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Revisiting a Prognosticating Algorithm from Cardiopulmonary Exercise Testing in Chronic Heart Failure (from the MECKI
Ugo Corrà1, Massimo Francesco Piepoli2, Andrea Giordano3
1Division of Rehabilitation Cardiology, Istituti Clinici Scientifici, IRCCS, Istituto di Veruno, Gattico-Veruno, Italy.
Abstract:
Cardiopulmonary exercise testing is a prognostic tool in heart failure with reduced left ventricular ejection fraction (HFrEF). Prognosticating algorithms have been proposed, but none has been validated. In 2017, a predictive algorithm, based on peak oxygen consumption (VO2), ventilatory response to exercise (ventilation [VE] carbon dioxide production [VCO2], the VE/VCO2 slope), exertional oscillatory ventilation (EOV), and peak respiratory exchange ratio, was recommended, according treatment with β blockers: patients with HFrEF registered in the metabolic exercise test data combined with cardiac and kidney indexes (MECKIs) database were used to validated this algorithm. According to the inclusion/exclusion criteria, 4,683 MECKI patients with HFrEF were enrolled. At 3 years follow-up, the end point was cardiovascular death and urgent heart transplantation (cardiovascular events [CV]). CV events occurred in 25% in patients without β blockers, whereas those with β-blockers had 11% (p <0.0001). In patients without β blockers, 36%, 24%, and 7% CV events were observed in those with peak VO2 ≤10, with peak VO2 >10 <18, and with peak VO2 ≥18 ml/kg/min (p = 0.0001), respectively; in MECKI patients with peak VO2 ≤10 and patients with intermediate exercise capacity, a peak respiratory exchange ratio (≥1.15) and VE/VCO2 slope (≥35) were diriment, respectively (p = 0.0001). EOV, when occurred, increased risk. In MECKI patients on β blockers, 29%, 17%, and 8% CV events were noticed in those with a peak VO2 ≤8, with peak VO2 = 8 to 12, and patients with peak VO2 ≥12 ml/kg/min, respectively (p = 0.0000); when EOV was monitored an increment of risk was witnessed. In conclusion, the outcome of this algorithm was confirmed with the MECKI cohort.
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