• COVID-19
• Computed tomography
• Lung transplant
• Quantitative CT

• Fat embolism syndrome
• case report
• chest computed tomography
• pulmonary imaging

• Humans
• Tomography, X-Ray Computed/adverse effects
• Lung/diagnostic imaging
• Pulmonary Embolism/diagnostic imaging
• Dyspnea/etiology
• Embolism, Fat/diagnostic imaging
• Embolism, Fat/etiology

• Aspergillus
• COVID-19
• Candida
• Cryptococcus
• Fungal infections
• Histoplasma
• Mucormycosis
• Pneumocystis
• SARS-CoV-2

• Artificial intelligence
• Covid
• Deep learning
• Imaging
• Pneumocystis
• SARS-CoV-2

• Artificial Intelligence
• COVID-19
• Deep Learning
• Humans
• Pneumonia, Pneumocystis/diagnostic imaging
• SARS-CoV-2

• COVID-19
• Pneumocystis jiroveci pneumonia
• ground-glass
• radiology
• respiratory medicine

• COVID-19
• chest CT
• ground glass opacities
• viral pneumonia

• COVID-19
• HIV
• diagnostics
• hepcidin
• pneumocystis pneumonia

• Aspergillus
• COVID-19
• Candida
• Pneumocystis
• SARS-CoV-2
• bronchoalveolar lavage

The relationship between computed tomography (CT) and prognosis of patients with COVID-19 pneumonia remains unclear. We hypothesized that the Ichikado CT score, obtained in the first 24 h of hospital admission, is an independent predictor for all-cause mortality during hospitalization in patients with COVID-19 pneumonia.

Single-center retrospective cohort study of patients with confirmed COVID-19 pneumonia admitted at our institution between March 20th, 2020 and October 31st, 2020. Patients were enrolled if, within 24 h of admission, a chest CT scan, an arterial blood gas, a complete blood count, and a basic metabolic panel were performed. Two independent radiologists, who were blinded to clinical data, retrospectively evaluated the chest CT scans following a previously described qualitative and quantitative CT scoring system. The primary outcome was all-cause in-hospital mortality or survival to hospital discharge. Secondary outcomes were new requirements for invasive mechanical ventilation and hospital length of stay. Cox regression models were used to test the association between potential independent predictors and all-cause mortality.

Two hundred thirty-five patients, 197 survivors and 38 nonsurvivors, were studied. The median Ichikado CT score for nonsurvivors was significantly higher than survivors (P < 0.001). An Ichikado CT score of more than 172 enabled prediction of mortality, with a sensitivity of 84.2% and a specificity of 79.7%. Multivariate analysis identified Ichikado CT score (HR, 7.772; 95% CI, 3.164–19.095; P < 0.001), together with age (HR, 1.030; 95% CI, 1.030–1.060; P = 0.043), as independent predictors of all-cause in-hospital mortality.

Ichikado CT score is an independent predictor of both requiring invasive mechanical ventilation and all-cause mortality in patients hospitalized with COVID-19 pneumonia. Further prospective evaluation is necessary to confirm these findings.

Trial registration: The WCG institutional review board approved this retrospective study and patient consent was waived due to its non-interventional nature (Identifier: 20210799).

• COVID-19
• COVID-19 prognostic factor
• CT score
• Coronavirus disease 2019
• Ichikado CT score
• Tomography

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pneumonia. However, COVID-19 is now recognized to involve multiple organ systems with varying severity and duration. In July 2021, the findings from a retrospective population study from the National COVID Cohort Collaborative (N3C) Consortium were published that included analysis by machine learning methods of 174,568 adults with SARS-CoV-2 infection from 34 medical centers in the US. The study stratified patients for COVID-19 according to the World Health Organization (WHO) Clinical Progression Scale (CPS). Severe clinical outcomes were identified as the requirement for invasive ventilatory support, or extracorporeal membrane oxygenation (ECMO), and patient mortality. Machine learning analysis showed that the factor most strongly associated with severity of clinical course in patients with COVID-19 was pH. A separate multivariable logistic regression model showed that independent factors associated with more severe clinical outcomes included age, dementia, male gender, liver disease, and obesity. This Editorial aims to present the rationale and findings of the largest population cohort of adult patients with COVID-19 to date and highlights the importance of using large population studies with sophisticated analytical methods, including machine learning.

• COVID-19
• Diagnostic imaging
• SARS-CoV-2
• Surveys and questionnaires

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. The acute phase of the infection may in certain individuals be followed by another longer phase of disease (long COVID) of unknown etiology probably associated in certain cases with autoimmune activation. It has been shown that COVID-19 can trigger autoantibody production and in genetically predisposed patients may cause the onset or exacerbation of autoimmune diseases. We are reporting a case of mild COVID-19 infection complicated by autoantibody production and cutaneous and gastrointestinal symptoms and subsequently diagnosed with systemic sclerosis (SSc). A 47-year-old man with no history of any autoimmune diseases and in good health became sick together with his family on the 12th of November with mild symptoms: tiredness, fever, cough, and sore throat. Oropharyngeal swab for SARS-CoV-2 tested positive. He was isolated at home and did not require hospitalization. Three weeks later he presented with clinical manifestation compatible with suspicion of SSc. He briefly presented with skin rush, periorbital edema and conjunctivitis, vomiting, dysphagia, burning sensation in the skin, above all in the fingertips and around the mouth, puffy fingers, Raynaud’s phenomenon, pain at the fingertip of the middle finger where a depressed area was noticed without a clear ulceration. ANA showed a strongly positive nucleolar pattern. Anti-PM/Scl 75 and PM/Scl 100 resulted positive. High-resolution computed tomography (HCRT) showed early stage of interstitial lung disease (ILD). The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical and radiological pictures according to the ACR/EULAR classification (scores: puffy finger, 2; ILD, 2; Raynaud’s phenomenon, 3; SSc related antibodies, 3; total 10). There are several cases described in the medical literature of possible new onset of SLE after COVID-19 infection. This is the first case that describes a possible new onset of SSc. Conclusion: SARS-CoV-2 may trigger systemic sclerosis.

• COVID-19
• autoimmunity
• long COVID
• scleroderma
• systemic sclerosis

• COVID-19
• Pandemic
• Pneumonia
• Radiology
• SARS-CoV-2
• Tomography