Prediction of restrictive ventilation disorders after COVID-19

Introduction . The effect of SARS-COV-2 on the lung function remains relevant at the present time. Aim . Determination of the most important predictors of the restrictive ventilation disorder after COVID-19. Materials and methods . The retrospective study included 341 patients without underlying lung diseases (median age 48 years) survivors after COVID-19 with bilateral pneumonia. The median of the greatest extent of parenchymal involvement in the acute phase of COVID-19 (CT max ) was 50%. Spirometry, body plethysmography, and diffusion test were performed. Descriptive statistics, correlation analysis, one-dimensional logistic regression analysis with an assessment of odds ratios (OR) and multivariate logistic regression analysis were applied. ROC analysis was used to assess the quality of the binary classifier model. Results . The initial model for predicting reduced total lung capacity (TLC) (criterion 1: TLC < 80% predicted, criterion 2: TLC<predicted-1.645SD) included predictors: CT max , time interval from the COVID-19 onset (TI), gender, age, body mass index. Backward stepwise regression was applied and a binary classifier model that includes CT max and TI was obtained. Applying criterion 1 for reducing TLC, the sensitivity and specificity of the model were 70,5% and 89.3%, respectively, and criterion 2 - 96.6% and 67.3%, respectively. The analysis of OR for the obtained binary classifier models showed that OR>1 is observed at CT max > 70%. Conclusions . The restrictive ventilation disorder after COVID-19 is caused by CT max and TI. The risk of reducing TLC after COVID-19 increases significantly with CT max 70% or more. The criterion of the low level of normal of TLC affects the sensitivity and specificity of the obtained models.