Performance of the REVEAL pulmonary arterial hypertension prediction model using non-invasive and routinely measured parameters

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• 作者：Rebecca Cogswell ; MD^a ; ^{cogsw014@umn.edu" class="auth_mail} ; Marc Pritzker ; MD^a ; Teresa De Marco ; MD^b
• 关键词：pulmonary arterial hypertension ; prediction model ; survival ; REVEAL
• 刊名：The Journal of Heart and Lung Transplantation
• 出版年：April, 2014
• 年：2014
• 卷：33
• 期：4
• 页码：382-387
• 全文大小：756 K

文摘

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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.