Hung-Ju Kuo1, Hung-Wen Chiu2, Chun-Nin Lee3, Tzu-Tao Chen4, Chih-Cheng Chang4, Mauo-Ying Bien5. 1. Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan. Graduate Institute of Biomedical Informatics. 2. Graduate Institute of Biomedical Informatics. 3. 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. 4. Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan. 5. 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.
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 (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.
RCT Entities:
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.
Authors: Lucas M Fleuren; Tariq A Dam; Michele Tonutti; Daan P de Bruin; Robbert C A Lalisang; Diederik Gommers; Olaf L Cremer; Rob J Bosman; Sander Rigter; Evert-Jan Wils; Tim Frenzel; Dave A Dongelmans; Remko de Jong; Marco Peters; Marlijn J A Kamps; Dharmanand Ramnarain; Ralph Nowitzky; Fleur G C A Nooteboom; Wouter de Ruijter; Louise C Urlings-Strop; Ellen G M Smit; D Jannet Mehagnoul-Schipper; Tom Dormans; Cornelis P C de Jager; Stefaan H A Hendriks; Sefanja Achterberg; Evelien Oostdijk; Auke C Reidinga; Barbara Festen-Spanjer; Gert B Brunnekreef; Alexander D Cornet; Walter van den Tempel; Age D Boelens; Peter Koetsier; Judith Lens; Harald J Faber; A Karakus; Robert Entjes; Paul de Jong; Thijs C D Rettig; Sesmu Arbous; Sebastiaan J J Vonk; Mattia Fornasa; Tomas Machado; Taco Houwert; Hidde Hovenkamp; Roberto Noorduijn Londono; Davide Quintarelli; Martijn G Scholtemeijer; Aletta A de Beer; Giovanni Cinà; Adam Kantorik; Tom de Ruijter; Willem E Herter; Martijn Beudel; Armand R J Girbes; Mark Hoogendoorn; Patrick J Thoral; Paul W G Elbers Journal: Crit Care Date: 2021-12-27 Impact factor: 9.097