Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

慢性阻塞性肺疾病 (AECOPD) 的急性加重在初级保健机构中很常见，但准确及时的诊断存在问题。使用语音识别技术中采用的技术，我们开发了一种基于智能手机的算法，用于快速准确地诊断 AECOPD。该算法结合了患者报告的特征（年龄、发烧和新咳嗽）、来自五次咳嗽的音频数据，并且可由新手用户部署。我们将算法的准确性与专家临床评估进行了比较。在已知 COPD 患者中，该算法正确识别出 82.6% (95% CI: 72.9–89.9%) 受试者 ( n  = 86)存在 AECOPD 。91.0% (95% CI: 82.4–96.3%) 的个体 ( n = 78）。诊断一致性在较轻的 AECOPD 病例（PPA：79.2%，95% CI：68.0–87.8%）中保持不变，这些病例通常包括就诊于初级保健的队列。该算法可能有助于早期识别 AECOPD 并纳入患者自我管理计划。