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Purpose To determine whether Macklin effect (a linear collection of air contiguous to the bronchovascular sheath) on baseline CT imaging is an accurate predictor for subsequent pneumomediastinum (PMD)/pneumothorax (PNX) development in invasively ventilated patients with COVID-19-related acute respiratory distress syndrome (ARDS). Materials and methods This is an observational, case-control study. From a prospectively acquired database, all consecutive invasively ventilated COVID-19 ARDS patients who underwent at least one baseline chest CT scan during the study time period (February 25th, 2020–December 31st, 2020) were identified; those who had tracheal lesion or already had PMD/PNX at the time of the first available chest imaging were excluded. Results 37/173 (21.4%) patients enrolled had PMD/PNX; specifically, 20 (11.5%) had PMD, 10 (5.8%) PNX, 7 (4%) both. 33/37 patients with subsequent PMD/PNX had Macklin effect on baseline CT (89.2%, true positive s) 8.5 days [range, 1–18] before the first actual radiological evidence of PMD/PNX. Conversely, 6/136 patients without PMD/PNX (4.4%, false positive s) demonstrated Macklin effect ( p < 0.001). Macklin effect yielded a sensitivity of 89.2% (95% confidence interval [CI]: 74.6–96.9), a specificity of 95.6% (95% CI: 90.6–98.4), a positive predictive value (PV) of 84.5% (95% CI: 71.3–92.3), a negative PV of 97.1% (95% CI: 74.6–96.9) and an accuracy of 94.2% (95% CI: 89.6–97.2) in predicting PMD/PNX (AUC:0.924). Conclusions Macklin effect accurately predicts, 8.5 days in advance, PMD/PNX development in COVID-19 ARDS patients.

Introduction While the beneficial effect of vaccination, restrictive measures, and social distancing in reducing mortality due to SARS-CoV-2 is intuitive and taken for granted, seasonality (predictable fluctuation or pattern that recurs or repeats over a one-year period) is still poorly understood and insufficiently taken into consideration. We aimed to examine SARS-CoV-2 seasonality in countries with temperate climate. Methods We identified countries with temperate climate and extracted average country temperature data from the National Center for Environmental information and from the Climate Change Knowledge Portal. We obtained mortality and vaccination rates from an open access database. We used the stringency index derived from the Oxford COVID-19 Government Response Tracker to quantify restriction policies. We used Spearman's and rank-correlation non-parametric test coefficients to investigate the association between COVID-19 mortality and temperature values. We employed multivariate regression models to analyze how containment measures, vaccinations, and monthly temperatures affected COVID-19 mortality rates. Results The time series for daily deaths per million inhabitants and average monthly temperatures of European countries with a temperate climate had a negative correlation (p < 0.0001 for all countries, 0.40 < R < 0.86). When running multivariate regression models with country fixed effects, we noted that mortality rates were significantly lower when temperature were higher. Interestingly, when adding an interaction term between monthly temperatures and vaccination rates, we found that as monthly temperatures dropped, the effect of the vaccination campaign on mortality was larger than at higher temperatures. Discussion Deaths attributed to SARS-CoV-2 decreased during the summer period in temperate countries. We found that the effect of vaccination rates on mortality was stronger when temperatures were lower. Stakeholders should consider seasonality in managing SARS-CoV-2 and future pandemics to minimize mortality, limit the pressure on hospitals and intensive care units while maintaining economic and social activities.