• COVID-19
• Cytokine
• Inflammation
• Interstitial lung disease
• SARS-CoV-2

• COVID-2020-12371808 Ministero della Salute

• COVID-19
• Coronavirus
• Nomogram
• Prediction
• Survival

• COVID-19
• SARS-CoV-2
• computed tomography
• pneumonia
• vaccination

• COVID-19
• body composition
• follow-up
• skeletal muscle
• visceral fat

• Aged
• Body Composition/physiology
• C-Reactive Protein/metabolism
• COVID-19
• Female
• Humans
• Intra-Abdominal Fat/metabolism
• Male
• Middle Aged
• Retrospective Studies
• Tomography, X-Ray Computed

• COVID-19
• artificial intelligence
• deployment
• implementation

• Artificial Intelligence
• COVID-19
• Humans
• Pandemics
• Radiography
• Radiology

COVID-19 prognostic factors include age, sex, comorbidities, laboratory and imaging findings, and time from symptom onset to seeking care.

The study aim was to evaluate indices combining disease severity measures and time from disease onset to predict mortality of COVID-19 patients admitted to the emergency department (ED).

All consecutive COVID-19 patients who underwent both computed tomography (CT) and chest X-ray (CXR) at ED presentation between 27/02/2020 and 13/03/2020 were included. CT visual score of disease extension and CXR Radiographic Assessment of Lung Edema (RALE) score were collected. The CT- and CXR-based scores, C-reactive protein (CRP), and oxygen saturation levels (sO₂) were separately combined with time from symptom onset to ED presentation to obtain severity/time indices. Multivariable regression age- and sex-adjusted models without and with severity/time indices were compared. For CXR-RALE, the models were tested in a validation cohort.

Of the 308 included patients, 55 (17.9%) died. In multivariable logistic age- and sex-adjusted models for death at 30 days, severity/time indices showed good discrimination ability, higher for imaging than for laboratory measures (AUC_CT = 0.92, AUC_CXR = 0.90, AUC_CRP = 0.88, AUC_sO2 = 0.88). AUC_CXR was lower in the validation cohort (0.79). The models including severity/time indices performed slightly better than models including measures of disease severity not combined with time and those including the Charlson Comorbidity Index, except for CRP-based models.

Time from symptom onset to ED admission is a strong prognostic factor and provides added value to the interpretation of imaging and laboratory findings at ED presentation.

• COVID-19/diagnostic imaging
• Cohort Studies
• Humans
• Prognosis
• Radiography, Thoracic
• Respiratory Sounds
• Retrospective Studies
• SARS-CoV-2
• Severity of Illness Index

The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest computed tomography (CT) characteristics had any prognostic value in patients with COVID-19.

A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality.

A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥50% (Hazard Ratio 2.25 [95% CI: 1.378-3.671], p = 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥50% lung involvement on a CT-scan.

Chest CT findings such as the percentage of pulmonary involvement ≥50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.

• COVID-19
• Chest CT
• Mortality
• Pneumonia
• Teleradiology

• COVID-19
• RT-PCR
• SARS-CoV-2
• computed tomography
• imaging diagnostic test
• meta-analysis
• rapid antigen diagnostic test
• systematic review
• ultrasonography

• No.2020R1F1A1068423 National Research Foundation of Korea
• No. 20015086 Bio Industry Technology Development Program

• humans
• covid-19
• covid-19/diagnostic imaging
• sars-cov-2
• sensitivity and specificity
• tomography, x-ray computed
• ultrasonography

• COVID-19/diagnostic imaging
• Humans
• SARS-CoV-2
• Sensitivity and Specificity
• Tomography, X-Ray Computed
• Ultrasonography

Inflammatory burden is associated with COVID-19 severity and outcomes. Residual computed tomography (CT) lung abnormalities have been reported after COVID-19. The aim was to evaluate the association between inflammatory burden during COVID-19 and residual lung CT abnormalities collected on follow-up CT scans performed 2–3 and 6–7 months after COVID-19, in severe COVID-19 pneumonia survivors. C-reactive protein (CRP) curves describing inflammatory burden during the clinical course were built, and CRP peaks, velocities of increase, and integrals were calculated. Other putative determinants were age, sex, mechanical ventilation, lowest PaO2/FiO2 ratio, D-dimer peak, and length of hospital stay (LOS). Of the 259 included patients (median age 65 years; 30.5% females), 202 (78%) and 100 (38.6%) had residual, predominantly non-fibrotic, abnormalities at 2–3 and 6–7 months, respectively. In age- and sex-adjusted models, best CRP predictors for residual abnormalities were CRP peak (odds ratio [OR] for one standard deviation [SD] increase = 1.79; 95% confidence interval [CI] = 1.23–2.62) at 2–3 months and CRP integral (OR for one SD increase = 2.24; 95%CI = 1.53–3.28) at 6–7 months. Hence, inflammation is associated with short- and medium-term lung damage in COVID-19. Other severity measures, including mechanical ventilation and LOS, but not D-dimer, were mediators of the relationship between CRP and residual abnormalities.

• covid-19
• thorax
• guideline
• consensus
• radiology

• Korean Society of Radiology

• RT-PCR
• SARS-CoV-2
• evidence
• false negative

• CO-RADS
• COVID-19
• SARS-CoV-2
• diagnostic imaging
• pneumonia
• tomography

• Adult
• COVID-19/diagnostic imaging
• Cough
• Dyspnea
• Humans
• SARS-CoV-2
• Tomography, X-Ray Computed/methods

To estimate the effect of tocilizumab or glucocorticoids in preventing death and intubation in patients hospitalized with SARS-CoV-2 pneumonia.

This was a retrospective cohort study enrolling all consecutive patients hospitalized at Reggio Emilia AUSL between February the 11^th and April 14^th 2020 for severe COVID-19 and treated with tocilizumab or glucocorticoids (at least 80 mg/day of methylprednisolone or equivalent for at least 3 days).

The primary outcome was death within 30 days from the start of the considered therapies. The secondary outcome was a composite outcome of death and/or intubation. All patients have been followed-up until May 19^th 2020, with a follow-up of at least 30 days for every patient.

To reduce confounding due to potential non-comparability of the two groups, those receiving tocilizumab and those receiving glucocorticoids, a propensity score was calculated as the inverse probability weighting of receiving treatment conditional on the baseline covariates.

Therapy with tocilizumab alone was associated with a reduction of deaths (OR 0.49, 95% CI 0.21-1.17) and of the composite outcome death/intubation (OR 0.35, 95% CI 0.13-0.90) compared to glucocorticoids alone. Nevertheless, this result should be cautiously interpreted due to a potential prescription bias.

• COVID-19
• Corticosteroids
• Glucocorticoids
• Methylprednisolone
• SARS-CoV-2
• Tocilizumab

• Antibodies, Monoclonal, Humanized
• Glucocorticoids/therapeutic use
• Humans
• Retrospective Studies
• SARS-CoV-2
• Treatment Outcome
• COVID-19 Drug Treatment

• Clinical Protocols
• Coronavirus
• Mass Screening
• Radiation Dosage
• Tomography, X-Ray Computed

The computed tomography (CT) diagnostic value of COVID-19 is controversial. We summarized the value of chest CT in the diagnosis of COVID-19 through a meta-analysis based on the reference standard.

All Chinese and English studies related to the diagnostic value of CT for COVID-19 across multiple publication platforms, was searched for and collected. Studies quality evaluation and plotting the risk of bias were estimated. A heterogeneity test and meta-analysis, including plotting sensitivity (Sen), specificity (Spe) forest plots, pooled positive likelihood ratio (+LR), negative likelihood ratio (-LR), dignostic odds ratio (DOR) values and 95% confidence interval (CI), were estimated. If there was a threshold effect, summary receiver operating characteristic curves (SROC) was further plotted. Pooled area under the receiver operating characteristic curve (AUROC) and 95% CI were also calculated.

Twenty diagnostic studies that represented a total of 9004 patients were included from 20 pieces of literatures after assessing all the aggregated studies. The reason for heterogeneity was caused by the threshold effect, so the AUROC = 0.91 (95% CI: 0.89–0.94) for chest CT of COVID-19. Pooled sensitivity, specificity, +LR, -LR from 20 studies were 0.91 (95% CI: 0.88–0.94), 0.71 (95% CI: 0.59–0.80), 3.1(95% CI: 2.2–4.4), 0.12 (95% CI: 0.09–0.17), separately. The I² was 85.6% (P = 0.001) by Q-test.

The results of this study showed that CT diagnosis of COVID-19 was close to the reference standard. The diagnostic value of chest CT may be further enhanced if there is a unified COVID-19 diagnostic standard. However, please pay attention to rational use of CT.

• COVID-19
• Reference standard
• Tomography
• X-ray computed

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

• Artificial intelligence
• Coronavirus infections
• Machine learning
• Pneumonia
• Tomography, X-ray computed

• COVID-19
• CT
• RT-PCR
• sensitivity
• specificity
• systematic review

• consensus
• coronavirus disease 2019
• internal medicine
• lung
• point-of-care ultrasound

• COVID-19
• Canada
• Consensus
• Humans
• Inpatients
• Lung/diagnostic imaging
• SARS-CoV-2

• COVID-19
• Computed tomography
• Corona virus
• Deep learning
• Machine learning
• Medical image computing
• Medical imaging

• Artificial Intelligence
• COVID-19/diagnostic imaging
• COVID-19 Testing
• Humans
• Image Processing, Computer-Assisted
• Tomography, X-Ray Computed

• Artificial intelligence
• Bronchogenic carcinoma
• COVID-19
• Immune checkpoint inhibitor-related pneumonitis
• Prioritizing imaging
• Surveillance of lung nodules

We aim to investigate high-resolution CT features of COVID-19 infection in Abu Dhabi, UAE, and to compare the diagnostic performance of CT scan with RT-PCR test.

Data of consecutive patients who were suspected to have COVID-19 infection and presented to our hospital were collected from March 2, 2020, until April 12, 2020. All patients underwent RT-PCR test; out of which 53.8% had chest CT scan done. Using RT-PCR as a standard reference, the sensitivity and specificity of the CT scan were calculated. We also analyzed the most common imaging findings in patients with positive RT-PCR results.

The typical HRCT findings were seen in 50 scans (65.8%) out of total positive ones; 44 (77.2%) with positive RT-PCR results and 6 (31.6%) with negative results. The peripheral disease distribution was seen in 86%, multilobe involvement in 70%, bilateral in 82%, and posterior in 82% of the 50 scans. The ground glass opacities were seen in 50/74 (89.3%) of the positive RT-PCR group. The recognized GGO patterns in these scans were: rounded 50%, linear 38%, and crazy-paving 24%. Using RT-PCR as a standard of reference, chest HRCT scan revealed a sensitivity of 68.8% and specificity of 70%.

The commonest HRCT findings in patients with COVID-19 pneumonia were peripheral, posterior, bilateral, multilobe rounded ground-glass opacities. The performance of HRCT scan can vary depending on multiple factors.

• COVID-19
• Ground glass opacity
• HRCT
• RT-PCR

We assessed the impact of chest CT body composition parameters on outcomes and disease severity at hospital presentation of COVID-19 patients, focusing also on the possible mediation of body composition in the relationship between age and death in these patients. Chest CT scans performed at hospital presentation by consecutive COVID-19 patients (02/27/2020-03/13/2020) were retrospectively reviewed to obtain pectoralis muscle density and total, visceral, and intermuscular adipose tissue areas (TAT, VAT, IMAT) at the level of T7-T8 vertebrae. Primary outcomes were: hospitalization, mechanical ventilation (MV) and/or death, death alone. Secondary outcomes were: C-reactive protein (CRP), oxygen saturation (SO2), CT disease extension at hospital presentation. The mediation of body composition in the effect of age on death was explored. Of the 318 patients included in the study (median age 65.7 years, females 37.7%), 205 (64.5%) were hospitalized, 68 (21.4%) needed MV, and 58 (18.2%) died. Increased muscle density was a protective factor while increased TAT, VAT, and IMAT were risk factors for hospitalization and MV/death. All these parameters except TAT had borderline effects on death alone. All parameters were associated with SO2 and extension of lung parenchymal involvement at CT; VAT was associated with CRP. Approximately 3% of the effect of age on death was mediated by decreased muscle density. In conclusion, low muscle quality and ectopic fat accumulation were associated with COVID-19 outcomes, VAT was associated with baseline inflammation. Low muscle quality partly mediated the effect of age on mortality.

• Aged
• Aged, 80 and over
• Body Composition
• Body Mass Index
• COVID-19/diagnostic imaging
• COVID-19/epidemiology
• COVID-19/mortality
• COVID-19/virology
• Female
• Hospitalization
• Humans
• Intra-Abdominal Fat/diagnostic imaging
• Italy/epidemiology
• Male
• Mass Chest X-Ray/methods
• Middle Aged
• Respiration, Artificial
• Retrospective Studies
• Risk Factors
• SARS-CoV-2/genetics
• Tomography, X-Ray Computed/methods
• Treatment Outcome

• Ministero della Salute

The aims of this study were to develop a multiparametric prognostic model for death in COVID-19 patients and to assess the incremental value of CT disease extension over clinical parameters.

Consecutive patients who presented to all five of the emergency rooms of the Reggio Emilia province between February 27 and March 23, 2020, for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20–39%, 40–59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using k-fold cross-validation for the area under the ROC curve (cvAUC).

Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level. CT disease extension was also independently associated with death (HR = 7.56, 95% CI = 3.49; 16.38 for ≥ 60% extension). cvAUCs were 0.927 (bootstrap bias-corrected 95% CI = 0.899–0.947) for the clinical model and 0.936 (bootstrap bias-corrected 95% CI = 0.912–0.953) when adding CT extension.

A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy.

• Early identification of COVID-19 patients at higher risk of disease progression and death is crucial; the role of CT scan in defining prognosis is unclear.

• A clinical model based on age, sex, comorbidities, days from symptom onset, and laboratory results was highly accurate in predicting death in COVID-19 patients presenting to the emergency room.

• Disease extension assessed with CT was independently associated with death when added to the model but did not produce a valuable increase in accuracy.

The online version contains supplementary material available at 10.1007/s00330-021-07993-9.

• COVID-19
• Clinical prediction rule
• Multidetector computed tomography
• Prognosis

• COVID-19
• CT
• RT-PCR
• accuracy
• sensitivity
• specificity

• Adolescent
• Adult
• Aged
• Bias
• COVID-19/diagnostic imaging
• COVID-19 Nucleic Acid Testing/standards
• Child
• Confidence Intervals
• Humans
• Lung/diagnostic imaging
• Middle Aged
• Radiography, Thoracic/standards
• Radiography, Thoracic/statistics & numerical data
• Reference Standards
• Sensitivity and Specificity
• Tomography, X-Ray Computed/standards
• Tomography, X-Ray Computed/statistics & numerical data
• Ultrasonography/standards
• Ultrasonography/statistics & numerical data
• Young Adult

• MR/T008768/1 Medical Research Council

• COVID-19
• Clinical laboratory
• Computed tomography

• COVID-19/diagnosis
• COVID-19/virology
• COVID-19 Nucleic Acid Testing/methods
• False Negative Reactions
• Humans
• RNA, Viral/genetics
• RNA, Viral/isolation & purification
• SARS-CoV-2/genetics
• SARS-CoV-2/isolation & purification

• COVID-19/diagnosis
• COVID-19/virology
• COVID-19 Nucleic Acid Testing/methods
• False Negative Reactions
• Humans
• RNA, Viral/genetics
• RNA, Viral/isolation & purification
• SARS-CoV-2/genetics
• SARS-CoV-2/isolation & purification