Machine Learning Algorithms Predict Successful Weaning from Mechanical Ventilation Before Intubation

Abstract Prediction of successful weaning from mechanical ventilation in advance to intubation can facilitate discussions regarding end-of-life care before unnecessary intubation. In this context, we aimed to develop a machine-learning-based model that predicts successful weaning from ventilator support based on routine clinical and laboratory data taken before or immediately after intubation. We used the Medical Information Mart for Intensive Care-IV database, including adult patients who underwent mechanical ventilation in intensive care at the Beth Israel Deaconess Medical Center, USA. Clinical and laboratory variables collected before or within 24 hours of intubation were used to develop machine-learning models that predict the probability of successful weaning within 14 days of ventilator support. Of 23,242 patients, 19,025 (81.9%) patients were successfully weaned from mechanical ventilation within 14 days. We selected 46 clinical and laboratory variables to create machine-learning models. The machine-learning-based ensemble voting classifier revealed the area under the receiver operating characteristic curve of 0.863 (95% confidence interval 0.855–0.870), which was significantly better than that of Sequential Organ Failure Assessment (0.588 [0.566–0.609]) and Simplified Acute Physiology Score II (0.749 [0.742–0.756]). The top features included lactate, anion gap, and prothrombin time. The model’s performance achieved a plateau with approximately the top 21 variables. We developed machine learning algorithms that can predict successful weaning from mechanical ventilation in advance to intubation in the intensive care unit. Our models can aid in appropriate management for patients who hesitate to decide on ventilator support or meaningless end-of-life care.