CT-Based Biomarkers for Prediction of Chronic Thromboembolic Pulmonary Hypertension After an Acute Pulmonary Embolic

Giovanni Lorenz1,2, Mnahi Bin Saeedan1,2, Jennifer Bullen3

  • 1Section of Thoracic Imaging, Imaging Institute, Cleveland Clinic, L-10, 413-4, 9500 Euclid Ave, Cleveland, OH 44195.

Insights

The degree of clot obstruction on initial CT scans may predict chronic thromboembolic pulmonary hypertension (CTEPH) development after acute pulmonary embolism. Mosaic attenuation and lung infarction also show potential predictive value for CTEPH.

Area of Science:

  • Radiology
  • Pulmonary Medicine
  • Cardiovascular Imaging

Background:

  • Acute pulmonary embolism (PE) can lead to chronic thromboembolic pulmonary hypertension (CTEPH).
  • Identifying early CT markers for CTEPH development is crucial for timely intervention.
  • Predictive markers can improve patient outcomes and resource allocation.

Purpose of the Study:

  • To evaluate CT-based imaging features that predict the development of CTEPH following acute PE.
  • To identify specific quantitative and qualitative CT findings associated with CTEPH progression.

Main Methods:

  • Retrospective analysis of CT scans from 48 CTEPH patients and 113 controls with acute PE.
  • Assessment of CT markers including clot obstruction, Walsh score, RV/LV ratio, and presence of lung infarction or mosaic attenuation.
  • Classification and regression tree analysis for predictive model development and external validation.

Main Results:

  • Increased clot obstruction, decreased Walsh score, lung infarction, and mosaic attenuation were associated with higher CTEPH probability.
  • The final decision tree model identified occlusive clot as a key predictor.
  • External validation showed the model correctly classified 33% of CTEPH cases and 86% of controls.

Conclusions:

  • Occlusive clot on initial CT scans is a significant predictor of CTEPH development.
  • Mosaic attenuation and lung infarction may also serve as predictive markers for CTEPH.
  • Further research is warranted to validate these findings and refine predictive models.

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