Machine learning prediction of the total duration of invasive and non-invasive ventilation During ICU Stay

Predicting the duration of ventilation in the ICU helps in assessing the risk of ventilator-induced lung injury, ensuring sufficient oxygenation, and optimizing resource allocation. Prior models provided a prediction of total duration without distinguishing between invasive and non-invasive ventilation. This work proposes two independent gradient boosting regression models for predicting the duration of invasive and non-invasive ventilation based on commonly available ICU features. These models are trained on 2.6 million patient stays across 350 US hospitals between 2010 to 2019. The mean absolute error (MAE) for the prediction of duration was 2.08 days for invasive ventilation and 0.36 days for non-invasive ventilation. The total ventilation duration predicted by our model had MAE of 2.38 days, which outperformed the gold standard (APACHE) with MAE of 3.02 days. The feature importance analysis of the trained models showed that, for invasive ventilation, high average heart rate, diagnosis of respiratory infection and admissions from locations other than the operating room were associated with longer ventilation durations. For non-invasive ventilation, higher respiratory rates and having any GCS measurement were associated with longer durations.Author summary: This study aimed to improve the accuracy of predicting how long a patient in the ICU will need a ventilator, which is crucial for patient safety and hospital resource management. Previous prediction models did not distinguish between invasive and non-invasive ventilation. However, our research proposes separate models for each method, which we developed using patient data from 350 US hospitals spanning nearly a decade. We used a technique known as gradient boosting regression, which leverages commonly available ICU data. Our models performed significantly better than existing standards, with errors being notably lower. Additionally, our findings highlight key factors that increase ventilation duration, including a high heart rate and a diagnosis of respiratory infection for invasive ventilation, and higher respiratory rates and any Glasgow Coma Scale (GCS) measurement for non-invasive ventilation. These models could therefore aid healthcare professionals in making better-informed decisions on patient treatment and managing their resources more effectively.