CAPTURE Screening Tool Shows Low Sensitivity in Identifying Undiagnosed COPD


The new analysis on the screening tool in primary care patients with COPD highlights the need for further research to optimize CAPTURE's performance and impact on clinical outcomes.

CAPTURE Screening Tool Shows Low Sensitivity in Identifying Undiagnosed COPD in US Primary Care Patients

Fernando Martinez, MD, MS

Results from a new analysis found the COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE) screening tool did not perform well in identifying undiagnosed, clinically significant chronic obstructive pulmonary disease (COPD) among primary care patients in the US.1

The tool demonstrated a high specificity, however, its low sensitivity suggested that it missed many cases of clinically significant COPD. Further research is needed to optimize the performance of the screening tool and to understand whether its use affects clinical outcomes, according to the study.

CAPTURE was developed to mitigate the burden of undiagnosed COPD, which often presents in the primary care setting, by identifying the condition in patients who haven’t previously been diagnosed. A team of investigators led by Fernando J. Martinez, MD, MS, Weill Cornell Medicine/NY Presbyterian Hospital, performed thecross-sectional study to assess the operating characteristics of the CAPTURE screening tool among primary care patients.

CAPTURE Tool Detects COPD in 2.5% of Undiagnosed Patients

Among the 4325 patients who had adequate data for analysis, 110 patients (2.5%) had undiagnosed, clinically significant COPD. According to the data, the CAPTURE tool demonstrated a sensitivity of 48.2% and a specificity of 88.6% for detecting COPD.

Clinically significant COPD was defined as spirometry-defined COPD combined with either an FEV1 < 60% of the predicted value, or a self-reported history of an acute respiratory illness within the past 12 months.

As reported in the results, the area under the receiver operating curve for varying positive screening thresholds was 0.81 (95% CI, 0.77-0.85). A positive screening result was defined as a CAPTURE questionnaire score of 5 or 6, or a questionnaire score of 2, 3, or 4 together with a peak expiratory flow rate of < 250 L/min for women, or < 350 L/min for men.

Despite the high prevalence of COPD worldwide, many individuals with the disease are undiagnosed, which limits their access to appropriate care and treatment. Investigators noted that with early diagnosis and treatment of COPD, patient outcomes, quality of life, symptom control, and survival can improve.

Optimizing Screening for COPD in Primary Care

The CAPTURE screening tool was developed to address the problem of underdiagnosis of COPD in primary care settings, where the majority of patients seek care. The tool uses a combination of five questions and selective use of peak expiratory flow rate to identify patients with undiagnosed, clinically significant COPD.

A total of 4679 patients between the ages of 45-80 years who did not have a prior COPD diagnosis, were enrolled by collecting patient information from 7 primary care practice-based research networks in the US. Enrollment took place from October 2018 to April 2022.

In addition to the CAPTURE questionnaire responses, investigators collected data on peak expiratory flow rate, COPD Assessment Test scores, history of acute respiratory illnesses, demographics, and spirometry.

“Within this US primary care population, the CAPTURE screening tool had a low sensitivity but a high specificity for identifying clinically significant COPD defined by presence of airflow obstruction that is of moderate severity or accompanied by a history of acute respiratory illness,” the team wrote. “Further research is needed to optimize performance of the screening tool and to understand whether its use affects clinical outcomes.”


  1. Martinez FJ, Han MK, Lopez C, et al. Discriminative Accuracy of the CAPTURE Tool for Identifying Chronic Obstructive Pulmonary Disease in US Primary Care Settings. JAMA. 2023;329(6):490–501. doi:10.1001/jama.2023.0128
Related Videos
Video 2 - "Differentiating Medication Non-Adherence From Underlying Comorbidities"
Video 1 - "Defining Resistant Diabetes"
Stephanie Nahas, MD, MSEd | Credit: Jefferson Health
Kelley Branch, MD, MS | Credit: University of Washington Medicine
Alayne Markland, DO | Credit:
© 2024 MJH Life Sciences

All rights reserved.