Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools

Chiung Zuei Chen; Chau Chyun Sheu; Shih Lung Cheng; Hao Chien Wang; Meng-Chih Lin; Wu Huei Hsu; Kang-Yun Lee; Diahn Warng Perng; Hen I. Lin; Ming Shian Lin; Sheng Hao Lin; Jong Rung Tsai; Chin Chou Wang; Cheng Yi Wang; Tsung-ming Yang; Ching Lung Liu; Tsai Yu Wang; Ching Hsiung Lin

doi:10.2147/COPD.S339340

Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools

Chiung Zuei Chen, Chau Chyun Sheu, Shih Lung Cheng, Hao Chien Wang, Meng-Chih Lin, Wu Huei Hsu, Kang-Yun Lee, Diahn Warng Perng, Hen I. Lin, Ming Shian Lin, Sheng Hao Lin, Jong Rung Tsai, Chin Chou Wang, Cheng Yi Wang, Tsung-ming Yang, Ching Lung Liu, Tsai Yu Wang, Ching Hsiung Lin^*

^*Corresponding author for this work

School of Traditional Chinese Medicine

Research output: Contribution to journal › Journal Article › peer-review

6 Scopus citations

Abstract

Background and Aim: Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed because of the unavailability of spirometers, especially in resource-limited outpatient settings. This study provides real-world evidence to identify optimal approaches for COPD case finding in outpatient settings. Methods: This retrospective study enrolled individuals who were at risk of COPD (age ≥40 years, ≥10 pack-years, and ≥1 respiratory symptom). Eligible participants were examined using various COPD case-finding tools, namely the COPD Population Screener (COPD-PS) questionnaire, a COPD prediction (P_COPD) model, and a microspirometer, Spirobank Smart; subsequently, the participants underwent confirmatory spirometry. The definition and confirmation of COPD were based on conventional spirometry. Receiver operating characteristic curve (ROC), area under the curve (AUC), and decision curve analyses were conducted, and a clinical impact curve was constructed. Results: In total, 385 participants took part in the study [284 without COPD (73.77%) and 101 with COPD (26.23%)]. The microspirometer exhibited a higher AUC value than did the COPD-PS questionnaire and the P_COPD model. The AUC for microspirometry was 0.908 (95% confidence interval [CI] = 0.87–0.95), that for the P_COPD model was 0.788 (95% CI = 0.74–0.84), and that for the COPD-PS questionnaire was 0.726 (95% CI = 0.67–0.78). Decision and clinical impact curve analyses revealed that a microspirometry-derived FEV1/FVC ratio of <74% had superior clinical utility to the other measurement tools. Conclusion: The P_COPD model and COPD-PS questionnaire were useful for identifying symptomatic patients likely to have COPD, but microspirometry was more accurate and had higher clinical utility. This study provides real-world evidence to identify optimal practices for COPD case finding; such practices ensure that physicians have convenient access to up-to-date evidence when they encounter a symptomatic patient likely to have COPD.

Original language	English
Pages (from-to)	3405-3415
Number of pages	11
Journal	International Journal of COPD
Volume	16
DOIs	https://doi.org/10.2147/COPD.S339340
State	Published - 2021

Bibliographical note

Publisher Copyright:
© 2021 Chen et al.

Keywords

COPD
COPD case-finding
COPD-PS questionnaire
P model
microspirometry
spirometry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.2147/COPD.S339340

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Chen, C. Z., Sheu, C. C., Cheng, S. L., Wang, H. C., Lin, M.-C., Hsu, W. H., Lee, K.-Y., Perng, D. W., Lin, H. I., Lin, M. S., Lin, S. H., Tsai, J. R., Wang, C. C., Wang, C. Y., Yang, T., Liu, C. L., Wang, T. Y., & Lin, C. H. (2021). Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools. International Journal of COPD, 16, 3405-3415. https://doi.org/10.2147/COPD.S339340

@article{5d83545df3f24862a48535f863d6f812,

title = "Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools",

abstract = "Background and Aim: Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed because of the unavailability of spirometers, especially in resource-limited outpatient settings. This study provides real-world evidence to identify optimal approaches for COPD case finding in outpatient settings. Methods: This retrospective study enrolled individuals who were at risk of COPD (age ≥40 years, ≥10 pack-years, and ≥1 respiratory symptom). Eligible participants were examined using various COPD case-finding tools, namely the COPD Population Screener (COPD-PS) questionnaire, a COPD prediction (PCOPD) model, and a microspirometer, Spirobank Smart; subsequently, the participants underwent confirmatory spirometry. The definition and confirmation of COPD were based on conventional spirometry. Receiver operating characteristic curve (ROC), area under the curve (AUC), and decision curve analyses were conducted, and a clinical impact curve was constructed. Results: In total, 385 participants took part in the study [284 without COPD (73.77%) and 101 with COPD (26.23%)]. The microspirometer exhibited a higher AUC value than did the COPD-PS questionnaire and the PCOPD model. The AUC for microspirometry was 0.908 (95% confidence interval [CI] = 0.87–0.95), that for the PCOPD model was 0.788 (95% CI = 0.74–0.84), and that for the COPD-PS questionnaire was 0.726 (95% CI = 0.67–0.78). Decision and clinical impact curve analyses revealed that a microspirometry-derived FEV1/FVC ratio of <74% had superior clinical utility to the other measurement tools. Conclusion: The PCOPD model and COPD-PS questionnaire were useful for identifying symptomatic patients likely to have COPD, but microspirometry was more accurate and had higher clinical utility. This study provides real-world evidence to identify optimal practices for COPD case finding; such practices ensure that physicians have convenient access to up-to-date evidence when they encounter a symptomatic patient likely to have COPD.",

keywords = "COPD, COPD case-finding, COPD-PS questionnaire, P model, microspirometry, spirometry",

author = "Chen, {Chiung Zuei} and Sheu, {Chau Chyun} and Cheng, {Shih Lung} and Wang, {Hao Chien} and Meng-Chih Lin and Hsu, {Wu Huei} and Kang-Yun Lee and Perng, {Diahn Warng} and Lin, {Hen I.} and Lin, {Ming Shian} and Lin, {Sheng Hao} and Tsai, {Jong Rung} and Wang, {Chin Chou} and Wang, {Cheng Yi} and Tsung-ming Yang and Liu, {Ching Lung} and Wang, {Tsai Yu} and Lin, {Ching Hsiung}",

note = "Publisher Copyright: {\textcopyright} 2021 Chen et al.",

year = "2021",

doi = "10.2147/COPD.S339340",

language = "英语",

volume = "16",

pages = "3405--3415",

journal = "International Journal of COPD",

issn = "1176-9106",

publisher = "Dove Medical Press Ltd.",

}

Chen, CZ, Sheu, CC, Cheng, SL, Wang, HC, Lin, M-C, Hsu, WH, Lee, K-Y, Perng, DW, Lin, HI, Lin, MS, Lin, SH, Tsai, JR, Wang, CC, Wang, CY, Yang, T, Liu, CL, Wang, TY & Lin, CH 2021, 'Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools', International Journal of COPD, vol. 16, pp. 3405-3415. https://doi.org/10.2147/COPD.S339340

TY - JOUR

T1 - Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools

AU - Chen, Chiung Zuei

AU - Sheu, Chau Chyun

AU - Cheng, Shih Lung

AU - Wang, Hao Chien

AU - Lin, Meng-Chih

AU - Hsu, Wu Huei

AU - Lee, Kang-Yun

AU - Perng, Diahn Warng

AU - Lin, Hen I.

AU - Lin, Ming Shian

AU - Lin, Sheng Hao

AU - Tsai, Jong Rung

AU - Wang, Chin Chou

AU - Wang, Cheng Yi

AU - Yang, Tsung-ming

AU - Liu, Ching Lung

AU - Wang, Tsai Yu

AU - Lin, Ching Hsiung

PY - 2021

Y1 - 2021

N2 - Background and Aim: Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed because of the unavailability of spirometers, especially in resource-limited outpatient settings. This study provides real-world evidence to identify optimal approaches for COPD case finding in outpatient settings. Methods: This retrospective study enrolled individuals who were at risk of COPD (age ≥40 years, ≥10 pack-years, and ≥1 respiratory symptom). Eligible participants were examined using various COPD case-finding tools, namely the COPD Population Screener (COPD-PS) questionnaire, a COPD prediction (PCOPD) model, and a microspirometer, Spirobank Smart; subsequently, the participants underwent confirmatory spirometry. The definition and confirmation of COPD were based on conventional spirometry. Receiver operating characteristic curve (ROC), area under the curve (AUC), and decision curve analyses were conducted, and a clinical impact curve was constructed. Results: In total, 385 participants took part in the study [284 without COPD (73.77%) and 101 with COPD (26.23%)]. The microspirometer exhibited a higher AUC value than did the COPD-PS questionnaire and the PCOPD model. The AUC for microspirometry was 0.908 (95% confidence interval [CI] = 0.87–0.95), that for the PCOPD model was 0.788 (95% CI = 0.74–0.84), and that for the COPD-PS questionnaire was 0.726 (95% CI = 0.67–0.78). Decision and clinical impact curve analyses revealed that a microspirometry-derived FEV1/FVC ratio of <74% had superior clinical utility to the other measurement tools. Conclusion: The PCOPD model and COPD-PS questionnaire were useful for identifying symptomatic patients likely to have COPD, but microspirometry was more accurate and had higher clinical utility. This study provides real-world evidence to identify optimal practices for COPD case finding; such practices ensure that physicians have convenient access to up-to-date evidence when they encounter a symptomatic patient likely to have COPD.

AB - Background and Aim: Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed because of the unavailability of spirometers, especially in resource-limited outpatient settings. This study provides real-world evidence to identify optimal approaches for COPD case finding in outpatient settings. Methods: This retrospective study enrolled individuals who were at risk of COPD (age ≥40 years, ≥10 pack-years, and ≥1 respiratory symptom). Eligible participants were examined using various COPD case-finding tools, namely the COPD Population Screener (COPD-PS) questionnaire, a COPD prediction (PCOPD) model, and a microspirometer, Spirobank Smart; subsequently, the participants underwent confirmatory spirometry. The definition and confirmation of COPD were based on conventional spirometry. Receiver operating characteristic curve (ROC), area under the curve (AUC), and decision curve analyses were conducted, and a clinical impact curve was constructed. Results: In total, 385 participants took part in the study [284 without COPD (73.77%) and 101 with COPD (26.23%)]. The microspirometer exhibited a higher AUC value than did the COPD-PS questionnaire and the PCOPD model. The AUC for microspirometry was 0.908 (95% confidence interval [CI] = 0.87–0.95), that for the PCOPD model was 0.788 (95% CI = 0.74–0.84), and that for the COPD-PS questionnaire was 0.726 (95% CI = 0.67–0.78). Decision and clinical impact curve analyses revealed that a microspirometry-derived FEV1/FVC ratio of <74% had superior clinical utility to the other measurement tools. Conclusion: The PCOPD model and COPD-PS questionnaire were useful for identifying symptomatic patients likely to have COPD, but microspirometry was more accurate and had higher clinical utility. This study provides real-world evidence to identify optimal practices for COPD case finding; such practices ensure that physicians have convenient access to up-to-date evidence when they encounter a symptomatic patient likely to have COPD.

KW - COPD

KW - COPD case-finding

KW - COPD-PS questionnaire

KW - P model

KW - microspirometry

KW - spirometry

UR - http://www.scopus.com/inward/record.url?scp=85122908184&partnerID=8YFLogxK

U2 - 10.2147/COPD.S339340

DO - 10.2147/COPD.S339340

M3 - 文章

C2 - 34955636

AN - SCOPUS:85122908184

SN - 1176-9106

VL - 16

SP - 3405

EP - 3415

JO - International Journal of COPD

JF - International Journal of COPD

ER -

Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools

Abstract

Bibliographical note

Keywords

UN SDGs

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