Kanwar, M. K., Krainsangka, J., Scott, J., Perer, A., Antaki, J., Benza, R. “Hemodynamic Parameters in Predicting Time to Clinical Worsening in Pulmonary Arterial Hypertension.” The Journal of Heart and Lung Transplantation, vol. 40, no. 4, Apr. 2021, p. S189.
Hemodynamics are considered to be extremely relevant when clinically assessing patients with PAH, especially for estimating prognosis. Yet, the role hemodynamic data in PAH in predicting clinical worsening remains controversial, having been derived from limited data-sets.
We analyzed hemodynamic variables obtained at baseline in 3 PAH clinical trials: SERAPHIN, BREATHE-1 and PATENT-2. Hemodynamics obtained at follow-up (early time point) were analyzed from SERAPHIN (week 26) and PATENT-2 (week 12) trials. Primary outcome analyzed was time to clinical worsening for duration of the trial, which was defined as hospitalization for worsening of PAH, need for lung transplantation or balloon septostomy, 15% decrease in 6-minute walk distance from baseline, worsening of WHO functional class or need for additional PAH-therapy. P-values of each hemodynamic variable were aggregated across the clinical trials using Stouffer’s method, weighed by their ‘n’ in clinical trials. Aggregated p-value were plotted for each of the hemodynamic variables into a prioritization matrix (sig. Stoffer W). A sig. stouffer’s w of 1.3 is equivalent to a p-value = 0.05, with higher values being increasingly significant.
Collective data from 1,351 patients was available at baseline and for 1,083 patients at follow-up. 23 hemodynamic variables were considered for this analysis. At baseline, the most significant variables predictive of primary outcomes were stroke volume index, cardiac output/ index and stroke work.(Fig) At follow-up, the most predictive variables were PVR, total pulmonary resistance, PAsat, and cardiac output.
To our knowledge, this is the largest aggregated attempt to analyze hemodynamic parameters in predicting clinical worsening. This analysis will be used to select features for derivation of machine-learned PAH patient risk stratification models.