使用患者报告症状和咳嗽分析通过智能手机诊断慢性阻塞性气道疾病：诊断准确性研究
Diagnosing Chronic Obstructive Airway Disease on a Smartphone Using Patient-Reported Symptoms and Cough Analysis: Diagnostic Accuracy Study.
作者信息
Porter Paul, Claxton Scott, Brisbane Joanna, Bear Natasha, Wood Javan, Peltonen Vesa, Della Phillip, Purdie Fiona, Smith Claire, Abeyratne Udantha
机构信息
Joondalup Health Campus, Perth, Australia.
School of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Australia.
出版信息
JMIR Form Res. 2020 Nov 10;4(11):e24587. doi: 10.2196/24587.
BACKGROUND
Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities.
OBJECTIVE
The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set.
METHODS
Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available.
RESULTS
The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97.
CONCLUSIONS
The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments.
TRIAL REGISTRATION
Australian New Zealand Clinical Trials Registry ACTRN12618001521213; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939.
背景
在急性护理环境中，慢性阻塞性肺疾病（COPD）的快速准确诊断存在问题，尤其是在存在感染性合并症的情况下。
目的
本研究的目的是开发一种基于智能手机的快速算法，用于在有无急性呼吸道感染的情况下检测COPD，并在独立验证集上评估诊断准确性。
方法
招募年龄在40至75岁之间、有或无呼吸道疾病症状、除COPD、慢性支气管炎或肺气肿外无慢性呼吸道疾病的参与者进入研究。该算法分析了5种咳嗽声音和4种患者报告的临床症状，在不到1分钟内提供诊断。临床诊断由专科医生根据所有可用的病例记录确定，包括可用时的肺活量测定。
结果
在整个队列（N = 252；阳性百分比一致性[PPA]=93.8%，阴性百分比一致性[NPA]=77.0%，曲线下面积[AUC]=0.95）、患有肺炎或COPD感染性加重的参与者（n = 117；PPA = 86.7%，NPA = 80.5%，AUC = 0.93）以及无感染性合并症的参与者（n = 135；PPA = 100.0%，NPA = 74.0%，AUC = 0.97）中，该算法与COPD临床诊断的PPA和NPA均较高。在通过肺活量测定确诊为COPD的参与者（n = 229）中，PPA为100.0%，NPA为77.0%；AUC为0.97。
结论
该算法与临床诊断高度一致，能快速检测出有无其他感染性肺部疾病的参与者中的COPD。该算法可安装在智能手机上，以便在急性护理环境中对COPD进行床边诊断，为治疗方案提供依据，并识别因季节性或其他呼吸道疾病而死亡风险增加的人群。
试验注册
澳大利亚新西兰临床试验注册中心ACTRN12618001521213；http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939。
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