MacCarter, Dean

Cardiopulmonary exercise testing (CPET) using a spectrum of different approaches demonstrates usefulness for objectively assessing patient disease severity in clinical and research settings. Still, an absence of trained specialists and/or improper data interpretation techniques can pose major limitations to the effective use of CPET for the clinical classification of patients. This study aimed to test an automated disease likelihood scoring algorithm system based on cardiopulmonary responses during a simplified step‐test protocol. For patients with heart failure (HF), pulmonary hypertension (PAH), obstructive lung disease (OLD), or restrictive lung disease (RLD), we compared patient scores stratified into one of four “silos” generated from our novel algorithm system against patient evaluations provided by expert clinicians. Patients with HF (n = 12), PAH (n = 9), OLD (n = 16), or RLD (n = 10) performed baseline pulmonary function testing followed by submaximal step‐testing. Breath‐by‐breath measures of ventilation and gas exchange, in addition to oxygen saturation and heart rate were collected continuously throughout testing. The algorithm demonstrated close alignment with patient assessments provided by clinical specialists: HF (r = 0.89, P < 0.01); PAH (r = 0.88, P < 0.01); OLD (r = 0.70, P < 0.01); and RLD (r = 0.88, P < 0.01). Furthermore, the algorithm was capable of differentiating major disease from other disease pathologies. Thus, in a clinically relevant manner, these data suggest this simplified automated disease algorithm scoring system used during step‐testing to identify the likelihood that patients have HF, PAH, OLD, or RLD closely correlates with patient assessments conducted by trained clinicians.

We developed a simplified automated algorithm to interpret noninvasive gas exchange in healthy subjects and patients with heart failure (HF, n = 12), pulmonary arterial hypertension (PAH, n = 11), chronic obstructive lung disease (OLD, n = 16), and restrictive lung disease (RLD, n = 12). They underwent spirometry and thereafter an incremental 3-minute step test where heart rate and SpO 2 respiratory gas exchange were obtained. A custom-developed algorithm for each disease pathology was used to interpret outcomes. Each algorithm for HF, PAH, OLD, and RLD was capable of differentiating disease groups ( P < .05) as well as healthy cohorts (n = 19, P < .05). In addition, this algorithm identified referral pathology and coexisting disease. Our primary finding was that the ranking algorithm worked well to identify the primary referral pathology; however, coexisting disease in many of these pathologies in some cases equally contributed to the cardiorespiratory abnormalities. Automated algorithms will help guide decision making and simplify a traditionally complex and often time-consuming process.

We determined whether a non-invasive gas exchange based estimate of pulmonary vascular (PV) capacitance [PV CAP = stroke volume (SV) x pulmonary arterial pressure (Ppa)] (GX CAP ) tracked the PV response to exercise in heart-failure (HF) patients. Pulmonary wedge pressure (Ppw), Ppa, PV resistance (PVR), and gas exchange were measured simultaneously during cycle exercise in 42 HF patients undergoing right-heart catheterization. During exercise, P ET CO 2 and V E /VCO 2 were related to each other ( r= -0.93, P < 0.01) and similarly related to mean Ppa (mPpa) ( r = -0.39 and 0.36; P < 0.05); P ET CO 2 was subsequently used as a metric of mPpa. Oxygen pulse (O 2 pulse) tracked the SV response to exercise (r = 0.91, P < 0.01). Thus, GX CAP was calculated as O 2 pulse x P ET CO 2 . During exercise, invasively determined PV CAP and non-invasive GX CAP were related (r = 0.86, P < 0.01), and GX CAP correlated with mPpa and PVR (r = -0.46 and -0.54; P < 0.01). In conclusion, noninvasive gas exchange measures may represent a simple way to track the PV response to exercise in HF.

Patients with pulmonary arterial hypertension (PAH) and heart failure (HF) display many abnormalities in respiratory gas exchange. These abnormalities are accentuated with exercise and track with disease severity. However, use of gas exchange measures in day-to-day clinical practice is limited by several issues, including the large number of variables available and difficulty in data interpretation. Moreover, maximal exercise testing has limitations in clinical populations due to their complexity, patient anxiety and variability in protocols and cost. Therefore, a multivariable gas exchange index (MVI) that integrates key gas exchange variables obtained during submaximal exercise into a severity score that ranges from normal to severe-very-severe is proposed. To demonstrate the usefulness of this index, we applied this to 2 groups (PAH,n=42and HF,n=47) as well as to age matched healthy controls (n=25). We demonstrate that this score tracks WHO classification and right ventricular systolic pressure in PAH (r=0.53and 0.73,P≤0.01) and NYHA and cardiac index in HF (r=0.49and 0.74,P≤0.01). This index demonstrates a stronger relationship than any single gas exchange variable alone. In conclusion, MVI obtained from light, submaximal exercise gas exchange is a useful approach to simplify data interpretation in PAH and HF populations.