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An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units.

ABSTRACT: BACKGROUND:Successful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs. METHODS:Data from 1/12/2009 through 31/12/2011 of 3602 patients with planned extubation in Chi-Mei Medical Center's ICUs was used to train and test an artificial neural network (ANN). The input was 37 clinical risk factors, and the output was a failed extubation prediction. RESULTS:One hundred eighty-five patients (5.1%) had a failed extubation. Multivariate analyses revealed that failure was positively associated with therapeutic intervention scoring system (TISS) scores (odds ratio [OR]: 1.814; 95% Confidence Interval [CI]: 1.283?2.563), chronic hemodialysis (OR: 12.264; 95% CI: 8.556?17.580), rapid shallow breathing (RSI) (OR: 2.003; 95% CI: 1.378?2.910), and pre-extubation heart rate (OR: 1.705; 95% CI: 1.173?2.480), but negatively associated with pre-extubation PaO?/FiO? (OR: 0.529; 95%: 0.370?0.750) and maximum expiratory pressure (MEP) (OR: 0.610; 95% CI: 0.413?0.899). A multilayer perceptron ANN model with 19 neurons in a hidden layer was developed. The overall performance of this model was F?: 0.867, precision: 0.939, and recall: 0.822. The area under the receiver operating characteristic curve (AUC) was 0.85, which is better than any one of the following predictors: TISS: 0.58 (95% CI: 0.54?0.62; p < 0.001); 0.58 (95% CI: 0.53?0.62; p < 0.001); and RSI: 0.54 (95% CI: 0.49?0.58; p = 0.097). CONCLUSIONS:The ANN performed well when predicting failed extubation, and it will help predict successful planned extubation.

SUBMITTER: Hsieh MH

PROVIDER: S-EPMC6162865 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units.

Hsieh Meng-Hsuen MH Hsieh Meng-Ju MJ Chen Chin-Ming CM Hsieh Chia-Chang CC Chao Chien-Ming CM Lai Chih-Cheng CC

Journal of clinical medicine 20180825 9

<h4>Background</h4>Successful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs.<h4>Methods</h4>Data from 1/12/2009 through 31/12/2011 of 3602 patients with planned extubation in Chi-Mei Medical Center's ICUs was used to train and test an artificial neural network (ANN). The input was 37 clinical risk factors, and the output was a failed extubation ...[more]

PMID: 30149612

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Project description:BACKGROUND:Given its narrow therapeutic range, digoxin's pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to support the safety and dosage requirements of digoxin, let alone to predict its concentrations in infants. This study aimed to compare the concentrations of digoxin predicted by traditional regression modeling and artificial neural network (ANN) modeling for newborn infants given digoxin for clinically significant patent ductus arteriosus (PDA). METHODS:A retrospective chart review was conducted to obtain data on digoxin use for clinically significant PDA in a neonatal intensive care unit. Newborn infants who were given digoxin and had digoxin concentration(s) within the acceptable range were identified as subjects in the training model and validation datasets, accordingly. Their demographics, disease, and medication information, which were potentially associated with heart failure, were used for model training and analysis of digoxin concentration prediction. The models were generated using backward standard multivariable linear regressions (MLRs) and a standard backpropagation algorithm of ANN, respectively. The common goodness-of-fit estimates, receiver operating characteristic curves, and classification of sensitivity and specificity of the toxic concentrations in the validation dataset obtained from MLR or ANN models were compared to identify the final better predictive model. RESULTS:Given the weakness of correlations between actual observed digoxin concentrations and pre-specified variables in newborn infants, the performance of all ANN models was better than that of MLR models for digoxin concentration prediction. In particular, the nine-parameter ANN model has better forecasting accuracy and differentiation ability for toxic concentrations. CONCLUSION:The nine-parameter ANN model is the best alternative than the other models to predict serum digoxin concentrations whenever therapeutic drug monitoring is not available. Further cross-validations using diverse samples from different hospitals for newborn infants are needed.

Dataset Information

An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units.

Publications

An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units.

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