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. 2023 Mar 17;23(1):83.
doi: 10.1186/s12871-023-02021-3.

Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study

Xuan Huang  1   2 , Ronghui Tan  1   2 , Jian-Wei Lin  1 , Gonghui Li  3 , Jianying Xie  1
Affiliations

Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study

Xuan Huang et al. BMC Anesthesiol. .

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.

Keywords: Delayed Emergence from Anesthesia; Extubation Time; Fuzzy Neural Network; Midterm Recovery Time; Prediction Model; Risk Factors.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Scatter fitting diagram of predicted and true values of each dataset in extubation time FNN. A fitting of the predicted and true value in training dataset; B fitting of the predicted and true value in validation dataset; C fitting of the predicted and true value in check dataset
Fig. 2
Fig. 2
Scatter fitting diagram of predicted and true values in each regression model of extubation time. A fitting of the predicted and true value in stepwise linear regression model; B fitting of the predicted and true value in regression tree model; C fitting of the predicted and true value in ensembles of trees regression model
Fig. 3
Fig. 3
Training state of LM algorithm artificial neural network model of extubation time. A performance in LM algorithm; B error histogram in LM algorithm; C training state in LM algorithm; D regression in LM algorithm
Fig. 4
Fig. 4
Training state of BR algorithm artificial neural network model of extubation time. A performance in BR algorithm; B error histogram in BR algorithm; C training state in BR algorithm; D regression in BR algorithm
Fig. 5
Fig. 5
Training state of SCG algorithm artificial neural network model of extubation time. A performance in SCG algorithm; B error histogram in SCG algorithm; C training state in SCG algorithm; D regression in SCG algorithm
Fig. 6
Fig. 6
Scatter fitting diagram of predicted and true values of each dataset in midterm recovery time FNN. A fitting of the predicted and true value in training dataset; B fitting of the predicted and true value in validation dataset; C fitting of the predicted and true value in check dataset
Fig. 7
Fig. 7
Scatter fitting diagram of predicted and true values in each regression model of midterm recovery time. A fitting of the predicted and true value in stepwise linear regression model; B fitting of the predicted and true value in regression tree model; C fitting of the predicted and true value in ensembles of trees regression model
Fig. 8
Fig. 8
Training state of LM algorithm artificial neural network model of midterm recovery time. A performance in LM algorithm; B error histogram in LM algorithm; C training state in LM algorithm; D regression in LM algorithm
Fig. 9
Fig. 9
Training state of BR algorithm artificial neural network model of midterm recovery time. A performance in BR algorithm; B error histogram in BR algorithm; C training state in BR algorithm; D regression in BR algorithm
Fig. 10
Fig. 10
Training state of SCG algorithm artificial neural network model of midterm recovery time. A performance in SCG algorithm; B error histogram in SCG algorithm; C training state in SCG algorithm; D regression in SCG algorithm
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