Predicting weaning difficulty for planned extubation patients with an artificial neural network

Hsieh, Meng Hsuen; Hsieh, Meng Ju; Cheng, Ai‐Chin; Chen, Chin‐Ming; Hsieh, Chih-Chang; Chao, Chien‐Ming; Lai, Chih‐Cheng; Cheng, Kuo-Chen; Chou, Willy

doi:10.1097/md.0000000000017392

Cited by 16 publications

(20 citation statements)

References 20 publications

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“…This study is unique as it provides predictive modeling of extubation failure across twenty-five hospitals. All previous machine learning studies in non-COVID patients for predicting extubation failure have been single center [ 7 – 15 ]. Model performance was higher in these studies, presumably due to overfitting resulting from the sole use of local data.…”

Section: Discussionmentioning

confidence: 99%

“…The collection of large intensive care datasets that span the entire intensive care admission paves the way for machine learning models to capture this complex interplay of predictors by using machine learning models. Previous non-COVID-19 machine learning work has aimed to predict simple and difficult weaning [ 7 ] and extubation failure [ 8 – 15 ]. However, data was frequently from over a decade ago, mechanical ventilator data was usually lacking, and no data was included from COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

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Predictors for extubation failure in COVID-19 patients using a machine learning approach

Fleuren

Dam

Tonutti³

et al. 2021

Crit Care

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Introduction Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. Methods We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. Results A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. Conclusion The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictors for extubation failure in COVID-19 patients using a machine learning approach

Fleuren

Dam

Tonutti³

et al. 2021

Crit Care

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“…Weaning difficulty prediction was done by Hsieh et.al in 2019 using ANN. The model was under construction by using differing data [18] . Kuo et al developed an ANN based decision support system for extubation decision systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

Radhakrishnan

Nair

Isaac

2022

Biomedical Signal Processing and Control

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Background and objective In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals. Methods The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming. Results Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs. Conclusions Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.

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“…AI experiments have created predictive models based on this classification system to predict difficulty of extubation before the weaning process to aid management strategy. 26…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

AI in the Intensive Care Unit: Up-to-Date Review

Nguyen

Ngo

vanSonnenberg

2020

J Intensive Care Med

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AI is the latest technologic trend that likely will have a huge impact in medicine. AI’s potential lies in its ability to process large volumes of data and perform complex pattern analyses. The ICU is an area of medicine that is particularly conducive to AI applications. Much AI ICU research currently is focused on improving high volumes of data on high-risk patients and making clinical workflow more efficient. Emerging topics of AI medicine in the ICU include AI sensors, sepsis prediction, AI in the NICU or SICU, and the legal role of AI in medicine. This review will cover the current applications of AI medicine in the ICU, potential pitfalls, and other AI medicine-related topics relevant for the ICU.

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Predicting weaning difficulty for planned extubation patients with an artificial neural network

Abstract: Supplemental Digital Content is available in the text

Cited by 16 publications

References 20 publications

Predictors for extubation failure in COVID-19 patients using a machine learning approach

Predictors for extubation failure in COVID-19 patients using a machine learning approach

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

AI in the Intensive Care Unit: Up-to-Date Review

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