TY - JOUR
T1 - Streamlining cardiopulmonary exercise testing for use as a screening and tracking tool in primary care
AU - Kim, Chul Ho
AU - Van Iterson, Erik H.
AU - Hansen, James E.
AU - MacCarter, Dean J.
AU - Johnson, Bruce D.
N1 - Publisher Copyright:
© 2018, The Author(s) 2018.
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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.
KW - disease tracking tool
KW - respiratory patterns
KW - submaximal exercise testing
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U2 - 10.1177/2045894018776489
DO - 10.1177/2045894018776489
M3 - Article
AN - SCOPUS:85050036384
SN - 2045-8932
VL - 8
JO - Pulmonary Circulation
JF - Pulmonary Circulation
IS - 2
ER -