Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection
Abstract This study aimed to develop and validate a risk prediction model based on real-world data to assess the risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent video-assisted thoracoscopic (VATS) radical resection. This study is a retrospective study of re...
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Nature Portfolio
2024-11-01
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| Online Access: | https://doi.org/10.1038/s41598-024-79648-w |
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| author | Qiang Li Fuhai Xia Xiaoshuang He Qing Yan Qiuling Wu Chang Liu Rui Chen Jing Li |
| author_facet | Qiang Li Fuhai Xia Xiaoshuang He Qing Yan Qiuling Wu Chang Liu Rui Chen Jing Li |
| author_sort | Qiang Li |
| collection | DOAJ |
| description | Abstract This study aimed to develop and validate a risk prediction model based on real-world data to assess the risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent video-assisted thoracoscopic (VATS) radical resection. This study is a retrospective study of real-world data. A total of 257 elderly lung adenocarcinoma patients who underwent VATS radical resection from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into delayed recovery (n = 42) and non- delayed recovery group (n = 215) according to whether delayed recovery occurred after anesthesia. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of delayed recovery from anesthesia, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Delayed recovery from anesthesia occurred in 42 of 257 elderly lung adenocarcinoma patients underwent VATS radical resection (16.34%). Logistic regression analysis showed that anesthesia duration, intraoperative infusion volume, inhaled desflurane, preoperative respiratory tract infection, intraoperative hypothermia and diagnosed with hypertension were risk factors for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection (P < 0.05). The area under receiver operating characteristic curve was 0.869, 95% CI (0.815 ~ 0.923). The optimal cutoff value was 0.198, the sensitivity was 0.738, and the specificity was 0.823. Hosmer-Lemeshow test showed that χ 2 = 7.346, P = 0.500. The decision curve analysis results have shown that the threshold probability is between 0.23 and 0.91, and the net benefit rate of the model is good. The risk prediction model constructed in this study can provide reference for medical staff to screen precisely high-risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection, which is of great significance. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-c5aa984d0d3544b4898a9540882180d12024-11-17T12:18:32ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-79648-wDevelopment and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resectionQiang Li0Fuhai Xia1Xiaoshuang He2Qing Yan3Qiuling Wu4Chang Liu5Rui Chen6Jing Li7Operating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyOperating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyOperating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyOperating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyOperating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyAnesthesiology Department, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyOperating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyOperating Room, Tongji Medical College, The Central Hospital of Wuhan, Huazhong University of Science and TechnologyAbstract This study aimed to develop and validate a risk prediction model based on real-world data to assess the risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent video-assisted thoracoscopic (VATS) radical resection. This study is a retrospective study of real-world data. A total of 257 elderly lung adenocarcinoma patients who underwent VATS radical resection from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into delayed recovery (n = 42) and non- delayed recovery group (n = 215) according to whether delayed recovery occurred after anesthesia. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of delayed recovery from anesthesia, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Delayed recovery from anesthesia occurred in 42 of 257 elderly lung adenocarcinoma patients underwent VATS radical resection (16.34%). Logistic regression analysis showed that anesthesia duration, intraoperative infusion volume, inhaled desflurane, preoperative respiratory tract infection, intraoperative hypothermia and diagnosed with hypertension were risk factors for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection (P < 0.05). The area under receiver operating characteristic curve was 0.869, 95% CI (0.815 ~ 0.923). The optimal cutoff value was 0.198, the sensitivity was 0.738, and the specificity was 0.823. Hosmer-Lemeshow test showed that χ 2 = 7.346, P = 0.500. The decision curve analysis results have shown that the threshold probability is between 0.23 and 0.91, and the net benefit rate of the model is good. The risk prediction model constructed in this study can provide reference for medical staff to screen precisely high-risk of delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent VATS radical resection, which is of great significance.https://doi.org/10.1038/s41598-024-79648-wDelayed recoveryAnesthesiaLung adenocarcinomaVideo-assisted thoracoscopicPrediction model |
| spellingShingle | Qiang Li Fuhai Xia Xiaoshuang He Qing Yan Qiuling Wu Chang Liu Rui Chen Jing Li Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection Scientific Reports Delayed recovery Anesthesia Lung adenocarcinoma Video-assisted thoracoscopic Prediction model |
| title | Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection |
| title_full | Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection |
| title_fullStr | Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection |
| title_full_unstemmed | Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection |
| title_short | Development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection |
| title_sort | development and validation of a prediction model for delayed recovery from anesthesia in elderly lung adenocarcinoma patients underwent thoracoscopic radical resection |
| topic | Delayed recovery Anesthesia Lung adenocarcinoma Video-assisted thoracoscopic Prediction model |
| url | https://doi.org/10.1038/s41598-024-79648-w |
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