thromboplastin time (APTT), bicarbonate ion (HCO<sub>3</sub> <sup>-</sup>), cerebrovascular disease (CVD), peripheral vascular disease (PVD), and acute myocardial infarction (AMI).</p><p><strong>Conclusions: </strong>Our study developed a model for predicting postoperative hospital mortality in patients underwent ECAOCS by incorporating AG, CVP, glucose, Cr, APTT, HCO<sub>3</sub> <sup>-</sup>, CVD, AMI, and PVD from the first 24 hours after admission to the ICU.</p><p><strong>Keywords: </strong>Extracorporeal circulation; cardiac surgery; intensive care; nomogram; prediction model.</p>\",\"PeriodicalId\":17542,\"journal\":{\"name\":\"Journal of thoracic disease\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11320247/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of thoracic disease\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/jtd-24-24\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"RESPIRATORY SYSTEM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of thoracic disease","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/jtd-24-24","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/17 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RESPIRATORY SYSTEM","Score":null,"Total":0}
Development of a prognostic nomogram for patients underwent extracorporeal circulation auxiliary to open cardiac surgery on hospital mortality: a retrospective cohort study.
Background: Extracorporeal circulation auxiliary to open cardiac surgery (ECAOCS) is one of the most complex surgical procedures and carries a very high risk of death. We developed a nomogram from a retrospective study to predict the risk of death during patient hospitalization.
Methods: All clinical data were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We extracted clinical variables for the first 24 hours after admission to the intensive care unit (ICU) in a total of 880 patients who underwent ECAOCS. All patients were randomly divided into training and validation cohort in a ratio of 7:3. All variables included in the study were subjected to univariate logistic regression analysis. In order to prevent overfitting and to address the problem of severe covariance, all factors with P<0.05 in the univariate logistic regression analysis were analyzed using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was developed based on the factors output from the LASSO regression and a nomogram was plotted. The receiver operating characteristic (ROC) curve was constructed and the area under the curve (AUC) was calculated in training and validation cohort. Finally, the evaluation of the model was performed by calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) and decision curve analysis (DCA) was performed.
Results: Indicators included in the nomogram were anion gap (AG), central venous pressure (CVP), glucose, creatinine (Cr), prothrombin time (PT), activated partial thromboplastin time (APTT), bicarbonate ion (HCO3-), cerebrovascular disease (CVD), peripheral vascular disease (PVD), and acute myocardial infarction (AMI).
Conclusions: Our study developed a model for predicting postoperative hospital mortality in patients underwent ECAOCS by incorporating AG, CVP, glucose, Cr, APTT, HCO3-, CVD, AMI, and PVD from the first 24 hours after admission to the ICU.
期刊介绍:
The Journal of Thoracic Disease (JTD, J Thorac Dis, pISSN: 2072-1439; eISSN: 2077-6624) was founded in Dec 2009, and indexed in PubMed in Dec 2011 and Science Citation Index SCI in Feb 2013. It is published quarterly (Dec 2009- Dec 2011), bimonthly (Jan 2012 - Dec 2013), monthly (Jan. 2014-) and openly distributed worldwide. JTD received its impact factor of 2.365 for the year 2016. JTD publishes manuscripts that describe new findings and provide current, practical information on the diagnosis and treatment of conditions related to thoracic disease. All the submission and reviewing are conducted electronically so that rapid review is assured.