Kim Chul-Ho, Hansen James E, MacCarter Dean J, Johnson Bruce D
Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
Clin Med Insights Circ Respir Pulm Med. 2017 Jul 13;11:1179548417719248. doi: 10.1177/1179548417719248. eCollection 2017.
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 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 ( < .05) as well as healthy cohorts (n = 19, < .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.
我们开发了一种简化的自动化算法，用于解读健康受试者以及患有心力衰竭（HF，n = 12）、肺动脉高压（PAH，n = 11）、慢性阻塞性肺疾病（OLD，n = 16）和限制性肺疾病（RLD，n = 12）患者的无创气体交换情况。他们接受了肺活量测定，之后进行了递增的3分钟阶梯试验，在此过程中获取心率和SpO呼吸气体交换数据。针对每种疾病病理定制开发的算法用于解读结果。用于HF、PAH、OLD和RLD的每种算法都能够区分疾病组（P <.05）以及健康队列（n = 19，P <.05）。此外，该算法还能识别转诊病理和并存疾病。我们的主要发现是，排序算法在识别主要转诊病理方面效果良好；然而，在某些情况下，这些病理中的许多并存疾病同样导致了心肺异常。自动化算法将有助于指导决策，并简化传统上复杂且往往耗时的过程。
