The long term effects of COVID-19 on Pulmonary diffusing capacity for nitric oxide (DLNO) and carbon monoxide (DLCO)
Description
Hypothesis: This study hypothesized that combining lung diffusing capacities for nitric oxide (DLNO) and carbon monoxide (DLCO) enhances detection of COVID-19-related lung pathologies over using DLCO-alone, or measures of spirometry and total lung capacity (TLC) or single measures alone. A combined DLNO+DLCO z-score was expected to better classify post-COVID lung function abnormalities, capturing a broader spectrum of pulmonary impairment. Data Overview: The dataset includes 256 COVID-19 survivors & 76 controls (332 total) who completed all pulmonary function tests (PFT) across six centers in Italy, France, Spain, and Australia. About 97% of data from these centers is published in 6 peer-reviewed journals & 2 conference abstracts. Controls and COVID-19 cases were tested with the same equipment and protocols. The complete battery of PFTs included DLNO and DLCO (average breath-hold 5.4 s), spirometry, and total lung capacity (TLC), with z-scores calculated from reference equations. COVID-19 survivors were tested between 17 and 575 days post-infection (median 143 days). An additional 190 subjects completed all PFT measurements except TLC. Mixed model binary logistic regression was used with “Study” as a random intercept, COVID-19 (1) vs. control (0) as the dependent variable, and z-scores for spirometry, TLC, and diffusion capacity as independent variables. Matthews correlation coefficient (MCC) assessed model classification with a threshold of 0.5. Notable Findings: Among 256 subjects with complete PFTs post-COVID-19, aged 18 to 89 (median 60 years), 56 (22%) of survivors had restrictive lung issues (TLC < lower limit of normal (LLN), defined by z-score < -1.645). Additionally, 57% showed impairment (airway obstruction, restriction, mixed disorder, or DLNO or DLCO < LLN). Of 34 models evaluated, the model with the lowest Bayesian Information Criterion (BIC) was a combined DLNO+DLCO z-score model, demonstrating superior COVID-19 detection. Five of the top nine models were combined DLNO+DLCO z-score models, while two were DLNO-only and two were DLCO-only z-score models. MCC values indicated 6 of the top 9 models were either combined DLNO+DLCO z-scores or DLNO-only models for highest classification accuracy, with MCCs of approximately 0.50 to 0.53. Furthermore. Dyspnea severity correlated with combined z-scores, DLNO5s-only and DLCO5s-only z-scores (p < 0.001). Data Interpretation and Implications: This study highlights that DLNO, combined with DLCO, provides insights into COVID-19-altered lung diffusion, especially within the alveolar-capillary membrane. Combined z-scores enhance post-COVID abnormality classification, supporting tailored follow-up for symptomatic survivors. Adoption of the DLNO-DLCO approach would require further regulatory approval and standardization. This combined technique is suggested as a promising tool for detecting subclinical COVID-19-related lung impairments, enhancing sensitivity for long-term lung monitoring.
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This diagnostic/prognostic study is a retrospective secondary analysis of data from eight studies on White individuals recovered from COVID-19 and individuals without previous COVID-19. Data from six centers (Italy: Genova, Verona, Bergamo; Spain: Vigo; France: Bobigny; Australia: Concord, NSW) were collected from Feb 2020 to Dec 2022 [1-8]. Local ethical standards allowed anonymized data use, eliminating the need for IRB approval for this analysis. The pooled dataset underwent rigorous screening and cleaning, with eligibility restricted to White subjects aged 18+. The data cleaning process excluded any entries exhibiting specific quality control issues. These issues included cases where (A) alveolar volume (VA) exceeded total lung capacity (TLC) by 0.25 liters or more, (B) forced expiratory volume in 1 second (FEV1) was greater than forced vital capacity, (C) forced vital capacity (FVC) surpassed TLC, (D) breath-hold time was > 12 seconds, or (E) control subjects displayed z-scores more negative than -2.99. This meticulous approach to data screening ensured accurate, reliable data contributed to the analysis, enhancing the validity of the results. Z-scores were generated for pulmonary diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO), VA, TLC, FEV1, FVC, the ratio of FEV1/FVC, and the ratio of DLNO z-scores to DLCO z-scores. Z-scores were obtained by using GLI reference equations for spirometry, TLC, and DLCO for White individuals [9-11], alongside double-diffusion reference equations for White individuals [12-16]. A mixed-effects logistic regression with “Study” as a random effect in R predicted disease; DLNO and other variables were treated as fixed. MCC classified disease. The files are saved in SPSS (.sav) format as well as .csv format. The "Variable_Labels_&_Values1.csv" file goes with the "Covid19_&_Controls.sav or .csv files". The Variable_Labels_&_values2.csv goes with Dyspnea_vs_Diffusing_capacity.sav or .csv files. References 1. Barisione G & Brusasco V. Physiol Rep 2021: 9(4): e14748. 2. Barisione G. & Brusasco V. ERJ Open Res 2023: 9(2). 3. Dal Negro RW, et al., Multidiscip Respir Med 2022: 17: 875. 4. Imeri G, et al. Multidiscip Respir Med 2024: J Clin Med 2021: 10(10). 5. Nunez-Fernandez M, et al. J Clin Med 2021; 10. 6. Seccombe LM, et al. Physiol Rep 2023: 11(7): e15660. 7. Sesé L, et al. European Respiratory Journal 2022: 60(suppl 66): 2662. 8. Sesé L, et al. [FRENCH]. Revue des Maladies Respiratoires Actualités 2022: 14(1): 138-139. 9. Hall GL, et al. Eur Respir J 2021: 57(3). 10. Quanjer PH, et al. Eur Respir J 2012: 40(6): 1324-1343. 11. Stanojevic S, et al. Eur Respir J 2017: 50(3). 12. Aguilaniu B, et al. Eur Respir J 2008: 31(5): 1091-1097. 13. Munkholm M, et al. Eur Respir J 2018: 52(1): 1500677. 14. Zavorsky GS & Cao J. BMJ Open Respir Res 2022: 9(1). 15. Zavorsky GS, et al. Nitric Oxide 2008: 18(1): 70-79. 16. Zavorsky GS et al. Eur Respir J 2017: 49(2): 1600962.