Clinical Variables in Predicting Survival and Hospitalization for Pulmonary Arterial Hypertension Using Harmonized Data

Abstract

<h3>Purpose</h3> Pulmonary arterial hypertension (PAH) is a type of high blood pressure that affects arteries in the lungs and the heart. Although clinical trials have been actively conducted, data from different trials are seldom harmonized for a more powerful study. In this study, we built a repository of harmonized data for adult PAH research. We then used the harmonized data to analyze clinical variables in predicting survival, and we also demonstrated the potential utility of the data for predicting hospitalization. <h3>Methods</h3> We cleaned data from seven adult PAH trials: GRIPHON, SERAPHIN, EARLY, COMPASS-2, COMPASS-3, MAESTRO and TRANSIT-1. Data from separate studies were merged as one harmonized data set. Using the harmonized data, we conducted Cox proportional hazard analysis to study the association between time to death at 30 days and each of two variables obtained at baseline, pulmonary artery pulsatility index (PAPi) and aortic pulsatility index (API). Using data from the GRIPHON study, we also constructed a Bayesian network to predict the outcome of hospitalization from clinical variables. The Tree-Augmented naive Bayes algorithm was implemented. The cutoff values discretizing continuous variables were obtained using a classification tree. <h3>Results</h3> The harmonized data comprise 2,870 subjects and 178 variables, with rich information including demographic, laboratory, electrocardiogram, vital sign, right heart catheterization, echocardiography, hospitalization and mortality. In our analysis, PAPi was shown to be significantly associated with time to death at 30 days. The p-value was 0.010 and the hazard ratio was 0.838 with a 95% confidence interval being (0.734, 0.958). The top three nodes of the classification tree were bilirubin less than 0.50 mg/dl, blood urea nitrogen less than 30.10 mg/dl, and WHO functional class II. Using the GRIPHON study as an example, the Bayesian network showed bilirubin, the top variable selected by the classification tree, was correlated with NP-proBNP, a well-known biomarker for evaluating the severity of heart disease. <h3>Conclusion</h3> The harmonized data is a valuable resource for PAH research. Our results suggest that PAPi may be an important biomarker for predicting survival. Additional biomarkers may also be minded from the harmonized data for other outcomes of interest, including hospitalization.