Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study
Abstract Background To develop prediction models for extubation time and midterm recovery time estimation in ophthalmic patients who underwent general anesthesia. Methods Totally 1824 ophthalmic patients who received general anesthesia at Joint Shantou International Eye Center were included. They we...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Anesthesiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12871-023-02021-3 |
| _version_ | 1797863733468332032 |
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| author | Xuan Huang Ronghui Tan Jian-Wei Lin Gonghui Li Jianying Xie |
| author_facet | Xuan Huang Ronghui Tan Jian-Wei Lin Gonghui Li Jianying Xie |
| author_sort | Xuan Huang |
| collection | DOAJ |
| description | Abstract Background To develop prediction models for extubation time and midterm recovery time estimation in ophthalmic patients who underwent general anesthesia. Methods Totally 1824 ophthalmic patients who received general anesthesia at Joint Shantou International Eye Center were included. They were divided into a training dataset of 1276 samples, a validation dataset of 274 samples and a check dataset of 274 samples. Up to 85 to 87 related factors were collected for extubation time and midterm recovery time analysis, respectively, including patient factors, anesthetic factors, surgery factors and laboratory examination results. First, multiple linear regression was used for predictor selection. Second, different methods were used to develop predictive models for extubation time and midterm recovery time respectively. Finally, the models’ generalization abilities were evaluated using a same check dataset with MSE, RMSE, MAE, MAPE, R-Squared and CCC. Results The fuzzy neural network achieved the highest R-Squared of 0.956 for extubation time prediction and 0.885 for midterm recovery time, and the RMSE value was 6.637 and 9.285, respectively. Conclusion The fuzzy neural network developed in this study had good generalization performance in predicting both extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia. Trial registration This study is prospectively registered in the Chinese Clinical Trial Registry, registration number: CHiCRT2000036416, registration date: August 23, 2020. |
| first_indexed | 2024-04-09T22:40:20Z |
| format | Article |
| id | doaj.art-3f67999420fd466f88a885a952510df0 |
| institution | Directory Open Access Journal |
| issn | 1471-2253 |
| language | English |
| last_indexed | 2024-04-09T22:40:20Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Anesthesiology |
| spelling | doaj.art-3f67999420fd466f88a885a952510df02023-03-22T12:12:27ZengBMCBMC Anesthesiology1471-22532023-03-0123111710.1186/s12871-023-02021-3Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional studyXuan Huang0Ronghui Tan1Jian-Wei Lin2Gonghui Li3Jianying Xie4Joint Shantou International Eye Centre of Shantou University and the Chinese University of Hong KongJoint Shantou International Eye Centre of Shantou University and the Chinese University of Hong KongJoint Shantou International Eye Centre of Shantou University and the Chinese University of Hong KongJoint Shantou International Eye Centre of Shantou University and the Chinese University of Hong KongJoint Shantou International Eye Centre of Shantou University and the Chinese University of Hong KongAbstract Background To develop prediction models for extubation time and midterm recovery time estimation in ophthalmic patients who underwent general anesthesia. Methods Totally 1824 ophthalmic patients who received general anesthesia at Joint Shantou International Eye Center were included. They were divided into a training dataset of 1276 samples, a validation dataset of 274 samples and a check dataset of 274 samples. Up to 85 to 87 related factors were collected for extubation time and midterm recovery time analysis, respectively, including patient factors, anesthetic factors, surgery factors and laboratory examination results. First, multiple linear regression was used for predictor selection. Second, different methods were used to develop predictive models for extubation time and midterm recovery time respectively. Finally, the models’ generalization abilities were evaluated using a same check dataset with MSE, RMSE, MAE, MAPE, R-Squared and CCC. Results The fuzzy neural network achieved the highest R-Squared of 0.956 for extubation time prediction and 0.885 for midterm recovery time, and the RMSE value was 6.637 and 9.285, respectively. Conclusion The fuzzy neural network developed in this study had good generalization performance in predicting both extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia. Trial registration This study is prospectively registered in the Chinese Clinical Trial Registry, registration number: CHiCRT2000036416, registration date: August 23, 2020.https://doi.org/10.1186/s12871-023-02021-3Delayed Emergence from AnesthesiaPrediction ModelFuzzy Neural NetworkExtubation TimeMidterm Recovery TimeRisk Factors |
| spellingShingle | Xuan Huang Ronghui Tan Jian-Wei Lin Gonghui Li Jianying Xie Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study BMC Anesthesiology Delayed Emergence from Anesthesia Prediction Model Fuzzy Neural Network Extubation Time Midterm Recovery Time Risk Factors |
| title | Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study |
| title_full | Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study |
| title_fullStr | Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study |
| title_full_unstemmed | Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study |
| title_short | Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study |
| title_sort | development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia a cross sectional study |
| topic | Delayed Emergence from Anesthesia Prediction Model Fuzzy Neural Network Extubation Time Midterm Recovery Time Risk Factors |
| url | https://doi.org/10.1186/s12871-023-02021-3 |
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