Zavorsky Gerald S, Barisione Giovanni, Gille Thomas, Dal-Negro Roberto W, Núñez-Fernández Marta, Seccombe Leigh, Imeri Gianluca, Marco Fabiano Di, Mortensen Jann, Salvioni Elisabetta, Agostoni Piergiuseppe, Brusasco Vito
Department of Physiology and Membrane Biology, University of California Davis, Sacramento, CA, United States.
Struttura Semplice Fisiopatologia Respiratoria, Clinica Malattie Respiratorie e Allergologia, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
Data Brief. 2025 Jul 25;62:111925. doi: 10.1016/j.dib.2025.111925. eCollection 2025 Oct.
Pulmonary complications remain a significant challenge for COVID-19 survivors, necessitating advanced diagnostic approaches for long-term assessment. We present a curated, open-access dataset of pulmonary function measurements-including nitric oxide (DLNO) and carbon monoxide (DLCO) diffusing capacities-in 572 post-COVID-19 patients and 72 healthy controls (filtered from an original cohort of 726 survivors and 126 controls). Collected across eight international centres, the data include demographics, spirometry, lung volumes, and 5-6 s single-breath DLNO, DLCO, and alveolar volume (VA). Missing values for total lung capacity were imputed, and low-quality or system-specific (Hyp'Air Compact) measurements were excluded in the filtered dataset. A third subset (333 patients, 54 controls) links these measurements to dyspnoea severity (mMRC scale) for correlation and proportional odds analyses. This resource underpins predictive modeling of post-COVID pulmonary impairment via summed z-scores (DLNO + DLCO) and aims to accelerate validation of NO-CO diagnostics. The freely accessible datasets are provided in both SPSS (.sav) and .csv formats at the Mendeley Data Cloud-based repository and includes nominal, ordinal, and scalar data.
肺部并发症仍然是新冠病毒病康复者面临的重大挑战,因此需要先进的诊断方法进行长期评估。我们展示了一个经过整理的、开放获取的数据集,该数据集包含572名新冠病毒病康复患者和72名健康对照者(从最初的726名康复者和126名对照者队列中筛选而来)的肺功能测量数据,包括一氧化氮(DLNO)和一氧化碳(DLCO)弥散能力。这些数据在八个国际中心收集,包括人口统计学信息、肺量计检查、肺容积,以及5 - 6秒单次呼吸的DLNO、DLCO和肺泡容积(VA)。对总肺容量的缺失值进行了插补,并在筛选后的数据集里排除了低质量或特定系统(Hyp'Air Compact)的测量数据。第三个子集(333名患者,54名对照者)将这些测量数据与呼吸困难严重程度(mMRC量表)相关联,用于相关性分析和比例优势分析。该资源通过汇总z分数(DLNO + DLCO)为新冠病毒感染后肺部损伤的预测建模提供支持,并旨在加速一氧化氮 - 一氧化碳诊断方法的验证。可免费获取的数据集以SPSS(.sav)和.csv格式提供在基于Mendeley Data Cloud的存储库中,包括名义数据、有序数据和标量数据。