| Literature DB >> 34337032 |
Huimin Huang1, Hong Jiang1, Jinxing Liu1, Jie Chen1, Lin Qiu1, Jiayi Wang1, Wenhui Liu1, Huan Chen1.
Abstract
BACKGROUND: Anaesthesia can alter neuronal excitability and vascular reactivity and ultimately lead to neurovascular coupling. Precise control of the skeletal muscle relaxant doses is the key in reducing anaesthetic damage.Entities:
Mesh:
Substances:
Year: 2021 PMID: 34337032 PMCID: PMC8298146 DOI: 10.1155/2021/5655061
Source DB: PubMed Journal: Biomed Res Int Impact factor: 3.411
Figure 1Relationship between T1% and the degree of muscle relaxation with skeletal muscle relaxants in facial nerve monitoring: (a) T1% and body movement score relationship; (b) T1% and patient-ventilator asynchrony relationship; (c) estimated T1% with/without BM/PVA; (d) T1% and positive facial nerve EMG reaction relationship; (e) comparison of estimated T1% between positive EMG reaction and negative EMG reaction.
Figure 2Relationship between TOF% and the cut-off value for the effect of facial nerve anaesthesia for muscle relaxation: (a) the relationship between TOF% and body movement score; (b) the relationship between TOF% and patient-ventilator asynchrony; (c) comparison of estimated TOF% with/without BM/PVA; (d) the relationship between TOF% and positive EMG reaction of the facial nerve; (e) comparison of estimated TOF% between positive EMG reaction and negative EMG reaction.
Figure 3ROC curves of TOF% and T1% for predicting the effect of facial nerve anaesthesia on muscle relaxation: (a) ROC curve of TOF% and T1% for body movement or patient-ventilator asynchrony; (b) ROC curve of TOF% and T1% for body movement or positive EMG reaction of the facial nerve; (c) correlation of EMG values with TOF% and T1%.
Figure 4Visualization of the decision tree. (a) A decision tree-heat map for predicting the occurrence of BM or PVA during surgery. MRVC: movement or ventilation confrontation has occurred; ASA: American Society of Anesthesiologists. (b) A decision tree-heat map for predicting a positive EMG response of the facial nerve during surgery. EMG: electromyogram. Note: the heat map colours present the relative value of a sample compared to the rest of the group.
Figure 5A visual nomogram prediction model. (a) Nomogram prediction model for predicting the occurrence of BM or PVA during surgery. (b) Calibration curve of the BM/PVA model for predicting the occurrence of BM/PVA during surgery. The closer the solid line to the dashed line, the better the predictive power.
Chart showing the prediction factors.
| Variable | Prediction model | ||
|---|---|---|---|
|
| Odds ratio (95% CI) |
| |
| Intercept | 0.034 | 0.001 (0-0.119) | 0.007 |
| TOF% | -0.059 | 1.396 (1.123-1.768) | 0.004 |
| T1% | -0.678 | 2.633 (1.909-3.888) |
|
| Sex (male) | 2.189 | 0.657 (0.203-2.053) | 0.472 |
| Age (years) | -2.003 | 0.983 (0.944-1.022) | 0.391 |
| BMI | -0.001 | 0.88 (0.723-1.048) | 0.172 |
Note: β is the regression coefficient.
C-index of the nomogram prediction model.
| Features | C-index (95% confidence interval) |
|---|---|
| Entire cohort | 0.959 (0.935-0.982) |
| Train set | 0.977 (0.957-0.997) |
| Validation set | 0.905 (0.84-0.97) |