MACHINE LEARNING FOR PREDICTING HEMODYNAMIC DETERIORATION OF PATIENTS WITH INTERMEDIATE-RISK PULMONARY EMBOLISM IN INTENSIVE CARE UNIT

Abstract

ABSTRACT Background: Intermediate-risk pulmonary embolism (PE) patients in the intensive care unit (ICU) are at a higher risk of hemodynamic deterioration than those in the general ward. This study aimed to construct a machine learning (ML) model to accurately identify the tendency for hemodynamic deterioration in the ICU patients with intermediate-risk PE. Method: A total of 704 intermediate-risk PE patients from the MIMIC-IV database were retrospectively collected. The primary outcome was defined as hemodynamic deterioration occurring within 30 days after admission to ICU. Four ML algorithms were used to construct models on the basis of all variables from MIMIC IV database with missing values less than 20%. The extreme gradient boosting (XGBoost) model was further simplified for clinical application. The performance of the ML models was evaluated by using the receiver operating characteristic curve, calibration plots, and decision curve analysis. Predictive performance of simplified XGBoost was compared with the simplified Pulmonary Embolism Severity Index score. SHapley Additive explanation (SHAP) was performed on a simplified XGBoost model to calculate the contribution and impact of each feature on the predicted outcome and presents it visually. Results: Among the 704 intermediate-risk PE patients included in this study, 120 patients experienced hemodynamic deterioration within 30 days after admission to the ICU. Simplified XGBoost model demonstrated the best predictive performance with an area under the curve of 0.866 (95% confidence interval, 0.800–0.925), and after recalibrated by isotonic regression, the area under the curve improved to 0.885 (95% confidence interval, 0.822–0.935). Based on the simplified XGBoost model, a web app was developed to identify the tendency for hemodynamic deterioration in ICU patients with intermediate-risk PE. Conclusion: A simplified XGBoost model can accurately predict the occurrence of hemodynamic deterioration for intermediate-risk PE patients in the ICU, assisting clinical workers in providing more personalized management for PE patients in the ICU.