Close
Help





JOURNAL

Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine

Algorithm for Predicting Disease Likelihood From a Submaximal Exercise Test

Submit a Paper


Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine 2017:11 1179548417719248

Original Research

Published on 13 Jul 2017

DOI: 10.1177/1179548417719248


Further metadata provided in PDF



Sign up for email alerts to receive notifications of new articles published in Clinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine

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 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.



Downloads

PDF  (577.73 KB PDF FORMAT)

RIS citation   (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)

XML   (61.88 KB XML FORMAT)

BibTex citation   (BIBDESK, LATEX)





Quick Links


New article and journal news notification services