Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU.
BACKGROUND
Twenty-five to 40% of patients pass a spontaneous breathing trial (SBT) but fail to wean from mechanical ventilation. There is no single appropriate and convenient predictor or method that can help clinicians to accurately predict weaning outcomes. This study designed an artificial neural network (ANN) model for predicting successful extubation in mechanically ventilated patients.


METHODS
Ready-to-wean subjects (N = 121) hospitalized in medical ICUs were recruited and randomly divided into training (n = 76) and test (n = 45) sets. Eight variables consisting of age, reasons for intubation, duration of mechanical ventilation, Acute Physiology and Chronic Health Evaluation II score, mean inspiratory and expiratory times, mean breathing frequency, and mean expiratory tidal volume in a 30-min SBT (pressure support ventilation of 5 cm H2O and PEEP of 5 cm H2O) were selected as the ANN input variables. The prediction performance of the ANN model was compared with the rapid shallow breathing index (RSBI), maximum inspiratory pressure, RSBI at 1 min (RSBI1) and 30 min (RSBI30) in an SBT, and absolute percentage change in RSBI from 1 to 30 min in an SBT (ΔRSBI30) using a confusion matrix and receiver operating characteristic curves.


RESULTS
The area under the receiver operating characteristic curves in the test set of the ANN model was 0.83 (95% CI 0.69-0.92, P < .001), which is better than any one of the following predictors: 0.66 (95% CI 0.50-0.80, P = .04) for RSBI, 0.52 (95% CI 0.37-0.67, P = .86) for maximum inspiratory pressure, 0.53 (95% CI 0.37-0.68, P = .79) for RSBI1, 0.60 (95% CI 0.44-0.74, P = .34) for RSBI30, and 0.51 (95% CI 0.36-0.66, P = .91) for ΔRSBI30. Predicting successful extubation based on the ANN model of the test set had a sensitivity of 82%, a specificity of 73%, and an accuracy rate of 80%, with an optimal threshold of ≥ 0.5 selected from the training set.


CONCLUSIONS
The ANN model improved the accuracy of predicting successful extubation. By applying it clinically, clinicians can select the earliest appropriate weaning time.
Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan. Graduate Institute of Biomedical Informatics.
Hung-Ju
HJ
Kuo
Graduate Institute of Biomedical Informatics.
Hung-Wen
HW
Chiu
Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan. School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Chun-Nin
CN
Lee
Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan.
Tzu-Tao
TT
Chen
Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan.
Chih-Cheng
CC
Chang
School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan. Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan. mybien@tmu.edu.tw.
Mauo-Ying
MY
Bien
D000368:Aged; D000369:Aged, 80 and over; D060666:Airway Extubation; D005260:Female; D005544:Forecasting / Q000379:methods; D006801:Humans; D007362:Intensive Care Units; D008168:Lung / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012372:ROC Curve; D011897:Random Allocation; D012119:Respiration; D012121:Respiration, Artificial; D012129:Respiratory Function Tests; D015300:Ventilator Weaning
airway extubation; artificial neural network; rapid shallow breathing index; receiver operating characteristic curve; spontaneous breathing trial; weaning prediction