Improvement in the prediction of ventilator weaning outcomes by an artificial neural network in a medical ICU

研究成果: 雜誌貢獻文章

15 引文 (Scopus)

摘要

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 (RSBI30) 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 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.
原文英語
頁(從 - 到)1560-1569
頁數10
期刊Respiratory Care
60
發行號11
DOIs
出版狀態已發佈 - 十一月 1 2015

指紋

Ventilator Weaning
Respiration
Neural Networks (Computer)
Weaning
Artificial Respiration
ROC Curve
APACHE
Tidal Volume
Intubation
Ventilation
Pressure

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Critical Care and Intensive Care Medicine

引用此文

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title = "Improvement in the prediction of ventilator weaning outcomes by an artificial neural network in a medical ICU",
abstract = "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 (RSBI30) 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 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.",
keywords = "Airway extubation, Artificial neural network, Rapid shallow breathing index, Receiver operating characteristic curve, Spontaneous breathing trial, Weaning prediction",
author = "Kuo, {Hung Ju} and Chiu, {Hung Wen} and Lee, {Chun Nin} and Chen, {Tzu Tao} and Chang, {Chih Cheng} and Bien, {Mauo Ying}",
year = "2015",
month = "11",
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T1 - Improvement in the prediction of ventilator weaning outcomes by an artificial neural network in a medical ICU

AU - Kuo, Hung Ju

AU - Chiu, Hung Wen

AU - Lee, Chun Nin

AU - Chen, Tzu Tao

AU - Chang, Chih Cheng

AU - Bien, Mauo Ying

PY - 2015/11/1

Y1 - 2015/11/1

N2 - 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 (RSBI30) 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 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.

AB - 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 (RSBI30) 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 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.

KW - Airway extubation

KW - Artificial neural network

KW - Rapid shallow breathing index

KW - Receiver operating characteristic curve

KW - Spontaneous breathing trial

KW - Weaning prediction

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