Claxton Scott, Porter Paul, Brisbane Joanna, Bear Natasha, Wood Javan, Peltonen Vesa, Della Phillip, Smith Claire, Abeyratne Udantha
Joondalup Health Campus, Joondalup, WA, Australia.
Genesis Care Sleep and Respiratory, Perth, WA, Australia.
NPJ Digit Med. 2021 Jul 2;4(1):107. doi: 10.1038/s41746-021-00472-x.
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.
慢性阻塞性肺疾病急性加重（AECOPD）在基层医疗环境中很常见，但其准确及时的诊断存在问题。利用语音识别技术中使用的类似技术，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。该算法纳入了患者报告的特征（年龄、发热和新发咳嗽）、五次咳嗽的音频数据，并且新手用户也可以使用。我们将该算法的准确性与专家临床评估进行了比较。在已知患有慢性阻塞性肺疾病（COPD）的患者中，该算法在82.6%（95%置信区间：72.9 - 89.9%）的受试者（n = 86）中正确识别出AECOPD的存在。在91.0%（95%置信区间：82.4 - 96.3%）的个体（n = 78）中正确识别出不存在AECOPD。在较轻的AECOPD病例（阳性预测值：79.2%，95%置信区间：68.0 - 87.8%）中保持了诊断一致性，这些病例通常是到基层医疗就诊的人群。该算法可能有助于早期识别AECOPD，并可纳入患者自我管理计划。
