Algorithm for Predicting Disease Likelihood From a Submaximal Exercise Test

Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine

Original Research

Chul-Ho Kim¹  Bruce D Johnson²  Dean J MacCarter²  James E Hansen² 

[1] Chul-Ho Kim, Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN 55901, USA. Email: ;Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA;

关键词: cardiopulmonary;    respiratory patterns;    decision making;    disease likelihood;   

DOI  :  10.1177/1179548417719248

 received in 2017-02-15, accepted in 2017-06-04,  发布年份 2017

来源: Sage Journals

【 摘 要 】

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.

【 授权许可】

CC BY-NC   
© The Author(s) 2017

【 图 表 】

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