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.

Interferon gamma-induced protein 10 (IP-10) for the early prognosis of the risk for severe respiratory failure and death in COVID-19 pneumonia.

OBJECTIVES: Elevated concentrations of soluble urokinase plasminogen activator receptor (suPAR) predict progression to severe respiratory failure (SRF) or death among patients with COVID-19 pneumonia and guide early anakinra treatment. As suPAR testing may not be routinely available in every health-care setting, alternative biomarkers are needed. We investigated the performance of C-reactive protein (CRP), interferon gamma-induced protein-10 (IP-10) and TNF-related apoptosis-inducing ligand (TRAIL) for predicting SRF or death in COVID-19. METHODS: Two cohorts were studied; one discovery cohort with 534 patients from the SAVE-MORE clinical trial; and one validation cohort with 364 patients from the SAVE trial including also 145 comparators. CRP, IP-10 and TRAIL were measured by the MeMed Key® platform in order to select the biomarker with the best prognostic performance for the early prediction of progression into SRF or death. RESULTS: IP-10 had the best prognostic performance: baseline concentrations 2000 pg/ml or higher predicted equally well to suPAR (sensitivity 85.0 %; negative predictive value 96.6 %). Odds ratio for poor outcome among anakinra-treated participants of the SAVE-MORE trial was 0.35 compared to placebo when IP-10 was 2,000 pg/ml or more. IP-10 could divide different strata of severity for SRF/death by day 14 in the validation cohort. Anakinra treatment decreased this risk irrespective the IP-10 concentrations. CONCLUSIONS: IP-10 concentrations of 2,000 pg/ml or higher are a valid alternative to suPAR for the early prediction of progression into SRF or death the first 14 days from hospital admission for COVID-19 and they may guide anakinra treatment. CLINICALTRIALS: gov, NCT04680949 and NCT04357366.

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.

Reduction in Hospitalizations for Respiratory Diseases during the First COVID-19 Wave in Greece.

INTRODUCTION: During the first COVID-19 wave, a considerable decline in hospital admissions was observed worldwide. AIM: This retrospective cohort study aimed to assess if there were any changes in the number of patients hospitalized for respiratory diseases in Greece during the first CO-VID-19 wave. METHODS: In the present study, we evaluated respiratory disease hospitalization rates across 9 tertiary hospitals in Greece during the study period (March-April 2020) and the corresponding period of the 2 previous years (2018-2019) that served as the control periods. Demographic data and discharge diagnosis were documented for every patient. RESULTS: Of the 1,307 patients who were hospitalized during the study period, 444 (35.5%) were males with a mean (±SD) age of 66.1 ± 16.6 years. There was a 47 and 46% reduction in all-cause respiratory morbidity compared to the corresponding periods of 2018 and 2019, respectively. The mean incidence rate for respiratory diseases during the study period was 21.4 admissions per day, and this rate was significantly lower than the rate during the same period in 2018 (40.8 admissions per day; incidence rate ratio [IRR], 0.525; 95% confidence interval [CI], 0.491-0.562; p < 0.001) or the rate during 2019 (39.9 admissions per day; IRR, 0.537; 95% CI, 0.502-0.574; p < 0.001). The greatest reductions (%) in the number of daily admissions in 2020 were observed for sleep apnoea (87% vs. 2018 and 84% vs. 2019) followed by admissions for asthma (76% vs. 2018 and 79% vs. 2019) and chronic obstructive pulmonary disease (60% vs. 2018 and 51% vs. 2019), while the lowest reductions were detected in hospitalizations for pulmonary embolism (6% vs. 2018 and 23% vs. 2019) followed by tuberculosis (25% vs. both 2018 and 2019). DISCUSSION/CONCLUSION: The significant reduction in respiratory admissions in 2020 raises the reasonable question of whether some patients may have avoided seeking medical attention during the COVID-19 pandemic and suggests an urgent need for transformation of healthcare systems during the pandemic to offer appropriate management of respiratory diseases other than COVID-19.