TY - JOUR
T1 - Algorithm for Predicting Disease Likelihood From a Submaximal Exercise Test
AU - Kim, Chul Ho
AU - Hansen, James E.
AU - MacCarter, Dean J.
AU - Johnson, Bruce D.
N1 - Funding Information:
FunDIng: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by Shape Medical Systems Inc.
Publisher Copyright:
© 2017, © The Author(s) 2017.
PY - 2017/7/12
Y1 - 2017/7/12
N2 - 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 SpO2 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.
AB - 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 SpO2 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.
KW - cardiopulmonary
KW - decision making
KW - disease likelihood
KW - respiratory patterns
UR - http://www.scopus.com/inward/record.url?scp=85044579003&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044579003&partnerID=8YFLogxK
U2 - 10.1177/1179548417719248
DO - 10.1177/1179548417719248
M3 - Article
AN - SCOPUS:85044579003
SN - 1179-5484
VL - 11
JO - Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine
JF - Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine
ER -