To extubate or not to extubate: Risk factors for extubation failure and deterioration with further mechanical ventilation

Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia

AIM

To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms.

METHODS

A dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model.

RESULTS

According to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF.

CONCLUSIONS

The present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

Independent Risk Factors of Failed Extubation among Adult Critically Ill Patients: A Prospective Observational Study from Saudi Arabia

Background

Mechanical ventilation provides essential support for critically ill patients in several diagnoses; however, extubation failure can affect patient outcomes. From Saudi Arabia, no study has assessed the factors associated with extubation failure in adults.

Methods

This prospective observational study was conducted in the intensive care unit of a tertiary care hospital in Riyadh, Saudi Arabia. Adult patients who had been mechanically ventilated via the endotracheal tube for a minimum of 24 hours and then extubated according to the weaning protocol were included. Failed extubation was defined as reintubation within 48 hours of extubation.

Results

A total of 505 patients were included, of which 72 patients had failed extubation (14.3%, 95% CI: 11.4%-17.7%). Compared with the failed extubation group, the successfully extubated group had significantly shorter duration of mechanical ventilation (mean difference: -2.6 days, 95% CI: -4.3 to -1; P = 0.001), a slower respiratory rate at the time of extubation (mean difference: -2.3 breath/min, 95% CI: -3.8 to -1; P = 0.0005), higher pH (mean difference: 0.02, 95% CI: 0.001-0.04; P = 0.03), and more patients with strong cough (percent difference: 17.7%, 95% CI: 4.8%-30.5%; P = 0.02). Independent risk factors of failed extubation were age (aOR = 1.02; 95% CI: 1.002-1.03; P = 0.03), respiratory rate (aOR = 1.06, 95% CI: 1.01-1.1; P = 0.008), duration of mechanical ventilation (aOR = 1.08, 95% CI: 1.03 - 1.1; P < 0.001), and pH (aOR = 0.02, 95% CI: 0.0006-0.5; P = 0.02).

Conclusion

Older age, longer duration of mechanical ventilation, faster respiratory rate, and lower pH were found to be independent risk factors that significantly increased the odds of extubation failure among adults.

Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters

Background

Successful weaning from mechanical ventilation is important for patients admitted to intensive care units. However, models for predicting real-time weaning outcomes remain inadequate. Therefore, this study aimed to develop a machine-learning model for predicting successful extubation only using time-series ventilator-derived parameters with good accuracy.

Methods

Patients with mechanical ventilation admitted to the Yuanlin Christian Hospital in Taiwan between August 2015 and November 2020 were retrospectively included. A dataset with ventilator-derived parameters was obtained before extubation. Recursive feature elimination was applied to select the most important features. Machine-learning models of logistic regression, random forest (RF), and support vector machine were adopted to predict extubation outcomes. In addition, the synthetic minority oversampling technique (SMOTE) was employed to address the data imbalance problem. The area under the receiver operating characteristic (AUC), F1 score, and accuracy, along with the 10-fold cross-validation, were used to evaluate prediction performance.

Results

In this study, 233 patients were included, of whom 28 (12.0%) failed extubation. The six ventilatory variables per 180 s dataset had optimal feature importance. RF exhibited better performance than the others, with an AUC value of 0.976 (95% confidence interval [CI], 0.975-0.976), accuracy of 94.0% (95% CI, 93.8-94.3%), and an F1 score of 95.8% (95% CI, 95.7-96.0%). The difference in performance between the RF and the original and SMOTE datasets was small.

Conclusion

The RF model demonstrated a good performance in predicting successful extubation in mechanically ventilated patients. This algorithm made a precise real-time extubation outcome prediction for patients at different time points.

Development and validation of a mechanical power-oriented prediction model of weaning failure in mechanically ventilated patients: a retrospective cohort study

OBJECTIVE

To develop and validate a mechanical power (MP)-oriented prediction model of weaning failure in mechanically ventilated patients.

DESIGN

A retrospective cohort study.

SETTING

Data were collected from the large US Medical Information Mart for Intensive Care-IV (MIMIC-IV) V.1.0, which integrates comprehensive clinical data from 76 540 intensive care unit (ICU) admissions from 2008 to 2019.

PARTICIPANTS

A total of 3695 patients with invasive mechanical ventilation for more than 24 hours and weaned with T-tube ventilation strategies were enrolled from the MIMIC-IV database.

PRIMARY AND SECONDARY OUTCOME

Weaning failure.

RESULTS

All eligible patients were randomised into development cohorts (n=2586, 70%) and validation cohorts (n=1109, 30%). Multivariate logistic regression analysis of the development cohort showed that positive end-expiratory pressure, dynamic lung compliance, MP, inspired oxygen concentration, length of ICU stay and invasive mechanical ventilation duration were independent predictors of weaning failure. Calibration curves showed good correlation between predicted and observed outcomes. The prediction model showed accurate discrimination in the development and validation cohorts, with area under the receiver operating characteristic curve values of 0.828 (95% CI: 0.812 to 0.844) and 0.833 (95% CI: 0.809 to 0.857), respectively. Decision curve analysis indicated that the predictive model was clinically beneficial.

CONCLUSION

The MP-oriented model of weaning failure accurately predicts the risk of weaning failure in mechanical ventilation patients and provides valuable information for clinicians making decisions on weaning.

Impairment in Preextubation Alveolar Gas Exchange Is Associated With Postextubation Respiratory Support Needs in Infants After Cardiac Surgery

OBJECTIVES:

DESIGN:

SETTING:

PATIENTS:

INTERVENTIONS:

MEASUREMENTS AND MAIN RESULTS:

CONCLUSIONS:

High Flow Oxygen Therapy at Two Initial Flow Settings versus Conventional Oxygen Therapy in Cardiac Surgery Patients with Postextubation Hypoxemia: A Single-Center, Unblinded, Randomized, Controlled Trial

In cardiac surgery patients with pre-extubation PaO₂/inspired oxygen fraction (FiO₂) < 200 mmHg, the possible benefits and optimal level of high-flow nasal cannula (HFNC) support are still unclear; therefore, we compared HFNC support with an initial gas flow of 60 or 40 L/min and conventional oxygen therapy. Ninety nine patients were randomly allocated (respective ratio: 1:1:1) to I = intervention group 1 (HFNC initial flow = 60 L/min, FiO₂ = 0.6), intervention group 2 (HFNC initial flow = 40 L/min, FiO₂ = 0.6), or control group (Venturi mask, FiO₂ = 0.6). The primary outcome was occurrence of treatment failure. The baseline characteristics were similar. The hazard for treatment failure was lower in intervention group 1 vs. control (hazard ratio (HR): 0.11, 95% CI: 0.03–0.34) and intervention group 2 vs. control (HR: 0.30, 95% CI: 0.12–0.77). During follow-up, the probability of peripheral oxygen saturation (SpO₂) > 92% and respiratory rate within 12–20 breaths/min was 2.4–3.9 times higher in intervention group 1 vs. the other 2 groups. There was no difference in PaO₂/FiO₂, patient comfort, intensive care unit or hospital stay, or clinical course complications or adverse events. In hypoxemic cardiac surgery patients, postextubation HFNC with an initial gas flow of 60 or 40 L/min resulted in less frequent treatment failure vs. conventional therapy. The results in terms of SpO₂/respiratory rate targets favored an initial HFNC flow of 60 L/min.

Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs). Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model. Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction. Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.