A Predictive Tool for Predicting Adverse Outcomes in Acute Pulmonary Embolism Patients Using Parameters Obtained by Computed Tomographic Pulmonary Angiography.

What this Trial Is About

Researchers are studying acute pulmonary embolism, a condition where blood clots block lung arteries, to predict possible serious outcomes within 30 days after hospital admission. This observational study collects clinical, laboratory, and CT scan data from patients diagnosed with acute pulmonary embolism. The goal is to develop a predictive tool using these data to identify patients at higher risk of adverse events. Patients diagnosed with pulmonary embolism by CT pulmonary angiography and aged 18 or older are included. Participants are divided into two groups: one to create and assess a prediction model using logistic regression, and the other to validate this model. The model's accuracy is compared to existing risk assessment systems, aiming to improve early identification of patients who may experience complications. During the study, clinical and imaging data, including CT scans and laboratory tests like cardiac troponin I and NT-pro BNP levels, are collected from admission. Researchers monitor outcomes over 7 and 30 days to evaluate the occurrence of adverse events. The study tracks participants' health status to assess the prediction tool's performance and safety throughout the monitoring period.

A Predictive Tool for Predicting Adverse Outcomes in Acute Pulmonary Embolism Patients Using CTPA.