Development and validation of a Prediction Model for Chronic Thromboembolic Pulmonary Disease

Area of Science:

• Cardiology
• Pulmonology
• Medical Diagnostics

Background:

• Acute pulmonary embolism (APE) is a serious condition with high mortality.
• A significant portion of APE survivors develop chronic thromboembolic pulmonary disease (CTEPD), impacting long-term prognosis.
• Early diagnosis of CTEPD remains a challenge, necessitating improved predictive tools.

Purpose of the Study:

• To establish and validate a predictive model for the early identification of CTEPD in APE patients.
• To identify independent risk factors associated with the development of CTEPD.

Main Methods:

• Prospective study of 464 APE patients across 7 Chinese centers.
• Risk factors identified using univariate and multivariate logistic regression.
• A nomogram predictive model was developed and validated using decision curve analysis and ROC analysis.

Main Results:

• 28% of APE patients developed CTEPD; 17% of these progressed to chronic thromboembolic pulmonary hypertension (CTEPH).
• Independent predictors for CTEPD included: delayed diagnosis (≥15 days), recurrent pulmonary embolism (RPE), right ventricular dysfunction (RVD), central embolus, and residual pulmonary vascular obstruction (RPVO) >10%.
• The nomogram demonstrated high predictive performance (C-index 0.895), with AUCs of 0.908 (training) and 0.875 (validation) for early CTEPD identification.

Conclusions:

• A reliable nomogram for predicting CTEPD has been successfully established and validated.
• This tool can assist clinicians in the earlier identification of high-risk patients for CTEPD.
• Earlier identification facilitates timely intervention and potentially improves outcomes for APE survivors at risk of CTEPD.