Corrigendum: Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model.

[This corrects the article DOI: 10.3389/fmed.2022.1083264.].

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model.

Introduction: Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD). Methods: In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1. Results: Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5-29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic. Conclusion: Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature." Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model

Introduction Post-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD). Methods In this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1. Results Two hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5–29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic. Conclusion Post-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are "immature.” Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches.

Corrigendum: ACTIVATE-2: A double-blind randomized trial of BCG vaccination against COVID-19 in individuals at risk.

[This corrects the article DOI: 10.3389/fimmu.2022.873067.].

ACTIVATE-2: A Double-Blind Randomized Trial of BCG Vaccination Against COVID-19 in Individuals at Risk.

In a recent study of our group with the acronym ACTIVATE, Bacillus Calmete-Guérin (BCG) vaccination reduced the occurrence of new infections compared to placebo vaccination in the elderly. Most benefit was found for respiratory infections. The ACTIVATE-2 study was launched to assess the efficacy of BCG vaccination against coronavirus disease 2019 (COVID-19). In this multicenter, double-blind trial, 301 volunteers aged 50 years or older were randomized (1:1) to be vaccinated with BCG or placebo. The trial end points were the incidence of COVID-19 and the presence of anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) antibodies, which were both evaluated through 6 months after study intervention. Results revealed 68% relative reduction of the risk to develop COVID-19, using clinical criteria or/and laboratory diagnosis, in the group of BCG vaccine recipients compared with placebo-vaccinated controls, during a 6-month follow-up (OR 0.32, 95% CI 0.13-0.79). In total, eight patients were in need of hospitalization for COVID-19: six in the placebo group and two in the BCG group. Three months after study intervention, positive anti-SARS-CoV-2 antibodies were noted in 1.3% of volunteers in the placebo group and in 4.7% of participants in BCG-vaccinated group. These data indicate that BCG vaccination confers some protection against possible COVID-19 among patients older than 50 years with comorbidities. BCG vaccination may be a promising approach against the COVID-19 pandemic.

Predictors of Mortality in Tocilizumab-Treated Severe COVID-19.

PURPOSE: Tocilizumab is associated with positive outcomes in severe COVID-19. We wanted to describe the characteristics of nonresponders to treatment. METHODS: This was a retrospective multicenter study in two respiratory departments investigating adverse outcomes at 90 days from diagnosis in subjects treated with tocilizumab (8 mg/kg intravenously single dose) for severe progressive COVID-19. RESULTS: Of 121 subjects, 62% were males, and 9% were fully vaccinated. Ninety-six (79.4%) survived, and 25 died (20.6%). Compared to survivors (S), nonsurvivors (NS) were older (median 57 versus 75 years of age), had more comorbidities (Charlson comorbidity index 2 versus 5) and had higher rates of intubation/mechanical ventilation (p < 0.05). On admission, NS had a lower PO2/FiO2 ratio, higher blood ferritin, and higher troponin, and on clinical progression (day of tocilizumab treatment), NS had a lower PO2/FiO2 ratio, decreased lymphocytes, increased neutrophil to lymphocyte ratio, increased ferritin and lactate dehydrogenase (LDH), disease located centrally on computed tomography scan, and increased late c-reactive protein. Cox proportional hazards regression analysis identified age and LDH on deterioration as predictors of death; admission PO2/FiO2 ratio and LDH as predictors of intubation; PO2/FiO2 ratios, LDH, and central lung disease on radiology as predictors of noninvasive ventilation (NIV) (a < 0.05). The log-rank test of mortality yielded the same results (p < 0.001). ROC analysis of the above predictors in a separate validation cohort yielded significant results. CONCLUSIONS: Older age and high serum LDH levels are predictors of mortality in tocilizumab-treated severe COVID-19 patients. Hypoxia levels, LDH, and central pulmonary involvement radiologically are associated with intubation and NIV.