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Updated: May 3, 2026
Evaluation of Right Ventricular Function in Experimental Models of Pulmonary Arterial Hypertension
Published on: June 27, 2025
Performance of the REVEAL pulmonary arterial hypertension prediction model using non-invasive and routinely measured
Rebecca Cogswell1, Marc Pritzker1, Teresa De Marco2
1Division of Cardiology, University of Minnesota, Minneapolis, Minnesota.
Insights
Simplifying the REVEAL model for pulmonary arterial hypertension (PAH) using fewer predictors maintained its predictive performance for 1-year survival. This suggests a more streamlined approach to assessing PAH prognosis is feasible.
Area of Science:
- Cardiology
- Pulmonology
- Medical Informatics
Background:
- The REVEAL model, a 19-predictor tool, estimates 1-year survival in pulmonary arterial hypertension (PAH).
- Identifying essential predictors for serial REVEAL score calculation is crucial for efficient patient monitoring.
- This study investigated the possibility of simplifying the REVEAL model without sacrificing its predictive accuracy.
Purpose of the Study:
- To identify high-yield predictors within the REVEAL score for pulmonary arterial hypertension (PAH).
- To determine if the REVEAL model can be simplified without compromising its performance in predicting 1-year outcomes.
- To explore the development of a simplified clinical model for PAH risk assessment.
Main Methods:
- Calculated REVEAL scores using the full 19 predictors in 140 PAH patients.
- Recalculated scores using a simplified model excluding right heart catheterization and pulmonary function tests.
- Developed a clinical model using only PAH type, NYHA class, BNP, renal function, and echocardiographic right atrial pressure.
Main Results:
- The c-indices for predicting 1-year survival were statistically similar across the Full REVEAL Model (0.765), Simple Model (0.759), and Clinical Model (0.745) (p=0.92).
- For the composite outcome of survival or freedom from lung transplant at 1 year, model performance (c-indices) was also not statistically different (Full: 0.805, Simple: 0.809, Clinical: 0.785; p=0.73).
Conclusions:
- The original, comprehensive REVEAL Model demonstrated comparable performance even when the number of predictors was significantly reduced.
- There is a clear opportunity to re-evaluate existing PAH registry data to pinpoint high-yield variables.
- Developing a simplified clinical model for PAH risk stratification is a viable and potentially beneficial endeavor.
Background:
The REVEAL model for pulmonary arterial hypertension (PAH) uses 19 predictors to calculate a 1-year survival probability and can be repeated over time. It is currently unclear which of the 19 variables are the most essential for serial REVEAL score calculation. We aimed to identify high-yield predictors in the REVEAL score and hypothesized that the model could be simplified considerably without compromising performance.
Methods:
REVEAL scores were calculated in a cohort of 140 PAH patients (Full REVEAL Model). Scores were then recalculated excluding all right heart catheterization and pulmonary function test data (Simple Model) and again using only PAH type, New York Heart Association class, brain natriuretic peptide, renal function and right atrial pressure by echocardiogram (Clinical Model). The models were then tested for the ability to predict 1-year outcomes and the performance of the models was compared.
Results:
The c indices of the models to predict 1-year survival were not statistically different from one another (Full REVEAL Model: 0.765; Simple Model: 0.759; Clinical Model: 0.745; p = 0.92). For the composite outcome of survival or freedom from lung transplant at 1 year, the models were again not statistically different from one another (c indices: Full REVEAL Model: 0.805; Simple Model: 0.809; Clinical Model: 0.785; p = 0.73).
Conclusions:
The original, Full REVEAL Model appeared to have comparable performance after selectively limiting the number of predictors. There is opportunity to re-evaluate large-registry PAH data to identify a limited number of high-yield variables and to develop a simplified, clinical model.
