Algorithm for Predicting Disease Likelihood From a Submaximal Exercise Test. Issue 11 (12th July 2017)

• Record Type: Journal Article
• Authors: Kim, Chul-Ho; Hansen, James E; MacCarter, Dean J; Johnson, Bruce D
• Is Part Of: Clinical medicine insights. Issue 11(2017)
• Publication Date: 2017-07-12
• Subjects: cardiopulmonary -- respiratory patterns -- decision making -- disease likelihood; Cardiovascular system -- Diseases -- Periodicals; Respiratory organs -- Diseases -- Periodicals; Blood Circulation; Respiration; Lung Diseases; Cardiovascular system -- Diseases; Respiratory organs -- Diseases; Electronic journals; Periodicals; Periodicals; 616.1005
• DOI: 10.1177/1179548417719248
• Languages: English
• ISSNs: 1179-5484

Abstract

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.