A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Setting
2.2. Data Source
2.3. Algorithms
2.3.1. Logistic Regression (LR)
2.3.2. Decision Trees (DT)
2.3.3. Random Forest (RF)
2.3.4. eXtreme Gradient Boosting (XGBoost)
2.3.5. Support Vector Machine (SVM)
2.3.6. Artificial Neural Network (ANN)
2.4. Statistical Analyses
2.4.1. Features Extraction
2.4.2. Analysis Procedure
3. Results
3.1. Patient Characteristics
3.2. Results of the Machine Learning Models
3.3. Using LR to Create a New Prediction Model
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variable | Weaned within 24 h (n = 1042) | Not Weaned within 24 h (n = 397) | p-Value | |
---|---|---|---|---|
Gender | 0.069 | |||
Males | 714 (68.5%) | 252 (63.5%) | ||
Females | 328 (31.5%) | 145 (36.5%) | ||
age, mean ± SD | 65.05 ± 12.53 | 68.34 ± 15.18 | <0.001 | * |
Smoking, n (%) | <0.001 | * | ||
No | 805 (77.3%) | 250 (63.0%) | ||
Yes | 198 (19.0%) | 113 (28.5%) | ||
Yes, quit smoking | 39 (3.7%) | 34 (8.6%) | ||
Ventilation set, mean ± SD | ||||
Ventilation rate set, 30/min | 12.23 ± 1.08 | 14.77 ± 8.77 | <0.001 | * |
Inspiration time, breath/min | 1.00 ± 0.00 | 4.08 ± 8.72 | <0.001 | * |
Pressure limit high, cmH2O | 40.72 ± 1.89 | 37.00 ± 6.75 | <0.001 | * |
Pressure limit low, cmH2O | 2.99 ± 0.40 | 3.91 ± 1.10 | <0.001 | * |
Spontaneous respiratory rate, % | 13.38 ± 2.92 | 20.22 ± 6.45 | <0.001 | * |
Inspiratory pressure, cmH2O | 20.74 ± 2.52 | 19.69 ± 5.84 | <0.001 | * |
PEEP, cmH2O | 5.47 ± 0.92 | 4.37 ± 2.69 | <0.001 | * |
Ramp, mS | 0.01 ± 0.13 | 0.24 ± 0.73 | <0.001 | * |
Ventilation monitoring, mean ± SD | ||||
Inspiratory tidal volume, mL/kg | 554.92 ± 84.93 | 422.41 ± 336.91 | <0.001 | * |
Expiratory tidal volume, mL/kg | 555.55 ± 80.85 | 507.98 ± 156.23 | <0.001 | * |
Peak pressure, cmH2O | 21.05 ± 2.85 | 211.52 ± 273.68 | <0.001 | * |
Mean pressure, cmH2O | 8.75 ± 1.33 | 13.37 ± 7.38 | <0.001 | * |
Expiratory minute ventilation, L/min | 7.26 ± 1.72 | 10.49 ± 4.13 | <0.001 | * |
Compliance, mL/cmH2O | 60.04 ± 29.42 | 28.78 ± 35.06 | <0.001 | * |
Resistance, mL/cmH2O | 13.68 ± 5.43 | 28.95 ± 33.67 | <0.001 | * |
Arterial blood gas test, ABG, mean ± SD | ||||
SpO2, % | 99.36 ± 31.84 | 63.59 ± 36.53 | <0.001 | * |
pH | 7.03 ± 0.18 | 7.04 ± 0.18 | 0.947 | |
PCO2, mmHg | 37.33 ± 8.15 | 32.03 ± 11.89 | <0.001 | * |
HCO3, mmol/L | 23.57 ± 4.06 | 29.36 ± 11.29 | <0.001 | * |
PO2, mmHg | 162.86 ± 96.22 | 72.75 ± 102.14 | <0.001 | * |
SAO2, % | 154.74 ± 140.22 | 230.17 ± 172.74 | <0.001 | * |
Base Excess, mmol/L | 2.99 ± 21.32 | 36.83 ± 49.32 | <0.001 | * |
Others, mean ± SD | ||||
Systolic blood pressure, mmHg | 124.55 ± 24.57 | 97.67 ± 33.42 | <0.001 | * |
Diastolic blood pressure, mmHg | 66.50 ± 26.76 | 93.78 ± 41.28 | <0.001 | * |
Heart rate, bpm | 82.42 ± 14.98 | 106.87 ± 28.78 | <0.001 | * |
Model | Accuracy | Sensitivity | Specificity | Precision | F1 Score | ROC-AUC | PR-AUC |
---|---|---|---|---|---|---|---|
Artificial neural network | 85.2% | 67.5% | 91.7% | 75.7% | 71.4% | 84.0% | 76.0% |
Decision tree | 87.7% | 66.2% | 93.6% | 79.7% | 72.4% | 84.0% | 79.0% |
Logistic regression | 83.1% | 64.5% | 98.3% | 93.4% | 76.3% | 86.0% | 84.0% |
Random forest | 86.8% | 67.5% | 91.7% | 75.7% | 71.4% | 84.0% | 76.0% |
Support vector machine | 86.8% | 64.2% | 98.8% | 95.5% | 76.8% | 88.0% | 70.0% |
XGBoost | 85.8% | 62.7% | 98.6% | 94.3% | 75.3% | 85.0% | 82.0% |
Variable | Coefficient |
---|---|
Expiratory minute ventilation (L/min) | 0.397 |
Expiratory tidal volume (mL/kg) | −0.010 |
Ventilation rate set (30/min) | 0.094 |
Heart rate (bpm) | 0.017 |
Peak pressure (cmH2O) | 0.069 |
pH | 0.667 |
Age | 0.015 |
Intercept | −11.430 |
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Chen, W.-T.; Huang, H.-L.; Ko, P.-S.; Su, W.; Kao, C.-C.; Su, S.-L. A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients. J. Pers. Med. 2022, 12, 501. https://doi.org/10.3390/jpm12030501
Chen W-T, Huang H-L, Ko P-S, Su W, Kao C-C, Su S-L. A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients. Journal of Personalized Medicine. 2022; 12(3):501. https://doi.org/10.3390/jpm12030501
Chicago/Turabian StyleChen, Wei-Teing, Hai-Lun Huang, Pi-Shao Ko, Wen Su, Chung-Cheng Kao, and Sui-Lung Su. 2022. "A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients" Journal of Personalized Medicine 12, no. 3: 501. https://doi.org/10.3390/jpm12030501