A Risk Prediction Model for Efficient Intubation in the Emergency Department: A Five-Year Single-Center Retrospective Analysis

Abstract

Abstract Background To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning (ML) algorithms. Methods This study was conducted in an academic tertiary hospital in Hangzhou, China. Critical ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs’ examination, and status of mechanical ventilation were recorded. These patients were assigned to the positive and negative groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression (LR), artificial neural network (ANN), and random forest (RF). SHapley Additive exPlanations (SHAP) was used to analyze the risk factors of intubated critically ill patients in the ED. Then, the best performance of the predictive model was used for auxiliary diagnosis. Results Of 14,589 critically ill patients, 10212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, the mean scores of vital signs, the parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest AUROC (0.8353) for predicting ED intubation. Conclusions For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.