Porter Paul, Brisbane Joanna, Abeyratne Udantha, Bear Natasha, Wood Javan, Peltonen Vesa, Della Phillip, Smith Claire, Claxton Scott
School of Nursing, Midwifery and Paramedicine, Curtin University, Bentley; Joondalup Health Campus, Joondalup.
Joondalup Health Campus, Joondalup.
Br J Gen Pract. 2021 Mar 26;71(705):e258-e265. doi: 10.3399/BJGP.2020.0750. Print 2021 Apr.
Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiological examinations are not possible, such as during telehealth consultations.
To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiological inputs.
A prospective cohort study using data from participants aged >12 years presenting with acute respiratory symptoms to a hospital in Western Australia.
Five cough audio-segments were recorded and four patient-reported symptoms (fever, acute cough, productive cough, and age) were analysed by the smartphone-based algorithm to generate an immediate diagnostic output for CAP. Independent cohorts were recruited to train and test the accuracy of the algorithm. Diagnostic agreement was calculated against the confirmed discharge diagnosis of CAP by specialist physicians. Specialist radiologists reported medical imaging.
The smartphone-based algorithm had high percentage agreement (PA) with the clinical diagnosis of CAP in the total cohort ( = 322, positive PA [PPA] = 86.2%, negative PA [NPA] = 86.5%, area under the receiver operating characteristic curve [AUC] = 0.95); in participants 22-<65 years ( = 192, PPA = 85.7%, NPA = 87.0%, AUC = 0.94), and in participants aged ≥65 years ( = 86, PPA = 85.7%, NPA = 87.5%, AUC = 0.94). Agreement was preserved across CAP severity: 85.1% ( = 80/94) of participants with CRB-65 scores 1 or 2, and 87.7% ( = 57/65) with a score of 0, were correctly diagnosed by the algorithm.
The algorithm provides rapid and accurate diagnosis of CAP. It offers improved accuracy over current protocols when clinical evaluation is difficult. It provides increased capabilities for primary and acute care, including telehealth services, required during the COVID-19 pandemic.
社区获得性肺炎（CAP）是基层医疗中出现呼吸道症状患者的重要考量因素；然而，在无法进行临床和影像学检查时，如远程医疗会诊期间，准确诊断颇具难度。
开发并测试一种基于智能手机的算法，用于在无需临床检查或影像学输入的情况下诊断CAP。
一项前瞻性队列研究，使用来自西澳大利亚一家医院的12岁以上出现急性呼吸道症状参与者的数据。
记录五段咳嗽音频片段，并通过基于智能手机的算法分析四种患者报告症状（发热、急性咳嗽、咳痰和年龄），以生成CAP的即时诊断结果。招募独立队列来训练和测试该算法的准确性。根据专科医生确诊的CAP出院诊断计算诊断一致性。专科放射科医生报告医学影像结果。
基于智能手机的算法在整个队列中与CAP临床诊断的百分比一致性（PA）较高（n = 322，阳性PA [PPA] = 86.2%，阴性PA [NPA] = 86.5%，受试者工作特征曲线下面积 [AUC] = 0.95）；在22 - <65岁的参与者中（n = 192，PPA = 85.7%，NPA = 87.0%，AUC = 0.94），以及在≥65岁的参与者中（n = 86，PPA = 85.7%，NPA = 87.5%，AUC = 0.94）。跨CAP严重程度的一致性得以保持：CRB - 65评分1或2的参与者中85.1%（n = 80/94），评分为0的参与者中87.7%（n = 57/65），被该算法正确诊断。
该算法可快速准确地诊断CAP。在临床评估困难时，其准确性优于当前方案。它增强了基层医疗和急性护理的能力，包括COVID - 19大流行期间所需的远程医疗服务。
