Dataset Information

Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

ABSTRACT: RATIONALE:Though treatment of the prematurely born infant breathing with assistance of a mechanical ventilator has much advanced in the past decades, predicting extubation outcome at a given point in time remains challenging. Numerous studies have been conducted to identify predictors for extubation outcome; however, the rate of infants failing extubation attempts has not declined. OBJECTIVE:To develop a decision-support tool for the prediction of extubation outcome in premature infants using a set of machine learning algorithms. METHODS:A dataset assembled from 486 premature infants on mechanical ventilation was used to develop predictive models using machine learning algorithms such as artificial neural networks (ANN), support vector machine (SVM), naïve Bayesian classifier (NBC), boosted decision trees (BDT), and multivariable logistic regression (MLR). Performance of all models was evaluated using area under the curve (AUC). RESULTS:For some of the models (ANN, MLR and NBC) results were satisfactory (AUC: 0.63-0.76); however, two algorithms (SVM and BDT) showed poor performance with AUCs of ~0.5. CONCLUSION:Clinician's predictions still outperform machine learning due to the complexity of the data and contextual information that may not be captured in clinical data used as input for the development of the machine learning algorithms. Inclusion of preprocessing steps in future studies may improve the performance of prediction models.

SUBMITTER: Mueller M

PROVIDER: S-EPMC4238927 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

Mueller Martina M Almeida Jonas S JS Stanislaus Romesh R Wagner Carol L CL

Journal of neonatal biology 20130101

<h4>Rationale</h4>Though treatment of the prematurely born infant breathing with assistance of a mechanical ventilator has much advanced in the past decades, predicting extubation outcome at a given point in time remains challenging. Numerous studies have been conducted to identify predictors for extubation outcome; however, the rate of infants failing extubation attempts has not declined.<h4>Objective</h4>To develop a decision-support tool for the prediction of extubation outcome in premature i ...[more]

PMID: 25419493

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Project description:BackgroundApproximately 30% of intubated preterm infants with respiratory distress syndrome (RDS) will fail attempted extubation, requiring reintubation and mechanical ventilation. Although ventilator technology and monitoring of premature infants have improved over time, optimal extubation remains challenging. Furthermore, extubation decisions for premature infants require complex informational processing, techniques implicitly learned through clinical practice. Computer-aided decision-support tools would benefit inexperienced clinicians, especially during peak neonatal intensive care unit (NICU) census.MethodsA five-step procedure was developed to identify predictive variables. Clinical expert (CE) thought processes comprised one model. Variables from that model were used to develop two mathematical models for the decision-support tool: an artificial neural network (ANN) and a multivariate logistic regression model (MLR). The ranking of the variables in the three models was compared using the Wilcoxon Signed Rank Test. The best performing model was used in a web-based decision-support tool with a user interface implemented in Hypertext Markup Language (HTML) and the mathematical model employing the ANN.ResultsCEs identified 51 potentially predictive variables for extubation decisions for an infant on mechanical ventilation. Comparisons of the three models showed a significant difference between the ANN and the CE (p = 0.0006). Of the original 51 potentially predictive variables, the 13 most predictive variables were used to develop an ANN as a web-based decision-tool. The ANN processes user-provided data and returns the prediction 0-1 score and a novelty index. The user then selects the most appropriate threshold for categorizing the prediction as a success or failure. Furthermore, the novelty index, indicating the similarity of the test case to the training case, allows the user to assess the confidence level of the prediction with regard to how much the new data differ from the data originally used for the development of the prediction tool.ConclusionState-of-the-art, machine-learning methods can be employed for the development of sophisticated tools to aid clinicians' decisions. We identified numerous variables considered relevant for extubation decisions for mechanically ventilated premature infants with RDS. We then developed a web-based decision-support tool for clinicians which can be made widely available and potentially improve patient care world wide.

Project description:BackgroundICU-acquired weakness (ICUAW) has been shown to be associated with prolonged duration of mechanical ventilation and extubation failure. It is usually assessed through Medical Research Council (MRC) score, a time-consuming score performed by physiotherapists. Handgrip strength (HG) can be monitored very easily at the bedside. It has been shown to be a reproducible and reliable marker of global muscular strength in critical care patients. We sought to test if muscular weakness, as assessed by handgrip strength, was associated with extubation outcome.MethodsProspective multicenter trial over an 18 months period in six mixed ICUs. Adults receiving mechanical ventilation for at least 48 h were eligible. Just before weaning trial, HG, Maximal Inspiratory Pressure (MIP), Peak Cough Expiratory Flow (PCEF) and Medical Research Council (MRC) score were registered. The attending physicians were unaware of the tests results and weaning procedures were conducted according to guidelines. Occurrence of unscheduled reintubation, non-invasive ventilation (NIV) or high-flow nasal continuous oxygen (HFNC) because of respiratory failure within 7 days after extubation defined extubation failure. The main outcome was the link between HG and extubation outcome.Results233 patients were included. Extubation failure occurred in 51 (22.5%) patients, 39 (17.2%) required reintubation. Handgrip strength was 12 [6-20] kg and 12 [8-20] kg, respectively, in extubation success and failure (p = 0.85). There was no association between extubation outcome and MRC score, MIP or PCEF. Handgrip strength was well correlated with MRC score (r = 0.718, p < 0.0001). ICU and hospital length of stay were significantly higher in the subset of patients harboring muscular weakness as defined by handgrip performed at the first weaning trial (respectively, 15 [10-25] days vs. 11 [7-17] days, p = 0.001 and 34 [19-66] days vs. 22 [15-43] days, p = 0.002).ConclusionNo association was found between handgrip strength and extubation outcome. Whether this was explained by the appropriateness of the tool in this specific setting, or by the precise impact of ICUAW on extubation outcome deserves to be further evaluated. Trial registration Clinical Trials; NCT02946502, 10/27/2016, URL: https://clinicaltrials.gov/ct2/results?cond=&term=gripwean&cntry=&state=&city=&dist=.

Project description:ObjectivesTo evaluate the contribution of a preextubation chest X-ray (CXR) to identify the risk of extubation failure in mechanically ventilated patients.DesignRetrospective cohort study.SettingsICUs in a tertiary center (the Medical Information Mart for Intensive Care IV database).PatientsPatients greater than or equal to 18 years old who were mechanically ventilated and extubated after a spontaneous breathing trial.InterventionsNone.Measurements and main resultsAmong 1,066 mechanically ventilated patients, 132 patients (12%) experienced extubation failure, defined as reintubation or death within 48 hours of extubation. To predict extubation failure, we developed the following models based on deep learning (EfficientNet) and machine learning (LightGBM) with the training data: 1) model using only the rapid-shallow breathing index (RSBI), 2) model using RSBI and CXR, 3) model using all candidate clinical predictors (i.e., patient demographics, vital signs, laboratory values, and ventilator settings) other than CXR, and 4) model using all candidate clinical predictors with CXR. We compared the predictive abilities between models with the test data to investigate the predictive contribution of CXR. The predictive ability of the model using CXR as well as RSBI was not significantly higher than that of the model using only RSBI (c-statistics, 0.56 vs 0.56; p = 0.95). The predictive ability of the model using clinical predictors with CXR was not significantly higher than that of the model using all clinical predictors other than CXR (c-statistics, 0.71 vs 0.70; p = 0.12). Based on SHapley Additive exPlanations values to interpret the model using all clinical predictors with CXR, CXR was less likely to contribute to the predictive ability than other predictors (e.g., duration of mechanical ventilation, inability to follow commands, and heart rate).ConclusionsAdding CXR to a set of other clinical predictors in our prediction model did not significantly improve the predictive ability of extubation failure in mechanically ventilated patients.

Project description:Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.

Dataset Information

Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

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Can Machine Learning Methods Predict Extubation Outcome in Premature Infants as well as Clinicians?

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