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Web-Based COPD Risk Self-Assessment Identifies a High-Risk Group With HRQoL Resembling Self-Reported COPD:

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A web-based tool effectively identifies individuals at high risk for Chronic Obstructive Pulmonary Disease (COPD) using algorithmic risk stratification. This approach aids in early COPD case finding and detecting undiagnosed cases, showing promise for public health initiatives.

Keywords:
COPDalgorithm-basedbreathlessnesshealth-related quality of liferisk factorsself-assessmentweb based

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Area of Science:

  • Pulmonary Medicine
  • Digital Health
  • Epidemiology

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) poses a significant global health burden.
  • Early case finding and diagnosis are crucial for effective COPD management.
  • Web-based tools offer scalable solutions for health screening and risk stratification.

Purpose of the Study:

  • To evaluate a web-based self-assessment tool for COPD case finding.
  • To assess the utility of algorithm-based COPD risk stratification.
  • To compare breathlessness and health-related quality of life (HRQoL) across risk groups and with self-reported COPD.

Main Methods:

  • A cross-sectional, anonymous web-based survey was conducted.
  • Participants completed a self-assessment tool including demographics, risk factors, breathlessness (mMRC), and respiratory HRQoL (CAT, K-BILD).
  • COPD risk was algorithmically classified as high- or low-risk; comparisons used chi-square or ANCOVA.

Main Results:

  • Out of 8779 respondents, 6569 were classified as high-risk and 1411 as low-risk.
  • The algorithmically identified high-risk group exhibited breathlessness and poorer HRQoL profiles closer to self-reported COPD than the low-risk group.
  • The tool successfully stratified participants, with high-risk individuals recommended to consult healthcare providers.

Conclusions:

  • A web-based self-assessment tool utilizing algorithmic risk stratification is a viable approach for early COPD case finding.
  • This digital health strategy shows potential for identifying undiagnosed COPD cases.
  • Further validation studies are warranted to confirm the tool's efficacy in diverse populations.