Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU

Mechanical ventilation is a critical life support technology in the intensive care unit. However, the weaning process remains complex, making the optimal timing for liberation from ventilation challenging to ascertain and imposing a considerable clinical workload. Additionally, advanced weaning assistance tools that integrate multidimensional clinical factors to help clinical staff make precise decisions during the weaning process are lacking. The aim of this study to develop and validate an interpretable machine learning model that comprehensively evaluates the factors influencing weaning to provide clinical decision support for weaning.

We collected data from the ICU of Shanghai Tenth People's Hospital and its affiliated hospitals. Ten distinct machine learning algorithms for predicting extubation outcomes in patients receiving mechanical ventilation were developed and internally validated. Model performance was quantified using the area under the receiver operating characteristic curve AUC, overall accuracy, sensitivity, specificity, and F1 score. NRI, IDI, and DCA were used to comprehensively identify the optimal model. The relative contribution of each predictor was ranked and compared through SHAP analysis, and the best-performing model was externally validated.

Through univariate and LASSO analyses, 24 predictive variables for machine learning model construction were identified. Comprehensive evaluation showed that among the candidate algorithms, the LGB model demonstrated the highest overall performance. SHAP analysis revealed that the top-ranked features for predicting successful liberation from mechanical ventilation were creatinine levels, lactate levels, the level of consciousness, SpO2, systolic blood pressure, cough reflex, chronic respiratory disease, diastolic blood pressure, and age.

The optimized predictive model, which was developed through the integration of multidimensional predictive factors with diverse machine learning algorithms, exhibits superior predictive accuracy and demonstrates significant clinical potential for determining the optimal timing for weaning patients receiving invasive mechanical ventilation. Trial registration Current Controlled Trials ChiCTR2400093658; registration date: December 10, 2024.