CT pulmonary angiography-based scoring system to predict the prognosis of acute pulmonary embolism

Background: The purpose is to develop a comprehensive risk-scoring system based on CT findings for predicting 30-day mortality after acute pulmonary embolism (PE), and to compare it with PE Severity Index (PESI). Materials and methods: The study included consecutive 1698 CT pulmonary angiograms (CTPA) positive for acute PE performed at a single institution (2003-2010). Two radiologists independently assessed each study regarding clinically relevant findings and then performed adjudication. These variables plus patient clinical information were included to build a LASSO logistic regression model to predict 30-day mortality. A point score for each significant variable was generated based on the final model. PESI score was calculated in 568 patients who visited the hospital after 2007. Results: Inter-reader agreements of interpretations were >95% except for septal bowing (92%). The final prediction model showed superior ability over PESI (AUC = 0.822 vs 0.745) for predicting all-cause 30 day mortality (12.4%). The scoring system based on the significant variables (age (years), pleural effusion (+20), pericardial effusion (+20), lung/liver/bone lesions suggesting malignancy (+60), chronic interstitial lung disease (+20), enlarged lymph node in thorax (+20), and ascites (+40)) stratified patients into 4 severity categories, with mortality rates of 0.008% in class-I (<= 50 pt), 3.8% in class-II (51-100 pt), 17.6% in class-III (101-150 pt), and 40.9% in class-IV (>150 pt). The mortality rate in the CTPA-high risk category (class-IV) was higher than those in the PESI's high risk (27.4%) and very high risk (25.2%) categories. Conclusion: The CTPA-based model was superior to PESI in predicting 30-day mortality. Incorporating the CTPA-based scoring system into image interpretation workflows may help physicians to select the most appropriate management approach for individual patients. (C) 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.