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Active case finding strategy for chronic obstructive pulmonary disease with handheld spirometry.

ABSTRACT: The early detection and diagnosis of chronic obstructive pulmonary disease (COPD) is critical to providing appropriate and timely treatment. We explored a new active case-finding strategy for COPD using handheld spirometry.We recruited subjects over 40 years of age with a smoking history of more than 10 pack-years who visited a primary clinic complaining of respiratory symptoms. A total of 190 of subjects were enrolled. Medical information was obtained from historical records and physical examination by general practitioners. All subjects had their pulmonary function evaluated using handheld spirometry with a COPD-6 device. Because forced expiratory volume in 6?seconds (FEV6) has been suggested as an alternative to FVC, we measured forced expiratory volume in 1?second (FEV1)/FEV6 for diagnosis of airflow limitation. All subjects were then referred to tertiary referral hospitals to complete a "Could it be COPD?" questionnaire, handheld spiromtery, and conventional spirometry. The results of each instrument were compared to evaluate the efficacy of both handheld spirometry and the questionnaire.COPD was newly diagnosed in 45 (23.7%) patients. According to our receiver-operating characteristic (ROC) curve analysis, sensitivity and specificity were maximal when the FEV1/FEV6 ratio was less than 77%. The area under the ROC curve was 0.759. The sensitivity, specificity, positive predictive value, and negative predictive value were 72.7%, 77.1%, 50%, and 90%, respectively. The area under the ROC curve of respiratory symptoms listed on the questionnaire ranged from 0.5 to 0.65, which indicates that there is almost no difference compared with the results of handheld spirometry.The present study demonstrated the efficacy of handheld spirometry as an active case-finding tool for COPD in a primary clinical setting. This study suggested that physicians should recommend handheld spirometry for people over the age of 40, who have a smoking history of more than 10 pack-years, regardless of respiratory symptoms. Furthermore, people who have abnormal results, determined using the FEV1/FEV6 ?0.77 cut-off, should be referred for further conventional spirometry to confirm the diagnosis of COPD. However, further studies within the general population are necessary to establish efficacy in the public.

SUBMITTER: Kim JK

PROVIDER: S-EPMC5268065 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Active case finding strategy for chronic obstructive pulmonary disease with handheld spirometry.

Kim Joo Kyung JK Lee Chang Min CM Park Ji Young JY Kim Joo Hee JH Park Sung-Hoon SH Jang Seung Hun SH Jung Ki-Suck KS Yoo Kwang Ha KH Park Yong Bum YB Rhee Chin Kook CK Kim Deog Kyeom DK Hwang Yong Il YI

Medicine 20161201 50

The early detection and diagnosis of chronic obstructive pulmonary disease (COPD) is critical to providing appropriate and timely treatment. We explored a new active case-finding strategy for COPD using handheld spirometry.We recruited subjects over 40 years of age with a smoking history of more than 10 pack-years who visited a primary clinic complaining of respiratory symptoms. A total of 190 of subjects were enrolled. Medical information was obtained from historical records and physical examin ...[more]

PMID: 27977619

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Project description:BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping).RESULTSAmong 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68-0.71), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).CONCLUSIONSStructural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.TRIAL REGISTRATIONClinicalTrials.gov NCT00608764.FUNDINGThis study was supported by NIH grants K23 HL133438 and R21EB027891 and an American Thoracic Foundation 2018 Unrestricted Research Grant. The COPDGene study is supported by NIH grants NHLBI U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprising AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion.

Dataset Information

Active case finding strategy for chronic obstructive pulmonary disease with handheld spirometry.

Publications

Active case finding strategy for chronic obstructive pulmonary disease with handheld spirometry.

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