Algorithm for Predicting Disease Likelihood From a Submaximal Exercise Test

Chul Ho Kim, James E. Hansen, Dean J. MacCarter, Bruce D. Johnson

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
JournalClinical Medicine Insights: Circulatory, Respiratory and Pulmonary Medicine
StatePublished - Jul 12 2017


  • cardiopulmonary
  • decision making
  • disease likelihood
  • respiratory patterns

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Cardiology and Cardiovascular Medicine


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