Prediction of Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography

What this Trial Is About

Researchers are developing an artificial intelligence tool to predict adverse outcomes in patients with acute pulmonary embolism using data from CT pulmonary angiography. This observational study collects clinical, laboratory, and CT scan information from patients admitted with acute pulmonary embolism to better understand and forecast complications within 30 days of hospital admission. The goal is to improve early risk assessment by comparing the new AI-based prediction model with established risk systems. The study divides participants into two groups: a derivation cohort used to create and test a logistic regression model, and a validation cohort to confirm the model's consistency. No interventions or treatments are given as part of the study. Data collected include CT parameters, echocardiography results, and blood markers such as cardiac troponin I and NT-pro BNP. The model's accuracy is assessed by comparing predicted outcomes with actual adverse events. Participants will have their clinical and imaging data analyzed from hospital admission, with follow-up to track treatment-emergent adverse events within 30 days and up to 2 years. Researchers monitor the incidence of these adverse events to evaluate the AI tool's predictive value. The entire participation involves data collection without experimental treatment, and the study spans several years to gather long-term safety information.

Adverse Outcome of Acute Pulmonary Embolism by Artificial Intelligence System Based on CT Pulmonary Angiography