Pulmonary diffusing capacity and dyspnoea following COVID-19: Insights from multicentre datasets

Area of Science:

• Pulmonary medicine and respiratory research
• Infectious disease epidemiology
• Biomedical data science

Background:

• Pulmonary complications are a persistent challenge for COVID-19 survivors, requiring robust methods for long-term evaluation.
• Accurate assessment of lung function is crucial for understanding and managing post-COVID-19 respiratory sequelae.

Purpose of the Study:

• To present a curated, open-access dataset of pulmonary function measurements in post-COVID-19 patients.
• To facilitate the development of predictive models for pulmonary impairment and validate novel diagnostics.

Main Methods:

• Collected spirometry, lung volumes, and diffusing capacities for nitric oxide (DLNO) and carbon monoxide (DLCO) from 572 patients and 72 controls across eight international centers.
• Filtered data by excluding missing values and low-quality measurements; a subset linked lung function to dyspnea severity (mMRC scale).
• Utilized SPSS (.sav) and .csv formats for data accessibility in the Mendeley Data Cloud repository.

Main Results:

• The dataset includes detailed pulmonary function metrics, including DLNO and DLCO, from a significant cohort of COVID-19 survivors.
• Analysis of a subset demonstrated correlations between lung function measurements and dyspnea severity.
• The data supports the calculation of summed z-scores (DLNO + DLCO) for predictive modeling.

Conclusions:

• This open-access dataset serves as a critical resource for researchers investigating long-term pulmonary effects of COVID-19.
• The data can accelerate the validation of nitric oxide-carbon monoxide diagnostics for assessing post-COVID-19 lung health.
• Facilitates a deeper understanding of respiratory impairment and aids in developing targeted interventions for survivors.