Implications of cardiac variability with cardiovascular magnetic resonance imaging for calculating trial sample size in pulmonary arterial hypertension

• Published in: International journal of cardiology 2018-04, Vol.257, p.332-338
• Main Authors: Göransson, Christoffer, Vejlstrup, Niels, Scheike, Thomas, Carlsen, Jørn
• Format: Article
• Language: English
• Subjects: Cardiac variability; Cardiovascular magnetic resonance imaging; Pulmonary arterial hypertension; Trial sample size
• ISSN: 0167-5273; 1874-1754
• DOI: 10.1016/j.ijcard.2017.11.020

Normally, morbidity precedes mortality in pulmonary arterial hypertension (PAH) and is assessed with recognized surrogate measures of survival. Cardiovascular magnetic resonance (CMR) can assess right ventricular (RV) structure and function which is directly related to survival in PAH. This study describes CMR-assessed weekly cardiac variability in PAH, allowing calculation of sample sizes for trials comparing PAH targeted treatment effects and optimal methods for individual monitoring. Ten clinically stable patients with PAH and ten healthy controls had three CMR examinations at weekly intervals. Stroke volume (SV) and cardiac output (CO) measured at six locations with two CMR-methods were, together with the right and left ventricular volumes and systolic function, assessed for variability, which allowed the calculation of sample sizes for clinically relevant changes. Variability (SD/mean) for SV and CO was lower in PAH patients than in control subjects (SV=5.7% vs. 8.9% [p=0.002]; CO=6.1% vs. 10.2% [p=0.003]), allowing a total sample size of 6 patients for a clinically relevant 10mL change in SV or 4 patients for a 10% increase in CO. For the lowest variability, SV is best measured with cine imaging in the left ventricle, and CO is best measured with flow imaging in the aorta. The RV volumes varied more than did the left ventricular volumes. For systolic function, the RV ejection fraction had the lowest variability (9.7%). Low cardiac variability measured with CMR in PAH enables the statistically strong detection of clinically relevant changes with a small trial sample size.