The Contribution of Chest X-Ray to Predict Extubation Failure in Mechanically Ventilated Patients Using Machine Learning-Based Algorithms

Abstract

Objectives: To evaluate the contribution of a preextubation chest X-ray (CXR) to identify the risk of extubation failure in mechanically ventilated patients.

Design: Retrospective cohort study.

Settings: ICUs in a tertiary center (the Medical Information Mart for Intensive Care IV database).

Patients: Patients greater than or equal to 18 years old who were mechanically ventilated and extubated after a spontaneous breathing trial.

Interventions: None.

Measurements and main results: Among 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).

Conclusions: Adding 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.

Keywords: chest X-ray; extubation; intubation; machine learning; mechanical ventilation; reintubation.

Figure 1.

Flow diagram of study participants’ selection for analysis. Among a total of 76,540 ICU admissions from 2008 through 2019, we identified 1,066 mechanically ventilated patients. RR = respiratory rate, RSBI = Rapid-Shallow Breathing Index, SBT = spontaneous breathing trial, TV = tidal volume.

Figure 2.

Curves to evaluate predictive abilities of the EfficientNet-based model and four LightGBM-based models. A, Receiver-operating-characteristic curves. The corresponding values of the area under the receiver-operating-characteristic curve for each model (i.e., the c-statistics) are presented in Table 1. B, Precision-recall curves. The corresponding values of the area under the precision-recall curve for each model are presented in Table 1. C, Decision curve analysis. The X-axis indicates threshold probabilities for extubation failure. The Y-axis indicates net benefit. The unit of the net benefit is true positive. CXR = chest X-ray, RSBI = Rapid-Shallow Breathing Index.

Figure 3.

SHapley Additive exPlanations (SHAP) summary plot of top 20 variables of LightGBM-based model using all candidate clinical predictors with chest X-ray (CXR). The horizontal axis represents SHAP values, and a dot indicates the attribution of each variable at a feature value from the data sample. The color of a dot indicates the absolute value of each variable (e.g., red dots represent higher feature values, and blue dots represent lower feature values). The higher the SHAP value, the higher the possibility of extubation failure. The vertical axis represents all variables input to the prediction models, which are sorted based on the impact on the prediction models, which was calculated by averages of absolute SHAP values across all data. APTT = activated partial thromboplastin time, GCS = Glasgow Coma Scale, PFR = Pao₂/Fio₂ ratio, PT-INR = prothrombin time-international normalized ratio, RSBI = Rapid-Shallow Breathing Index.