T Helper 17 Response to Severe Acute Respiratory Syndrome Coronavirus 2: A Type of Immune Response with Possible Therapeutic Implications.

The initial immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) includes an interferon-dependent antiviral response. A late and uncontrolled inflammatory response characterized by high activity of proinflammatory cytokines and the recruitment of neutrophils and macrophages develops in predisposed individuals and is potentially harmful in some cases. Interleukin (IL)-17 is one of the many cytokines released during coronavirus disease 2019 (COVID-19). IL-17 is crucial in recruiting and activating neutrophils, cells that can migrate to the lung, and are heavily involved in the pathogenesis of COVID-19. During the infection T helper 17 (Th17) cells and IL-17-related pathways are associated with a worse outcome of the disease. All these have practical consequences considering that some drugs with therapeutic targets related to the Th17 response may have a beneficial effect on patients with SARS-CoV-2 infection. Herein, we present the arguments underlying our assumption that blocking the IL-23/IL-17 axis using targeted biological therapies as well as drugs that act indirectly on this pathway such as convalescent plasma therapy and colchicine may be good therapeutic options.

Use of the COVID-19 Reporting and Data System (CO-RADS) classification and chest computed tomography involvement score (CT-IS) in COVID-19 pneumonia.

PURPOSE: The increasing tendency of chest CT usage throughout the COVID-19 epidemic requires new tools and a systematic scheme for diagnosing and assessing the lung involvement in Coronavirus Disease 2019 (COVID-19). To investigate the use of the COVID-19 Reporting and Data System (CO-RADS) classification and chest CT Involvement Score (CT-IS) in COVID-19 pneumonia. MATERIAL AND METHODS: This retrospective study enrolled 280 hospitalized patients diagnosed with COVID-19 pneumonia in a tertiary hospital in Turkey. All patients underwent non-contrast CT chest imaging. Two radiologists interpreted all CT images according to CO-RADS classification without knowing the clinical features, laboratory findings. We used CT involvement score (CT-IS) for assessing chest CT images of COVID-19 patients. Also, we examined the relationship between CT-IS and clinical outcomes in COVID-19 patients. RESULTS: Of the patients, 111(39.6%) had positive real-time reverse transcriptase-polymerase chain reaction (RT-PCR) results. CO-RADS 5 group patients had statistically significant positive RT-PCR results than the other groups (P < 0.001). All of the CO-RADS 2 group patients (30) had negative RT-PCR results. The mean total CT-IS in CO-RADS 2 group was 3.4 ± 2.8. The mean total CT-IS in CO-RADS 5 group was 8.2 ± 4.7. Total CT-IS was statistically significantly different among CO-RADS groups (P < 0.001). The mean total CT-IS was statistically significantly different between survivors and patients died of COVID-19 pneumonia (P < 0.001). CONCLUSIONS: CO-RADS is useful in detecting COVID-19 disease, even if RT-PCR testing is negative. CT-IS is also helpful as an imaging tool for evaluation of the severity and extent of COVID-19 pneumonia.

Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data.

OBJECTIVE: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Deep Ensemble Model for Classification of Novel Coronavirus in Chest X-Ray Images.

The novel coronavirus, SARS-CoV-2, can be deadly to people, causing COVID-19. The ease of its propagation, coupled with its high capacity for illness and death in infected individuals, makes it a hazard to the community. Chest X-rays are one of the most common but most difficult to interpret radiographic examination for early diagnosis of coronavirus-related infections. They carry a considerable amount of anatomical and physiological information, but it is sometimes difficult even for the expert radiologist to derive the related information they contain. Automatic classification using deep learning models can help in better assessing these infections swiftly. Deep CNN models, namely, MobileNet, ResNet50, and InceptionV3, were applied with different variations, including training the model from the start, fine-tuning along with adjusting learned weights of all layers, and fine-tuning with learned weights along with augmentation. Fine-tuning with augmentation produced the best results in pretrained models. Out of these, two best-performing models (MobileNet and InceptionV3) selected for ensemble learning produced accuracy and FScore of 95.18% and 90.34%, and 95.75% and 91.47%, respectively. The proposed hybrid ensemble model generated with the merger of these deep models produced a classification accuracy and FScore of 96.49% and 92.97%. For test dataset, which was separately kept, the model generated accuracy and FScore of 94.19% and 88.64%. Automatic classification using deep ensemble learning can help radiologists in the correct identification of coronavirus-related infections in chest X-rays. Consequently, this swift and computer-aided diagnosis can help in saving precious human lives and minimizing the social and economic impact on society.

Hemophagocytic lymphohistiocytosis: a review inspired by the COVID-19 pandemic.

Hemophagocytic syndrome (HPS) or hemophagocytic lymphohistiocytosis (HLH) is an acute and rapidly progressive systemic inflammatory disorder characterized by cytopenia, excessive cytokine production, and hyperferritinemia. Common clinical manifestations of HLH are acute unremitting fever, lymphadenopathy, hepatosplenomegaly, and multiorgan failure. Due to a massive cytokine release, this clinical condition is considered as a cytokine storm syndrome. HPS has primary and acquired (secondary, reactive) forms. Its primary form is mostly seen in childhood and caused by various mutations with genetic inheritance and, therefore, is called familial HLH. Secondary HLH may be caused in the presence of an underlying disorder, that is, secondary to a malignant, infectious, or autoimmune/autoinflammatory stimulus. This paper aims to review the pathogenesis and the clinical picture of HLH, and its severe complication, the cytokine storm, with a special emphasis on the developed classification criteria sets for rheumatologists, since COVID-19 infection has clinical symptoms resembling those of the common rheumatologic conditions and possibly triggers HLH. MED-LINE/Pubmed was searched from inception to April 2020, and the following terms were used for data searching: "hemophagocytic syndrome" OR "macrophage activation syndrome" OR "hemophagocytic lymphohistiocytosis", OR "cytokine storm". Finally, AND "COVID-19" was included in this algorithm. The selection is restricted to the past 5 years and limited numbers of earlier key references were manually selected. Only full-text manuscripts, published in an English language peer-reviewed journal were included. Manuscript selection procedure and numbers are given in Fig. 2. Briefly, the database search with the following terms of "Hemophagocytic syndrome" OR "Macrophage activation syndrome" OR "Hemophagocytic lymphohistiocytosis" OR "Cytokine storm" yielded 6744 results from inception to April 2020. The selection is restricted to the past 5 years and only limited numbers of earlier key references were selected, and this algorithm resulted in 3080 manuscripts. The addition of (AND "COVID-19") resulted in 115 publications of which 47 studies, together with four sections of an online book were used in the final review. No statistical method was used. HLH is triggered by genetic conditions, infections, malignancies, autoimmune-autoinflammatory diseases, and some drugs. In COVID-19 patients, secondary HLH and cytokine storm may be responsible for unexplained progressive fever, cytopenia, ARDS, neurological and renal impairment. Differentiation between the primary and secondary forms of HLH is utterly important, since primary form of HLH requires complicated treatments such as hematopoietic stem cell transplantation. Further studies addressing the performance of HScore and other recommendations in the classification of these patients is necessary.

Haemophagocytic syndrome and COVID-19.

Primary and secondary haemophagocytic lymphohistiocytosis (HLH) are hyperferritinaemic hyperinflammatory syndromes with a common terminal pathway triggered by different etiopathogenetic factors. HLH is characterised by a decreased capacity of interferon gamma production with an activated NK phenotype profile similar to other hyperinflammatory syndromes. Viruses are closely linked to the development of HLH as infectious triggers, and the break of tolerance to self-antigens is considered a critical mechanism involved in the development of immune-mediated conditions triggered by viral infections. Emerging studies in patients with COVID-19 are suggesting a key role of monocytes/macrophages in the pathogenesis of this viral infection, and there is a significant overlap between several features reported in severe COVID-19 and the features included in the HLH-2004 diagnostic criteria. Therefore, SARS-Cov-2, as other respiratory viruses, may also be considered a potential etiological trigger of HLH. The frequency of HLH in adult patients with severe COVID-19 is lower than 5%, although this figure could be underestimated considering that most reported cases lacked information about some specific criteria (mainly the histopathological criteria and the measurement of NK cell function and sCD25 levels). Because HLH is a multi-organ syndrome, the diagnostic approach in a patient with severe COVID-19 in whom HLH is suspected must be carried out in a syndromic and holistic way, and not in the light of isolated clinical or laboratory features. In COVID-19 patients presenting with persistent high fever, progressive pancytopenia, and hepatosplenic involvement, together with the characteristic triad of laboratory abnormalities (hyperferritinaemia, hypertriglyceridaemia, and hypofibrinogenaemia), the suspicion of HLH is high, and the diagnostic workup must be completed with specific immunological and histopathological studies.

COVID-19-Zoonosis or Emerging Infectious Disease?

The World Health Organization defines a zoonosis as any infection naturally transmissible from vertebrate animals to humans. The pandemic of Coronavirus disease (COVID-19) caused by SARS-CoV-2 has been classified as a zoonotic disease, however, no animal reservoir has yet been found, so this classification is premature. We propose that COVID-19 should instead be classified an "emerging infectious disease (EID) of probable animal origin." To explore if COVID-19 infection fits our proposed re-categorization vs. the contemporary definitions of zoonoses, we reviewed current evidence of infection origin and transmission routes of SARS-CoV-2 virus and described this in the context of known zoonoses, EIDs and "spill-over" events. Although the initial one hundred COVID-19 patients were presumably exposed to the virus at a seafood Market in China, and despite the fact that 33 of 585 swab samples collected from surfaces and cages in the market tested positive for SARS-CoV-2, no virus was isolated directly from animals and no animal reservoir was detected. Elsewhere, SARS-CoV-2 has been detected in animals including domesticated cats, dogs, and ferrets, as well as captive-managed mink, lions, tigers, deer, and mice confirming zooanthroponosis. Other than circumstantial evidence of zoonotic cases in mink farms in the Netherlands, no cases of natural transmission from wild or domesticated animals have been confirmed. More than 40 million human COVID-19 infections reported appear to be exclusively through human-human transmission. SARS-CoV-2 virus and COVID-19 do not meet the WHO definition of zoonoses. We suggest SARS-CoV-2 should be re-classified as an EID of probable animal origin.

Comparison of 2 Triage Scoring Guidelines for Allocation of Mechanical Ventilators.

Importance: In the current setting of the coronavirus disease 2019 pandemic, there is concern for the possible need for triage criteria for ventilator allocation; to our knowledge, the implications of using specific criteria have never been assessed. Objective: To determine which and how many admissions to intensive care units are identified as having the lowest priority for ventilator allocation using 2 distinct sets of proposed triage criteria. Design, Setting, and Participants: This retrospective cohort study conducted in spring 2020 used data collected from US hospitals and reported in the Philips eICU Collaborative Research Database. Adult admissions (N = 40 439) to 291 intensive care units from 2014 to 2015 who received mechanical ventilation and were not elective surgery patients were included. Exposures: New York State triage criteria and original triage criteria proposed by White and Lo. Main Outcomes and Measures: Sequential Organ Failure Assessment (SOFA) scores were calculated for admissions. The proportion of patients who met initial criteria for the lowest level of priority for mechanical ventilation using each set of criteria and their characteristics and outcomes were assessed. Agreement was compared between the 2 sets of triage criteria, recognizing differences in stated criteria aims. Results: Among 40 439 intensive care unit admissions of patients who received mechanical ventilation, the mean (SD) age was 62.6 (16.6) years, 54.9% were male, and the mean (SD) SOFA score was 4.5 (3.7). Using the New York State triage criteria, 8.9% (95% CI, 8.7%-9.2%) were in the lowest priority category; these lowest priority admissions had a mean (SD) age of 62.9 (16.6) years, used a median (interquartile range) of 57.3 (20.1-133.5) ventilator hours each, and had a hospital survival rate of 38.6% (95% CI, 37.0%-40.2%). Using the White and Lo triage criteria, 4.3% (95% CI, 4.1%-4.5%) were in the lowest priority category; these admissions had a mean (SD) age of 68.6 (13.2) years, used a median (interquartile range) of 61.7 (24.3-142.8) ventilator hours each, and had a hospital survival rate of 56.2% (95% CI, 53.8%-58.7%). Only 655 admissions (1.6%) were in the lowest priority category for both guidelines, with the κ statistic for agreement equal to 0.20 (95% CI, 0.18-0.21). Conclusions and Relevance: Use of 2 initially proposed ventilator triage guidelines identified approximately 1 in every 10 to 25 admissions as having the lowest priority for ventilator allocation, with little agreement. Clinical assessment of different potential criteria for triage decisions in critically ill populations is important to ensure valid and equitable allocation of resources.

Clinical stability and in-hospital mortality prediction in COVID-19 patients presenting to the Emergency Department.

BACKGROUND: The Novara-COVID score was developed to help the emergency physician to decide which Coronavirus disease (COVID) patient required hospitalization at Emergency Department (ED) presentation and to which intensity of care. We aimed at evaluating its prognostic role. METHODS: We retrospectively collected data of COVID patients admitted to our ED between March 16 and April 22, 2020. The Novara-COVID score was systematically applied to all COVID patients since its introduction in clinical practice and adopted to decide patients' destination. The ability of the Novara-COVID score to predict in-hospital clinical stability and in-hospital mortality were evaluated through multivariable logistic regression and cox regression hazard models, respectively. RESULTS: Among the 480 COVID patients admitted to the ED, 338 were hospitalized: the Novara-COVID score was 0-1 in 49.7%, 2 in 24.6%, 3 in 15.4% and 4-5 in 10.3% of patients. Novara-COVID score values of 3 and 4-5 were associated with lower clinical stability with adjusted odds ratios of 0.28 (0.13-0.59) and 0.03 (0.01-0.12), respectively. When in-hospital mortality was evaluated, a significant difference emerged between scores of 0-1 and 2 vs. 3 and 4-5. In particular, the death adjusted hazard ratio for Novara-COVID scores of 3 and 4-5 were 2.6 (1.4-4.8) and 8.4 (4.7-15.2), respectively. CONCLUSIONS: The Novara-COVID score reliably predicts in-hospital clinical instability and mortality of COVID patients at ED presentation. This tool allows the emergency physician to detect patients at higher risk of clinical deterioration, suggesting a more aggressive therapeutic management from the beginning.

Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan.

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.

Is extensive cardiopulmonary screening useful in athletes with previous asymptomatic or mild SARS-CoV-2 infection?

OBJECTIVE: During the COVID-19 pandemic, it is essential to understand if and how to screen SARS-CoV-2-positive athletes to safely resume training and competitions. The aim of this study is to understand which investigations are useful in a screening protocol aimed at protecting health but also avoiding inappropriate examinations. METHODS: We conducted a cohort study of a professional soccer team that is based on an extensive screening protocol for resuming training during the COVID-19 pandemic. It included personal history, antigen swabs, blood tests, spirometry, resting/stress-test ECG with oxygen saturation monitoring, echocardiogram, Holter and chest CT. We also compared the findings with prior data from the same subjects before infection and with data from SARS-CoV-2-negative players. RESULTS: None of the players had positive swab and/or anti-SARS-CoV-2 IgM class antibodies. Out of 30 players, 18 (60%) had IgG class antibodies. None had suffered severe SARS-CoV-2-related disease, 12 (66.7%) had complained of mild COVID-19-related symptoms and 6 (33.3%) were asymptomatic. None of the players we examined revealed significant cardiovascular abnormalities after clinical recovery. A mild reduction in spirometry parameters versus pre-COVID-19 values was observed in all athletes, but it was statistically significant (p<0.05) only in SARS-CoV-2-positive athletes. One SARS-CoV-2-positive player showed increased troponin I level, but extensive investigation did not show signs of myocardial damage. CONCLUSION: In this small cohort of athletes with previous asymptomatic/mild SARS-CoV-2 infection, a comprehensive screening protocol including blood tests, spirometry, resting ECG, stress-test ECG with oxygen saturation monitoring and echocardiogram did not identify relevant anomalies. While larger studies are needed, extensive cardiorespiratory and haematological screening in athletes with asymptomatic/mild SARS-CoV-2 infection appears unnecessary.

Risk of severe coronavirus disease in imported and secondary cases in Zhejiang province, China.

BACKGROUND: To our knowledge, no previous studies have focused on determining whether the virulence and case fatality rate of the severe acute respiratory coronavirus 2 (SARS-CoV-2) decreases as the virus continues to spread. Hence, our aim was to retrospectively explore the differences in the risk of severe or critical COVID-19 among imported, secondary and tertiary cases in Zhejiang, China. METHODS: We categorized COVID-19 cases reported by hospitals in Zhejiang as first-, second- and third-generation cases. Univariate and multivariate logistic regression analyses were performed to compare disease severity and case generation. RESULTS: Of 1187 COVID-19 cases, 227 (19.1%, 95% CI: 16.9-21.4) manifested severe or critical illness. The adjusted risk difference for severe or critical illness was lower for second- (odds ratio (OR) = 0.84, 95% confidence interval (CI): 0.52-1.36) and third-generation (OR = 0.55, 95% CI: 0.37-0.83) cases than for first-generation cases. Compared with hospitalized patients, cases identified at centralized isolation locations (OR = 0.62, 95% CI: 0.40-0.97) and those identified through active search or gateway screening (OR = 0.28, 95% CI: 0.08-1.04) were at a lower risk of severe or critical illness. CONCLUSIONS: Second- and third-generation cases of COVID-19 have a lower risk of developing severe or critical illness than first-generation cases.

Dynamic changes in fibrinogen and D-dimer levels in COVID-19 patients on nafamostat mesylate.

Critical illnesses associated with coronavirus disease 2019 (COVID-19) are attributable to a hypercoagulable status. There is limited knowledge regarding the dynamic changes in coagulation factors among COVID-19 patients on nafamostat mesylate, a potential therapeutic anticoagulant for COVID-19. First, we retrospectively conducted a cluster analysis based on clinical characteristics on admission to identify latent subgroups among fifteen patients with COVID-19 on nafamostat mesylate at the University of Tokyo Hospital, Japan, between April 6 and May 31, 2020. Next, we delineated the characteristics of all patients as well as COVID-19-patient subgroups and compared dynamic changes in coagulation factors among each subgroup. The subsequent dynamic changes in fibrinogen and D-dimer levels were presented graphically. All COVID-19 patients　were classified into three subgroups: clusters A, B, and C, representing low, intermediate, and high risk of poor outcomes, respectively. All patients were alive 30 days from symptom onset. No patient in cluster A required mechanical ventilation; however, all patients in cluster C required mechanical ventilation, and half of them were treated with venovenous extracorporeal membrane oxygenation. All patients in cluster A maintained low D-dimer levels, but some critical patients in clusters B and C showed dynamic changes in fibrinogen and D-dimer levels. Although the potential of nafamostat mesylate needs to be evaluated in randomized clinical trials, admission characteristics of patients with COVID-19 could predict subsequent coagulopathy.

Prevalence of phenotypes of acute respiratory distress syndrome in critically ill patients with COVID-19: a prospective observational study.

BACKGROUND: In acute respiratory distress syndrome (ARDS) unrelated to COVID-19, two phenotypes, based on the severity of systemic inflammation (hyperinflammatory and hypoinflammatory), have been described. The hyperinflammatory phenotype is known to be associated with increased multiorgan failure and mortality. In this study, we aimed to identify these phenotypes in COVID-19-related ARDS. METHODS: In this prospective observational study done at two UK intensive care units, we recruited patients with ARDS due to COVID-19. Demographic, clinical, and laboratory data were collected at baseline. Plasma samples were analysed for interleukin-6 (IL-6) and soluble tumour necrosis factor receptor superfamily member 1A (TNFR1) using a novel point-of-care assay. A parsimonious regression classifier model was used to calculate the probability for the hyperinflammatory phenotype in COVID-19 using IL-6, soluble TNFR1, and bicarbonate levels. Data from this cohort was compared with patients with ARDS due to causes other than COVID-19 recruited to a previous UK multicentre, randomised controlled trial of simvastatin (HARP-2). FINDINGS: Between March 17 and April 25, 2020, 39 patients were recruited to the study. Median ratio of partial pressure of arterial oxygen to fractional concentration of oxygen in inspired air (PaO2/FiO2) was 18 kpa (IQR 15-21) and acute physiology and chronic health evaluation II score was 12 (10-16). 17 (44%) of 39 patients had died by day 28 of the study. Compared with survivors, patients who died were older and had lower PaO2/FiO2. The median probability for the hyperinflammatory phenotype was 0·03 (IQR 0·01-0·2). Depending on the probability cutoff used to assign class, the prevalence of the hyperinflammatory phenotype was between four (10%) and eight (21%) of 39, which is lower than the proportion of patients with the hyperinflammatory phenotype in HARP-2 (186 [35%] of 539). Using the Youden index cutoff (0·274) to classify phenotype, five (63%) of eight patients with the hyperinflammatory phenotype and 12 (39%) of 31 with the hypoinflammatory phenotype died. Compared with matched patients recruited to HARP-2, levels of IL-6 were similar in our cohort, whereas soluble TNFR1 was significantly lower in patients with COVID-19-associated ARDS. INTERPRETATION: In this exploratory analysis of 39 patients, ARDS due to COVID-19 was not associated with higher systemic inflammation and was associated with a lower prevalence of the hyperinflammatory phenotype than that observed in historical ARDS data. This finding suggests that the excess mortality observed in COVID-19-related ARDS is unlikely to be due to the upregulation of inflammatory pathways described by the parsimonious model. FUNDING: US National Institutes of Health, Innovate UK, and Randox.