A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation
ObjectiveProlonged mechanical ventilation (PMV) is a significant postoperative complication in cardiac surgery, associated with increased mortality and healthcare costs. This study aims to develop and validate a novel scoring model to predict the risk of PMV in cardiac surgery patients.MethodsA retr...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1573874/full |
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| author | Quan Liu Quan Liu Pengfei Chen Wuwei Wang Yifei Zhou Yifei Zhou Yichen Xu Xu Cao Rui Fan Wen Chen Fuhua Huang Xin Chen Xin Chen |
| author_facet | Quan Liu Quan Liu Pengfei Chen Wuwei Wang Yifei Zhou Yifei Zhou Yichen Xu Xu Cao Rui Fan Wen Chen Fuhua Huang Xin Chen Xin Chen |
| author_sort | Quan Liu |
| collection | DOAJ |
| description | ObjectiveProlonged mechanical ventilation (PMV) is a significant postoperative complication in cardiac surgery, associated with increased mortality and healthcare costs. This study aims to develop and validate a novel scoring model to predict the risk of PMV in cardiac surgery patients.MethodsA retrospective analysis was conducted using data from 14 comprehensive hospitals in Jiangsu Province, including adult patients who underwent coronary artery bypass grafting (CABG), valve surgery, and aortic surgery from January 2021 to December 2022. Predictive variables were selected based on clinical expertise and prior literature, and a nomogram was developed using LASSO regression and multiple logistic regression. Model performance was evaluated using the C-index, calibration plots, and decision curve analysis (DCA).ResultsA total of 5,206 patients were included in the final analysis. The incidence rate of PMV were 11.83% in the training set, 8.65% in the internal validation set, and 15.4% in the external validation set. The nomogram identified 9 significant predictors, including age, gender, preoperative conditions, and surgical factors. The model demonstrated robust performance with C-index values of 0.79 in the training and internal validation sets and 0.75 in the external validation set, indicating good predictive capability. Calibration curves confirmed the accuracy of predicted probabilities, and DCA indicated substantial net benefits for clinical decision-making.ConclusionsThis study presents a validated scoring model for predicting PMV in cardiac surgery patients, integrating a comprehensive range of clinical variables. The model facilitates early identification of high-risk patients, enabling tailored perioperative strategies and potentially improving patient outcomes and resource utilization in cardiac surgery. |
| format | Article |
| id | doaj-art-8cd87e7a15b44460b33ec1c1cd61a74c |
| institution | OA Journals |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-8cd87e7a15b44460b33ec1c1cd61a74c2025-08-20T02:07:59ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-03-011210.3389/fcvm.2025.15738741573874A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validationQuan Liu0Quan Liu1Pengfei Chen2Wuwei Wang3Yifei Zhou4Yifei Zhou5Yichen Xu6Xu Cao7Rui Fan8Wen Chen9Fuhua Huang10Xin Chen11Xin Chen12School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaCardiovascular Surgery Department, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaSchool of Medicine, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaSchool of Medicine, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, ChinaObjectiveProlonged mechanical ventilation (PMV) is a significant postoperative complication in cardiac surgery, associated with increased mortality and healthcare costs. This study aims to develop and validate a novel scoring model to predict the risk of PMV in cardiac surgery patients.MethodsA retrospective analysis was conducted using data from 14 comprehensive hospitals in Jiangsu Province, including adult patients who underwent coronary artery bypass grafting (CABG), valve surgery, and aortic surgery from January 2021 to December 2022. Predictive variables were selected based on clinical expertise and prior literature, and a nomogram was developed using LASSO regression and multiple logistic regression. Model performance was evaluated using the C-index, calibration plots, and decision curve analysis (DCA).ResultsA total of 5,206 patients were included in the final analysis. The incidence rate of PMV were 11.83% in the training set, 8.65% in the internal validation set, and 15.4% in the external validation set. The nomogram identified 9 significant predictors, including age, gender, preoperative conditions, and surgical factors. The model demonstrated robust performance with C-index values of 0.79 in the training and internal validation sets and 0.75 in the external validation set, indicating good predictive capability. Calibration curves confirmed the accuracy of predicted probabilities, and DCA indicated substantial net benefits for clinical decision-making.ConclusionsThis study presents a validated scoring model for predicting PMV in cardiac surgery patients, integrating a comprehensive range of clinical variables. The model facilitates early identification of high-risk patients, enabling tailored perioperative strategies and potentially improving patient outcomes and resource utilization in cardiac surgery.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1573874/fullprolonged mechanical ventilationcardiac surgerypredicting modelmultiple centersretrospective study |
| spellingShingle | Quan Liu Quan Liu Pengfei Chen Wuwei Wang Yifei Zhou Yifei Zhou Yichen Xu Xu Cao Rui Fan Wen Chen Fuhua Huang Xin Chen Xin Chen A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation Frontiers in Cardiovascular Medicine prolonged mechanical ventilation cardiac surgery predicting model multiple centers retrospective study |
| title | A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation |
| title_full | A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation |
| title_fullStr | A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation |
| title_full_unstemmed | A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation |
| title_short | A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation |
| title_sort | novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients development and validation |
| topic | prolonged mechanical ventilation cardiac surgery predicting model multiple centers retrospective study |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1573874/full |
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