CT imaging biomarkers to predict severity and prognosis of pulmonary hypertension
Abstract
Purpose To explore whether there are computed tomography (CT) imaging biomarkers that can stratify the severity of patients with pulmonary hypertension (PH). Methods We retrospectively enrolled 144 consecutive patients with suspected PH who underwent CT pulmonary angiography and right heart catheterization (RHC). CT findings were analyzed by two observers for large vessel size [ascending aorta (A), pulmonary artery (P), inferior vena cava (IVC)], each chamber size, and septal angle. We investigated the associations between CT imaging parameters and the mean pulmonary artery pressure (mPAP) from RHC. During a median follow-up of 36 months, we observed major adverse cardiovascular events (MACE; all-cause mortality and hospitalization for PH worsening). Univariate and multivariate Cox regression models were used with hazard ratios (HR) and 95% confidence intervals (95% CI) to determine independent predictors of MACE in patients with PH. Results Of 144 patients, 116 (80.2%) were diagnosed with PH based on an mPAP of 20 mmHg. Among CT parameters, P, P/A ratio, right ventricle (RV), and RV/left ventricle (LV) ratio were strongly correlated with mPAP values (Pearson’s correlation coefficient, all r < 0.001). During the follow-up period, 44 (30.6%) patients developed MACE (14 deaths and 30 hospitalizations). Using multivariate Cox regression analysis, the RV/LV ratio (HR 2.32; 95% CI: 1.17–4.59) was the best predictor of MACE, followed by age (HR 1.03, 95% CI;1.00–1.05) (all p < 0.05). Among various CT parameters, A, P, and P/A ratio showed excellent reliability with intraclass correlation coefficient ≥ 0.95. Conclusion Among CT parameters, the RV/LV ratio was the most robust predictor of MACE in patients with PH, while the P and P/A ratios served as reliable indicators reflecting mPAP levels.
Article Details
Authors (6)
Do won Yoon
Yeonyee E. Yoon
In Chang Hwang
WonJae Lee
School of Biological Sciences, Seoul National University
Ki-Yeal Lee
Eun Ju Chun