which biomarkers predict the severe clinical course of 2019-nCOV infection?

Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury

The outbreak of the 2019-nCoV infection began in December 2019 in Wuhan, Hubei province, and rapidly spread to many provinces in China as well as other countries. Here we report the epidemiological, clinical, laboratory, and radiological characteristics, as well as potential biomarkers for predicting disease severity in 2019-nCoV-infected patients in Shenzhen, China. All 12 cases of the 2019-nCoV-infected patients developed pneumonia and half of them developed acute respiratory distress syndrome (ARDS). The most common laboratory abnormalities were hypoalbuminemia, lymphopenia, decreased percentage of lymphocytes (LYM) and neutrophils (NEU), elevated C-reactive protein (CRP) and lactate dehydrogenase (LDH), and decreased CD8 count. The viral load of 2019-nCoV detected from patient respiratory tracts was positively linked to lung disease severity. ALB, LYM, LYM (%), LDH, NEU (%), and CRP were highly correlated to the acute lung injury. Age, viral load, lung injury score, and blood biochemistry indexes, albumin (ALB), CRP, LDH, LYM (%), LYM, and NEU (%), may be predictors of disease severity. Moreover, the Angiotensin II level in the plasma sample from 2019-nCoV infected patients was markedly elevated and linearly associated to viral load and lung injury. Our results suggest a number of potential diagnosis biomarkers and angiotensin receptor blocker (ARB) drugs for potential repurposing treatment of 2019-nCoV infection. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available for this article at 10.1007/s11427-020-1643-8 and is accessible for authorized users.

Background: Severe ill patients with 2019 novel coronavirus (2019-nCoV) infection progressed rapidly to acute respiratory failure. We aimed to select the most useful prognostic factor for severe illness incidence. Methods: The study prospectively included 61 patients with 2019-nCoV infection treated at Beijing Ditan Hospital from January 13, 2020 to January 31, 2020. Prognostic factor of severe illness was selected by the LASSO COX regression analyses, to predict the severe illness probability of 2019-CoV pneumonia. The predictive accuracy was evaluated by concordance index, calibration curve, decision curve and clinical impact curve. Results: The neutrophil-to-lymphocyte ratio (NLR) was identified as the independent risk factor for severe illness in patients with 2019-nCoV infection. The NLR had a c-index of 0.807 (95% confidence interval, 0.676-0.38), the calibration curves fitted well, and the decision curve and clinical impact curve showed that the NLR had superior standardized net benefit. In addition, the incidence of severe illness was 9.1% in age ≥ 50 and NLR < 3.13 patients, and half of patients with age ≥ 50 and NLR ≥ 3.13 would develop severe illness. Based on the risk stratification of NLR with age, the study developed a 2019-nCoV pneumonia management process. Conclusions: The NLR was the early identification of risk factors for 2019-nCoV severe illness. Patients with age ≥ 50 and NLR ≥ 3.13 facilitated severe illness, and they should rapidly access to intensive care unit if necessary.

Neutrophil-to-Lymphocyte Ratio Predicts Severe Illness Patients with 2019 Novel Coronavirus in the Early Stage

The coronavirus disease 2019 (COVID-19) pandemic is a scientific, medical, and social challenge. The complexity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is centered on the unpredictable clinical course of the disease that can rapidly develop, causing severe and deadly complications. The identification of effective laboratory biomarkers able to classify patients based on their risk is imperative in being able to guarantee prompt treatment. The analysis of recently published studies highlights the role of systemic vasculitis and cytokine mediated coagulation disorders as the principal actors of multi organ failure in patients with severe COVID-19 complications. The following biomarkers have been identified: hematological (lymphocyte count, neutrophil count, neutrophil-lymphocyte ratio (NLR)), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT)), immunological (interleukin (IL)-6 and biochemical (D-dimer, troponin, creatine kinase (CK), aspartate aminotransferase (AST)), especially those related to coagulation cascades in disseminated intravascular coagulation (DIC) and acute respiratory distress syndrome (ARDS). New laboratory biomarkers could be identified through the accurate analysis of multicentric case series; in particular, homocysteine and angiotensin II could play a significant role.

Biomarkers associated with COVID-19 disease progression

The coronavirus disease 2019 (COVID-19) pandemic is a scientific, medical, and social challenge. The complexity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is centered on the unpredictable clinical course of the disease that can rapidly develop, causing severe and deadly complications. The identification of effective laboratory biomarkers able to classify patients based on their risk is imperative in being able to guarantee prompt treatment. The analysis of recently published studies highlights the role of systemic vasculitis and cytokine mediated coagulation disorders as the principal actors of multi organ failure in patients with severe COVID-19 complications. The following biomarkers have been identified: hematological (lymphocyte count, neutrophil count, neutrophil–lymphocyte ratio (NLR)), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT)), immunological (interleukin (IL)-6 and biochemical (D-dimer, troponin, creatine kinase (CK), aspartate aminotransferase (AST)), especially those related to coagulation cascades in disseminated intravascular coagulation (DIC) and acute respiratory distress syndrome (ARDS). New laboratory biomarkers could be identified through the accurate analysis of multicentric case series; in particular, homocysteine and angiotensin II could play a significant role.

Infection of novel Coronavirus has been declared pandemic by the WHO and now is a world public health crisis. Laboratory activity becames essential for the timely diagnosis. Few parameters, such Lymphocytes count, SaO2 and CRP serum level can be used to assess the severity of COVID-19 in emergency room.

Laboratory Biomarkers Predicting COVID-19 Severity in the Emergency Room

ACE2, COVID19 and serum ACE as a possible biomarker to predict severity of disease

Biomarkers of biological age as predictors of COVID-19 disease severity

OBJECTIVE: Most cases of coronavirus disease 2019 (COVID-19) are identified as moderate, which is defined as having a fever or dry cough and lung imaging with ground-glass opacities. The risk factors and predictors of prognosis in such cohorts remain uncertain. METHODS: All adults with COVID-19 of moderate severity diagnosed using quantitative RT-PCR and hospitalized at the Central Hospital of Wuhan, China, from 1 January to 20 March 2020 were enrolled in this retrospective study. The main outcomes were progression from moderate to severe or critical condition or death. RESULTS: Among the 456 enrolled patients with moderate COVID-19, 251/456 (55.0%) had poor prognosis. Multivariate logistic regression analysis identified higher neutrophil count: lymphocyte count ratio (NLR) on admission (OR 1.032, 95% CI 1.042-1.230, p 0.004) and higher C-reactive protein (CRP) on admission (OR 3.017, 95% CI 1.941-4.690, p < 0.001) were associated with increased OR of poor prognosis. The area under the receiver operating characteristic curve (AUC) for NLR and CRP in predicting progression to critical condition was 0.77 (95% CI 0.694-0.846, p < 0.001) and 0.84 (95% CI 0.780-0.905, p < 0.001), with a cut-off value of 2.79 and 25.95 mg/L, respectively. The AUC of NLR and CRP in predicting death was 0.81 (95% CI 0.732-0.878, p < 0.001) and 0.89 (95% CI 0.825-0.946, p < 0.001), with a cut-off value of 3.19 and 33.4 mg/L, respectively. CONCLUSIONS: Higher levels of NLR and CRP at admission were associated with poor prognosis of individuals with moderate COVID-19. NLR and CRP were good predictors of progression to critical condition and death.

Predictors of progression from moderate to severe coronavirus disease 2019: a retrospective cohort

COVID-19 is characterised by dysregulated immune responses, metabolic dysfunction and adverse effects on the function of multiple organs. To understand how host responses contribute to COVID-19 pathophysiology, we used a multi-omics approach to identify molecular markers in peripheral blood and plasma samples that distinguish COVID-19 patients experiencing a range of disease severities. A large number of expressed genes, proteins, metabolites and extracellular RNAs (exRNAs) were identified that exhibited strong associations with various clinical parameters. Multiple sets of tissue-specific proteins and exRNAs varied significantly in both mild and severe patients, indicative of multi-organ damage. The continuous activation of IFN-I signalling and neutrophils, as well as a high level of inflammatory cytokines, were observed in severe disease patients. In contrast, COVID-19 in mild patients was characterised by robust T cell responses. Finally, we show that some of expressed genes, proteins and exRNAs can be used as biomarkers to predict the clinical outcomes of SARS-CoV-2 infection. These data refine our understanding of the pathophysiology and clinical progress of COVID-19 and will help guide future studies in this area.

COVID-19 severity is associated with immunopathology and multi-organ damage

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 was first reported in Wuhan, December 2019, and continuously poses a serious threat to public health, highlighting the urgent need of identifying biomarkers for disease severity and progression. OBJECTIVE: We sought to identify biomarkers for disease severity and progression of COVID-19. METHODS: Forty-eight cytokines in the plasma samples from 50 COVID-19 cases including 11 critically ill, 25 severe, and 14 moderate patients were measured and analyzed in combination with clinical data. RESULTS: Levels of 14 cytokines were found to be significantly elevated in COVID-19 cases and showed different expression profiles in patients with different disease severity. Moreover, expression levels of IFN-Î³-induced protein 10, monocyte chemotactic protein-3, hepatocyte growth factor, monokine-induced gamma IFN, and macrophage inflammatory protein 1 alpha, which were shown to be highly associated with disease severity during disease progression, were remarkably higher in critically ill patients, followed by severe and then the moderate patients. Serial detection of the 5 cytokines in 16 cases showed that continuously high levels were associated with deteriorated progression of disease and fatal outcome. Furthermore, IFN-Î³-induced protein 10 and monocyte chemotactic protein-3 were excellent predictors for the progression of COVID-19, and the combination of the 2 cytokines showed the biggest area under the curve of the receiver-operating characteristics calculations with a value of 0.99. CONCLUSIONS: In this study, we report biomarkers that are highly associated with disease severity and progression of COVID-19. These findings add to our understanding of the immunopathologic mechanisms of severe acute respiratory syndrome coronavirus 2 infection, and provide potential therapeutic targets and strategies.

Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19

Abstract Background The inflammatory response plays a critical role in coronavirus disease 2019 (COVID-19), and inflammatory cytokine storm increases the severity of COVID-19. Objective To investigate the ability of interleukin-6 (IL-6), C-reactive protein (CRP), and procalcitonin (PCT) to predict mild and severe cases of COVID-19. Study design This retrospective cohort study included 140 patients diagnosed with COVID-19 from January 18, 2020, to March 12, 2020. The study population was divided into two groups according to disease severity: a mild group (MG) (n = 107) and a severe group (SG) (n = 33). Data on demographic characteristics, baseline clinical characteristics, and the levels of IL-6, CRP, and PCT on admission were collected. Results Among the 140 patients, the levels of IL-6, CRP, and PCT increased in 95 (67.9 %), 91 (65.0 %), and 8 (5.7 %) patients on admission, respectively. The proportion of patients with increased IL-6, CRP, and PCT levels was significantly higher in the SG than in the MG. Cox proportional hazard model showed that IL-6 and CRP could be used as independent factors to predict the severity of COVID-19. Furthermore, patients with IL-6 > 32.1 pg/mL or CRP > 41.8 mg/L were more likely to have severe complications. Conclusion The serum levels of IL-6 and CRP can effectively assess disease severity and predict outcome in patients with COVID-19.

Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19

BACKGROUND: The inflammatory response plays a critical role in coronavirus disease 2019 (COVID-19), and inflammatory cytokine storm increases the severity of COVID-19. OBJECTIVE: To investigate the ability of interleukin-6 (IL-6), C-reactive protein (CRP), and procalcitonin (PCT) to predict mild and severe cases of COVID-19. STUDY DESIGN: This retrospective cohort study included 140 patients diagnosed with COVID-19 from January 18, 2020, to March 12, 2020. The study population was divided into two groups according to disease severity: a mild group (MG) (n = 107) and a severe group (SG) (n = 33). Data on demographic characteristics, baseline clinical characteristics, and the levels of IL-6, CRP, and PCT on admission were collected. RESULTS: Among the 140 patients, the levels of IL-6, CRP, and PCT increased in 95 (67.9 %), 91 (65.0 %), and 8 (5.7 %) patients on admission, respectively. The proportion of patients with increased IL-6, CRP, and PCT levels was significantly higher in the SG than in the MG. Cox proportional hazard model showed that IL-6 and CRP could be used as independent factors to predict the severity of COVID-19. Furthermore, patients with IL-6 > 32.1 pg/mL or CRP > 41.8 mg/L were more likely to have severe complications. CONCLUSION: The serum levels of IL-6 and CRP can effectively assess disease severity and predict outcome in patients with COVID-19.

There is a high mortality and long hospitalization period for severe cases with 2019 novel coronavirus disease (COVID-19) pneumonia. Therefore, it makes sense to search for a potential biomarker that could rapidly and effectively identify severe cases early. Clinical samples from 28 cases of COVID-19 (8 severe cases, 20 mild cases) in Zunyi District from January 29, 2020 to February 21, 2020 were collected and otherwise statistically analysed for biochemical markers. Serum urea, creatinine (CREA) and cystatin C (CysC) concentrations in severe COVID-19 patients were significantly higher than those in mild COVID-19 patients (P<0.001), and there were also significant differences in serum direct bilirubin (DBIL), cholinesterase (CHE) and lactate dehydrogenase (LDH) concentrations between severe and mild COVID-19 patients (P<0.05). Serum urea, CREA, CysC, DBIL, CHE and LDH could be used to distinguish severe COVID-19 cases from mild COVID-19 cases. In particular, serum biomarkers, including urea, CREA, CysC, which reflect glomerular filtration function, may have some significance as potential indicators for the early diagnosis of severe COVID-19 and to distinguish it from mild COVID-19. Glomerular filtration function injury in severe COVID-19 patients should also be considered by clinicians.

Potential biochemical markers to identify severe cases among COVID-19 patients

Presepsin as a predictive biomarker of severity in COVID-19: A case series

Guidelines for the diagnosis and treatment of the novel coronavirus disease 2019 (COVID‐19) present clear criteria, including respiratory rate, hemoglobin oxygen saturation (SaO(2)), and oxygenation indicator (PaO(2)/FiO(2))(1). This article is protected by copyright. All rights reserved.

Presepsin as a predictive biomarker of severity in COVID‐19: a case series

Abstract Background The outbreak of Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, December 2019, and continuously poses a serious threat to public health, highlighting the urgent need of identifying biomarkers for disease severity and progression. Objective To identify biomarkers for disease severity and progression of COVID-19. Methods Forty-eight cytokines in the plasma samples from 50 COVID-19 cases including 11 critically ill, 25 severe and 14 moderate patients were measured and analyzed in combination with clinical data. Results Fourteen cytokines were found to be significantly elevated in COVID-19 cases and showed different expression profiles in patients with different disease severity. Moreover, expression levels of IP-10, MCP-3, HGF, MIG and MIP-1α, which were shown to be highly associated with disease severity during disease progression, were remarkably higher in critically ill patients, followed by severe and then the moderate patients. Serial detection of the five cytokines in 16 cases showed that continuously high levels were associated with deteriorated progression of disease and fatal outcome. Furthermore, IP-10 and MCP-3 were excellent predictors for the progression of COVID-19, and the combination of the two cytokines showed the biggest area under the curve (AUC) of the receiver-operating characteristics (ROC) calculations with a value of 0.99. Conclusion In this study, we report biomarkers that highly associated with disease severity and progression of COVID-19. These findings add to our understanding of the immunopathologic mechanisms of SARS-CoV-2 infection, and provide potential therapeutic targets and strategies.

We read the article by D'Alessandro Miriana et al. with great interest and appreciate their efforts to evaluate the role of serum Krebs von den Lungen-6 (KL-6) as a prognostic biomarker of severe coronavirus disease 2019 (COVID-19).1 This article is protected by copyright. All rights reserved.

Serum KL-6 can distinguish between different phenotypes of severe COVID-19

Background: The global pandemic caused by COVID-19 remains poorly understood by clinicians. Identifying biologic markers associated with prognosis can help clinicians recognize disease severity. Objective: To describe the association between D-dimer, CRP, IL-6, ferritin, LDH, and clinical outcomes in a cohort of COVID-19 patients treated on the inpatient medical service at a university hospital in Washington, DC. Design: In this retrospective study, we included all adults admitted to the inpatient medicine service at George Washington University Hospital between March 12, 2020 and May 9, 2020 with laboratory confirmed COVID-19. Clinical and laboratory data were extracted from electronic medical records and compared between survivors not requiring ICU transfer, survivors requiring ICU transfer, survivors requiring intubation, and non-survivors. Key Results: 299 patients were included in our study, of whom 69 required transfer to the ICU, 39 required intubation, and 71 died. Threshold values for IL-6 (>50 pg/mL), D-dimer (>3 mcg/mL), ferritin (>450 ng/mL), CRP (>100 mg/L), and LDH (1,200 u/L) were found to be statistically significant and independently associated with higher odd of clinical deterioration and death. Hypertension, CVA and heart disease independently had an increased risk of all three outcomes, while CKD had only an increased risk of death. Patient co-morbidities had no effect on the different biomarkers' significant association with poor patient clinical outcomes, except cancer. Conclusion: Laboratory markers of inflammation and coagulopathy can help clinicians identify patients who are at high risk for clinical deterioration, independent of clinically significant medical comorbidities

The Association Between Biomarkers and Clinical Outcomes in Novel Coronavirus (COVID-19) Pneumonia in a U.S. Cohort

Tumor biomarkers predict clinical outcome of COVID-19 patients

BACKGROUND: The early identification of patients at risk of clinical deterioration is of interest considering the timeline of COVID-19 after the onset of symptoms. OBJECTIVE: The aim of our study was to evaluate the usefulness of testing serum IL-6 and other serological and clinical biomarkers, to predict a short-term negative clinical course of patients with noncritical COVID-19. METHODS: A total of 208 patients with noncritical COVID-19 pneumonia at admission were consecutively enrolled. Clinical and laboratory findings obtained on admission were analyzed by using survival analysis and stepwise logistic regression for variable selection. Three-day worsening as outcome in a logistic model to generate a prognostic score was used. RESULTS: Clinical worsening occurred in 63 patients (16 = died; 39 = transferred to intensive care unit; 8 worsening of respiratory failure). Forty-five of them worsened within 3 days after admission. The risk of clinical worsening was progressively enhanced along with increasing quartiles of IL-6 levels. Multivariate analysis showed that IL-6 (P = .005), C-reactive protein (CRP) (P = .003), and SaO2/FiO2 (P = .014) were the best predictors for clinical deterioration in the first 3 days after admission. The combined score yielded an area under the curve = 0.88 (95% confidence interval: 0.83-0.93). A nomogram predicting the probability of 3-day worsening was generated. The score also showed good performance for 7-day and 14- or 21-day worsening and in predicting death occurring during all the follow-up. CONCLUSIONS: Combining IL-6, CRP, and SaO2/FiO2 in a score may help clinicians to identify on admission those patients with COVID-19 who are at high risk for a further 3-day clinical deterioration.

Prompt Predicting of Early Clinical Deterioration of Moderate-to-Severe COVID-19 Patients: Usefulness of a Combined Score Using IL-6 in a Preliminary Study

OBJECTIVE To identify the biomarkers as early warning signals for severe COVID-19. METHODS We retrospectively analyzed the clinical data of 63 patients with COVID- 19 from Hubei Provincial Hospital of Integrated Chinese and Western Medicine, including 32 moderate cases and 31 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory test results were compared between the two groups. Logistic regression analysis was performed to identify the factors that predicted the severity of COVID-19. The receiver- operating characteristic curve (ROC) of neutrophil/lymphocyte ratio (NLR) was calculated, and the area under the curve (AUC) was determined to estimate the optimal threshold of NLR for predicting severe cases of COVID-19. RESULTS The patients with moderate and server COVID-19 showed significant differences in the rate of diabetes, NLR, serum amyloid A (SSA), C-reactive protein (CRP) and serum albumin (ALB) levels (P < 0.05). The co- morbidity of diabetes, NLR, SSA and CRP were found to positively correlate and ALB to inversely correlate with the severity of COVID-19 (P < 0.05). Multivariate logistic regression analysis showed that NLR was an independent risk factor for severe COVID-19 (OR=1.264, 95% CI: 1.046-1.526, P=0.015) with an AUC of 0.831 (95% CI: 0.730-0.932), an optimal diagnostic threshold of 4.795, a sensitivity of 0.839, and a specificity of 0.750. CONCLUSIONS An increased NLR can serve as an early warning signal of severe COVID-19.

[An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19].

Background As coronavirus disease 2019 (COVID-19) pandemic rages on, there is urgent need for identification of clinical and laboratory predictors for progression towards severe and fatal forms of this illness. In this study we aimed to evaluate the discriminative ability of hematologic, biochemical and immunologic biomarkers in patients with and without the severe or fatal forms of COVID-19. Methods An electronic search in Medline (PubMed interface), Scopus, Web of Science and China National Knowledge Infrastructure (CNKI) was performed, to identify studies reporting on laboratory abnormalities in patients with COVID-19. Studies were divided into two separate cohorts for analysis: severity (severe vs. non-severe and mortality, i.e. non-survivors vs. survivors). Data was pooled into a meta-analysis to estimate weighted mean difference (WMD) with 95% confidence interval (95% CI) for each laboratory parameter. Results A total number of 21 studies was included, totaling 3377 patients and 33 laboratory parameters. While 18 studies (n = 2984) compared laboratory findings between patients with severe and non-severe COVID-19, the other three (n = 393) compared survivors and non-survivors of the disease and were thus analyzed separately. Patients with severe and fatal disease had significantly increased white blood cell (WBC) count, and decreased lymphocyte and platelet counts compared to non-severe disease and survivors. Biomarkers of inflammation, cardiac and muscle injury, liver and kidney function and coagulation measures were also significantly elevated in patients with both severe and fatal COVID-19. Interleukins 6 (IL-6) and 10 (IL-10) and serum ferritin were strong discriminators for severe disease. Conclusions Several biomarkers which may potentially aid in risk stratification models for predicting severe and fatal COVID-19 were identified. In hospitalized patients with respiratory distress, we recommend clinicians closely monitor WBC count, lymphocyte count, platelet count, IL-6 and serum ferritin as markers for potential progression to critical illness.

Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis

This study aims to investigate blood and biochemical laboratory findings in patients with severe Corona Virus Disease 2019 (COVID-19) and to develop a joint predictor for predicting the likelihood of severe COVID-19 and its adverse clinical outcomes, to provide more information for treatment. We collected the data of 88 patients with laboratory-confirmed COVID-19. Then patients were divided into a non-severe group and a critical group (including critically ill cases). Univariate analysis showed that the absolute lymphocyte count, albumin level, albumin/globulin (A/G) ratio, lactate dehydrogenase (LDH) level, interleukin-6 (IL-6) level, erythrocyte count, globulin level, blood glucose level, and age were significantly correlated with the severity of COVID-19. The multivariate binary logistic regression model revealed that Age, absolute lymphocyte count, and IL-6 level were independent risk factors in patients with COVID-19. The receiver operating characteristic (ROC) curve revealed that the combination of IL-6 level, absolute lymphocyte count and age is superior to a single factor as predictors for predicting severe COVID-19, regardless of whether it is the area under curve (AUC) or the prediction sensitivity and specificity. Early application is beneficial to early identification of critically ill patients and timing individual treatments to reduce mortality.

Prediction of the severity of Corona Virus Disease 2019 and its adverse clinical outcomes.

OBJECTIVE: To identify the biomarkers as early warning signals for severe COVID-19. METHODS: We retrospectively analyzed the clinical data of 63 patients with COVID- 19 from Hubei Provincial Hospital of Integrated Chinese and Western Medicine, including 32 moderate cases and 31 severe cases. The demographic data, underlying diseases, clinical manifestations and laboratory test results were compared between the two groups. Logistic regression analysis was performed to identify the factors that predicted the severity of COVID-19. The receiver- operating characteristic curve (ROC) of neutrophil/lymphocyte ratio (NLR) was calculated, and the area under the curve (AUC) was determined to estimate the optimal threshold of NLR for predicting severe cases of COVID-19. RESULTS: The patients with moderate and server COVID-19 showed significant differences in the rate of diabetes, NLR, serum amyloid A (SSA), C-reactive protein (CRP) and serum albumin (ALB) levels (P < 0.05). The co- morbidity of diabetes, NLR, SSA and CRP were found to positively correlate and ALB to inversely correlate with the severity of COVID-19 (P < 0.05). Multivariate logistic regression analysis showed that NLR was an independent risk factor for severe COVID-19 (OR=1.264, 95% CI: 1.046-1.526, P=0.015) with an AUC of 0.831 (95% CI: 0.730-0.932), an optimal diagnostic threshold of 4.795, a sensitivity of 0.839, and a specificity of 0.750. CONCLUSIONS: An increased NLR can serve as an early warning signal of severe COVID-19.

[An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19]

Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis.

TRIAL REGISTRATION: ChiCTR, ChiCTR2000029758. Registered 12 February 2020 - Retrospectively registered.

Neutrophil-to-lymphocyte ratio as a predictive biomarker for moderate-severe ARDS in severe COVID-19 patients

Prediction of the severity of Corona Virus Disease 2019 and its adverse clinical outcomes

The novel coronavirus infection has spread worldwide, causing a wide spectrum of clinical manifestations. Most patients develop moderate clinical illness, but a substantial number will experience severe pneumonia, which may rapidly progress to acute respiratory distress syndrome and multiple organ failure. In this population, soluble urokinase plasminogen activator receptor (suPAR) could serve as a quick triage test and independent marker of clinical severity, hospital and intensive care unit admission, complications, and mortality.

Soluble Urokinase Plasminogen Activator Receptor: A Biomarker for Predicting Complications and Critical Care Admission of COVID-19 Patients

To study the relationship between clinical indexes and the severity of coronavirus disease 2019 (COVID‐19), and to explore its role in predicting the severity of COVID‐19. Clinical data of 443 patients with COVID‐19 admitted to our hospital were retrospectively analyzed, which were divided into nonsevere group (n = 304) and severe group (n = 139) according to their condition. Clinical indicators were compared between different groups. The differences in sex, age, the proportion of patients with combined heart disease, leukocyte, neutrophil‐to‐lymphocyte ratio (NLR), neutrophil, lymphocyte, platelet, D‐dimer, C‐reactive protein (CRP), procalcitonin, lactate dehydrogenase, and albumin on admission between the two groups were statistically significant (P < .05). Multivariate logistic regression analysis showed NLR and CRP were independent risk factors for severe COVID‐19. Platelets were independent protective factors for severe COVID‐19. The receiver operating characteristic (ROC) curve analysis demonstrated area under the curve of NLR, platelet, CRP, and combination was 0.737, 0.634, 0.734, and 0.774, respectively. NLR, CRP, and platelets can effectively assess the severity of COVID‐19, among which NLR is the best predictor of severe COVID‐19, and the combination of three clinical indicators can further predict severe COVID‐19.

The value of clinical parameters in predicting the severity of COVID‐19

We read the article by D'Alessandro Miriana et al. with great interest and appreciate their efforts to evaluate the role of serum Krebs von den Lungen‐6 (KL‐6) as a prognostic biomarker of severe coronavirus disease 2019 (COVID‐19).(1) This article is protected by copyright. All rights reserved.

Serum KL‐6 can distinguish between different phenotypes of severe COVID‐19

To study the relationship between clinical indexes and the severity of coronavirus disease 2019 (COVID-19), and to explore its role in predicting the severity of COVID-19. Clinical data of 443 patients with COVID-19 admitted to our hospital were retrospectively analyzed, which were divided into nonsevere group (n = 304) and severe group (n = 139) according to their condition. Clinical indicators were compared between different groups. The differences in sex, age, the proportion of patients with combined heart disease, leukocyte, neutrophil-to-lymphocyte ratio (NLR), neutrophil, lymphocyte, platelet, D-dimer, C-reactive protein (CRP), procalcitonin, lactate dehydrogenase, and albumin on admission between the two groups were statistically significant (P < .05). Multivariate logistic regression analysis showed NLR and CRP were independent risk factors for severe COVID-19. Platelets were independent protective factors for severe COVID-19. The receiver operating characteristic (ROC) curve analysis demonstrated area under the curve of NLR, platelet, CRP, and combination was 0.737, 0.634, 0.734, and 0.774, respectively. NLR, CRP, and platelets can effectively assess the severity of COVID-19, among which NLR is the best predictor of severe COVID-19, and the combination of three clinical indicators can further predict severe COVID-19.

The value of clinical parameters in predicting the severity of COVID-19

The sudden increase of COVID-19 cases is putting a high pressure on healthcare services worldwide. At the current stage, fast, accurate and early clinical assessment of the disease severity is vital. To support decision making and logistical planning in healthcare systems, this study leverages a database of blood samples from 404 infected patients in the region of Wuhan, China to identify crucial predictive biomarkers of disease severity. For this purpose, machine learning tools selected three biomarkers that predict the survival of individual patients with more than 90% accuracy: lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP). In particular, relatively high levels of LDH alone seem to play a crucial role in distinguishing the vast majority of cases that require immediate medical attention. This finding is consistent with current medical knowledge that high LDH levels are associated with tissue breakdown occurring in various diseases, including pulmonary disorders such as pneumonia. Overall, this paper suggests a simple and operable formula to quickly predict patients at the highest risk, allowing them to be prioritised and potentially reducing the mortality rate.

A machine learning-based model for survival prediction in patients with severe COVID-19 infection

Effective laboratory markers for the estimation of disease severity and predicting the clinical progression of coronavirus disease-2019 (COVID-19) is urgently needed. Laboratory tests, including blood routine, cytokine profiles and infection markers, were collected from 389 confirmed COVID-19 patients. The included patients were classified into mild (n = 168), severe (n = 169) and critical groups (n = 52). The leukocytes, neutrophils, infection biomarkers [such as C-reactive protein (CRP), procalcitonin (PCT) and ferritin] and the concentrations of cytokines [interleukin (IL)-2R, IL-6, IL-8, IL-10 and tumor necrosis factor (TNF)-α] were significantly increased, while lymphocytes were significantly decreased with increased severity of illness. The amount of IL-2R was positively correlated with the other cytokines and negatively correlated with lymphocyte number. The ratio of IL-2R to lymphocytes was found to be remarkably increased in severe and critical patients. IL-2R/lymphocytes were superior compared with other markers for the identification of COVID-19 with critical illness, not only from mild but also from severe illness. Moreover, the cytokine profiles and IL-2R/lymphocytes were significantly decreased in recovered patients, but further increased in disease-deteriorated patients, which might be correlated with the outcome of COVID-19. Lymphopenia and increased levels of cytokines were closely associated with disease severity. The IL-2R/lymphocyte was a prominent biomarker for early identification of severe COVID-19 and predicting the clinical progression of the disease.

Using IL-2R/lymphocytes for predicting the clinical progression of patients with COVID-19

OBJECTIVE: Most coronavirus disease 2019 (COVID-19) cases were identified as moderate, which is defined as having a fever or dry cough and lung imaging with ground-glass opacities. The risk factors and predictors of prognosis in such cohorts remain uncertain. METHODS: All adult patients with COVID-19 of moderate severity diagnosed using qRT-PCR and hospitalized at the Central Hospital of Wuhan, China, from Jan 1 to Mar 20, 2020 were enrolled in this retrospective study. The main outcomes were progression from moderate to severe or critical condition or death. RESULTS: Among the 456 enrolled patients with moderate COVID-19, 251/456 (55.0%) had poor prognosis. Multivariate logistic regression analysis identified higher NLR on admission (OR =1.032, 95%CI 1.042-1.230, P = 0.004) and higher CRP on admission (OR =3.017, 95%CI 1.941-4.690, P < 0.001) were associated with increased odds ratios of poor prognosis. The area under the receiver operating characteristic (ROC) curve (AUC) for NLR and CRP in predicting progression to critical condition was 0.77 (95% CI 0.694-0.846, P < 0.001) and 0.84 (95% CI 0.780-0.905, P < 0.001), with a cut-off value of 2.79 and 25.95 mg/l, respectively. The AUC of NLR and CRP in predicting death was 0.81 (95% CI, 0.732-0.878, P < 0.001) and 0.89 (95% CI 0.825-0.946, P < 0.001), with a cut-off value of 3.19 and 33.4 mg/l, respectively. CONCLUSIONS: Higher levels of NLR and CRP at admission were associated with poor prognosis of moderate COVID-19 patients. NLR and CRP were good predictors of progression to critical condition and death.

Predictors of progression from moderate to severe COVID-19: a retrospective cohort

Abstract Background The early identification of patients at risk of clinical deterioration is of interest considering the timeline of COVID-19 after the onset of symptoms. Objective The aim of our study was to evaluate the usefulness of testing serum IL-6 and other serological and clinical biomarkers, to predict a short-term negative clinical course of non critical COVID-19 patients. Methods 208 patients with non critical COVID-19 pneumonia at admission were consecutively enrolled. Clinical and laboratory findings obtained upon admission were analyzed by using survival analysis and stepwise logistic regression for variable selection. Three-day worsening as outcome in a logistic model to generate a prognostic score was used. Results Clinical worsening occurred in 63 patients (16=died; 39=transferred to Intensive Care Unit; 8 worsening of respiratory failure). Forty-five of them worsened within 3 days after admission. The risk of clinical worsening was progressively enhanced along with increasing quartiles of IL-6 levels. Multivariate analysis showed that IL-6 (p=0.005), CRP (p=0.003) and SaO2/FiO2 (p=0.014) and were the best predictors for clinical deterioration in the first 3 days after admission. The combined score yielded an AUC=0.88 (95% CI 0.83–0.93). A nomogram predicting the probability of 3-day worsening was generated. The score also showed good performance for 7-day and 14-day or 21-day worsening and in predicting death occurring during all the follow-up. Conclusions Combining IL-6, CRP and SaO2/FiO2 in a score, may help clinicians to identify upon admission those patients with COVID-19 who are at high risk for a further 3-day clinical deterioration.

Prompt predicting of early clinical deterioration of moderate-to-severe COVID-19 patients: usefulness of a combined score using IL-6 in a preliminary study

Context: A relevant portion of COVID-19 patients develop severe disease with negative outcomes. Several biomarkers have been proposed to predict COVID-19 severity, but no definite interpretative criteria have been established to date for stratifying risk. Objective: To evaluate six serum biomarkers (C-reactive protein, lactate dehydrogenase, D-dimer, albumin, ferritin and cardiac troponin T) for predicting COVID-19 severity and to define related cut-offs able to aid clinicians in risk stratification of hospitalized patients. Design: A retrospective study of 427 COVID-19 patients was performed. Patients were divided into groups based on their clinical outcome: non-survivors vs. survivors and patients admitted to intensive care unit vs. others. ROC curves and likelihood ratios were employed to define predictive cut-offs for evaluated markers. Results: Marker concentrations at peak were significantly different between groups for both selected outcomes. At univariate logistic regression analysis, all parameters were significantly associated with higher odds of death and intensive care. At the multivariate analysis, high concentrations of lactate dehydrogenase and low concentrations of albumin in serum remained significantly associated with higher odds of death, while only low lactate dehydrogenase activities remained associated with lower odds of intensive care admission. The best cut-offs for death prediction were >731 U/L for lactate dehydrogenase and ≤18 g/L for albumin, while a lactate dehydrogenase activity <425 U/L was associated with a negative likelihood ratio of 0.10 for intensive treatment. Conclusions: Our study identifies which biochemistry tests represent major predictors of COVID-19 severity and defines the best cut-offs for their use.

A comprehensive appraisal of laboratory biochemistry tests as major predictors of COVID-19 severity

The novel coronavirus (2019-nCoV) infection caused pneumonia. we retrospectively analyzed the virus presence in the pharyngeal swab, blood, and the anal swab detected by real-time PCR in the clinical lab. Unexpectedly, the 2109-nCoV RNA was readily detected in the blood (6 of 57 patients) and the anal swabs (11 of 28 patients). Importantly, all of the 6 patients with detectable viral RNA in the blood cohort progressed to severe symptom stage, indicating a strong correlation of serum viral RNA with the disease severity (p-value = 0.0001). Meanwhile, 8 of the 11 patients with annal swab virus-positive was in severe clinical stage. However, the concentration of viral RNA in the anal swab (Ct value = 24 + 39) was higher than in the blood (Ct value = 34 + 39) from patient 2, suggesting that the virus might replicate in the digestive tract. Altogether, our results confirmed the presence of virus RNA in extra-pulmonary sites.

Detectable 2019-nCoV viral RNA in blood is a strong indicator for the further clinical severity

Background: The coronavirus disease 2019 (COVID-19) is spreading worldwide with 16,558 deaths till date. Serum albumin, high-density lipoprotein (HDL-C), and C-reactive protein have been known to be associated with the severity and mortality of community-acquired pneumonia. However, the characteristics and role of metabolic and inflammatory indicators in COVID-19 is unclear. Methods: We included 97 hospitalized patients with laboratory-confirmed COVID-19. Epidemiological, clinical, and laboratory indices; radiological features; and treatment were analysed. The differences in the clinical and laboratory parameters between mild and severe COVID-19 patients and the role of these indicators in severity prediction of COVID-19 were investigated. Results: All were Wuhan residents with contact with confirmed COVID-19 cases. The median age was 39 years (IQR: 30-59). The most common presenting symptoms were fever (58.8%), cough (55.7%), and fatigue (33%). Other features were lymphopenia, impaired fasting glucose, hypoproteinaemia, hypoalbuminemia, low high-density lipoproteinemia. Decrease in lymphocyte count, serum total protein, serum albumin, high-density lipoprotein cholesterol (HDL-C), ApoA1, CD3+T%, and CD8+T% were found to be valuable in predicting the transition of COVID-19 from mild to severe illness. Chest computed tomography (CT) images showed that the absorption of bilateral lung lesions synchronized with the recovery of metabolic and inflammatory indicators. Conclusions: Hypoproteinaemia, hypoalbuminemia, low high-density lipoproteinemia, and decreased ApoA1, CD3+T%, and CD8+T% could predict severity of COVID-19. Lymphocyte count, total serum protein, and HDL-C may be potentially useful for the evaluation of COVID-19.

Metabolic disturbances and inflammatory dysfunction predict severity of coronavirus disease 2019 (COVID-19): a retrospective study

A comprehensive appraisal of laboratory biochemistry tests as major predictors of COVID-19 severity.

Objective: To analyze the clinical characteristics of 2019 novel coronavirus (2019-nCoV) pneumonia and to investigate the correlation between serum inflammatory cytokines and severity of the disease. Methods: 29 patients with 2019-ncov admitted to the isolation ward of Tongji hospital affiliated to Tongji medical college of Huazhong University of Science and Technology in January 2020 were selected as the study subjects. Clinical data were collected and the general information, clinical symptoms, blood test and CT imaging characteristics were analyzed. According to the relevant diagnostic criteria, the patients were divided into three groups: mild (15 cases), severe (9 cases) and critical (5 cases). The expression levels of inflammatory cytokines and other markers in the serum of each group were detected, and the changes of these indicators of the three groups were compared and analyzed, as well as their relationship with the clinical classification of the disease. Results: (1) The main symptoms of 2019-nCoV pneumonia was fever (28/29) with or without respiratory and other systemic symptoms. Two patients died with underlying disease and co-bacterial infection, respectively. (2) The blood test of the patients showed normal or decreased white blood cell count (23/29), decreased lymphocyte count (20/29), increased hypersensitive C reactive protein (hs-CRP) (27/29), and normal procalcitonin. In most patients,serum lactate dehydrogenase (LDH) was significantly increased (20/29), while albumin was decreased(15/29). Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbil), serum creatinine (Scr) and other items showed no significant changes. (3) CT findings of typical cases were single or multiple patchy ground glass shadows accompanied by septal thickening. When the disease progresses, the lesion increases and the scope expands, and the ground glass shadow coexists with the solid shadow or the stripe shadow. (4) There were statistically significant differences in the expression levels of interleukin-2 receptor (IL-2R) and IL-6 in the serum of the three groups (P<0.05), among which the critical group was higher than the severe group and the severe group was higher than the mildgroup. However, there were no statistically significant differences in serum levels of tumor necrosis factor-alpha (TNF-α), IL-1, IL-8, IL-10, hs-CRP, lymphocyte count and LDH among the three groups (P>0.05). Conclusion: The clinical characteristics of 2019-nCoV pneumonia are similar to those of common viral pneumonia. High resolution CT is of great value in the differential diagnosis of this disease. The increased expression of IL-2R and IL-6 in serum is expected to predict the severity of the 2019-nCoV pneumonia and the prognosis of patients.

[Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia].

Objective: To analyze the clinical characteristics of 2019 novel coronavirus (2019-nCoV) pneumonia and to investigate the correlation between serum inflammatory cytokines and severity of the disease. Methods: 29 patients with 2019-ncov admitted to the isolation ward of Tongji hospital affiliated to Tongji medical college of Huazhong University of Science and Technology in January 2020 were selected as the study subjects. Clinical data were collected and the general information, clinical symptoms, blood test and CT imaging characteristics were analyzed. According to the relevant diagnostic criteria, the patients were divided into three groups: mild (15 cases), severe (9 cases) and critical (5 cases). The expression levels of inflammatory cytokines and other markers in the serum of each group were detected, and the changes of these indicators of the three groups were compared and analyzed, as well as their relationship with the clinical classification of the disease. Results: (1) The main symptoms of 2019-nCoV pneumonia was fever (28/29) with or without respiratory and other systemic symptoms. Two patients died with underlying disease and co-bacterial infection, respectively. (2) The blood test of the patients showed normal or decreased white blood cell count (23/29), decreased lymphocyte count (20/29), increased hypersensitive C reactive protein (hs-CRP) (27/29), and normal procalcitonin. In most patients, serum lactate dehydrogenase (LDH) was significantly increased (20/29), while albumin was decreased (15/29). Alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (Tbil), serum creatinine (Scr) and other items showed no significant changes. (3) CT findings of typical cases were single or multiple patchy ground glass shadows accompanied by septal thickening. When the disease progresses, the lesion increases and the scope expands, and the ground glass shadow coexists with the solid shadow or the stripe shadow. (4) There were statistically significant differences in the expression levels of interleukin-2 receptor (IL-2R) and IL-6 in the serum of the three groups (P<0.05), among which the critical group was higher than the severe group and the severe group was higher than the mild group. However, there were no statistically significant differences in serum levels of tumor necrosis factor-alpha (TNF-α), IL-1, IL-8, IL-10, hs-CRP, lymphocyte count and LDH among the three groups (P>0.05). Conclusion: The clinical characteristics of 2019-nCoV pneumonia are similar to those of common viral pneumonia. High resolution CT is of great value in the differential diagnosis of this disease. The increased expression of IL-2R and IL-6 in serum is expected to predict the severity of the 2019-nCoV pneumonia and the prognosis of patients.

ChiCTR, ChiCTR2000029758. Registered 12 February 2020 - Retrospectively registered

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a respiratory disease, which can evolve into multi-organ failure (MOF), leading to death. Several biochemical alterations have been described in COVID-19 patients. To date, many biomarkers reflecting the main pathophysiological characteristics of the disease have been identified and associated with the risk of developing severe disease. Lymphopenia represents the hallmark of the disease, and it can be detected since the early stage of infection. Increased levels of several inflammatory biomarkers, including c-reactive protein, have been found in COVID-19 patients and associated with an increased risk of severe disease, which is characterised by the so-called "cytokine storm". Also, the increase of cardiac and liver dysfunction biomarkers has been associated with poor outcome. In this review, we provide an overview of the main biochemical characteristics of COVID-19 and the associated biomarkers alterations.

Biochemical biomarkers alterations in Coronavirus Disease 2019 (COVID-19).

Biochemical biomarkers alterations in Coronavirus Disease 2019 (COVID-19)

The novel avian origin influenza A (H7N9) virus has caused severe diseases in humans in eastern China since the spring of 2013. Fatal outcomes of H7N9 infections are often attributed to the severe pneumonia and acute respiratory distress syndrome (ARDS). There is urgent need to discover biomarkers predicting the progression of disease and fatal outcome of potentially lethal flu infections, based on sound statistical analysis. We discovered that 34 of the 48 cytokines and chemokines examined in this study were significantly elevated in the plasma samples from patients infected with H7N9. We report for the first time that the levels of MIF, SCF, MCP-1, HGF, and SCGF-β are highly positively linked to disease severity and the profile of mediators MIF, SCF, MCP-1, HGF, SCGF-β, IP-10, IL-18, and IFN-γ is an independent outcome predictor.

The Serum Profile of Hypercytokinemia Factors Identified in H7N9-Infected Patients can Predict Fatal Outcomes

Elevated interleukin-6, interleukin-10 and neutrophil : lymphocyte ratio as identifiers of severe coronavirus disease 2019

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a worldwide health threat. Here, we report that low plasma interleukin-3 (IL-3) levels were associated with increased severity and mortality during SARS-CoV-2 infections. IL-3 promoted the recruitment of antiviral circulating plasmacytoid dendritic cells (pDCs) into the airways by stimulating CXCL12 secretion from pulmonary CD123+ epithelial cells. This study identifies IL-3 as a predictive disease marker and potential therapeutic target for SARS-CoV-2 infections.

Interleukin-3 is a predictive marker for severity and outcome during SARS-CoV-2 infections

Effective laboratory markers for the estimation of disease severity and predicting the clinical progression of coronavirus disease‐2019 (COVID‐19) is urgently needed. Laboratory tests, including blood routine, cytokine profiles and infection markers, were collected from 389 confirmed COVID‐19 patients. The included patients were classified into mild (n = 168), severe (n = 169) and critical groups (n = 52). The leukocytes, neutrophils, infection biomarkers [such as C‐reactive protein (CRP), procalcitonin (PCT) and ferritin] and the concentrations of cytokines [interleukin (IL)‐2R, IL‐6, IL‐8, IL‐10 and tumor necrosis factor (TNF)‐α] were significantly increased, while lymphocytes were significantly decreased with increased severity of illness. The amount of IL‐2R was positively correlated with the other cytokines and negatively correlated with lymphocyte number. The ratio of IL‐2R to lymphocytes was found to be remarkably increased in severe and critical patients. IL‐2R/lymphocytes were superior compared with other markers for the identification of COVID‐19 with critical illness, not only from mild but also from severe illness. Moreover, the cytokine profiles and IL‐2R/lymphocytes were significantly decreased in recovered patients, but further increased in disease‐deteriorated patients, which might be correlated with the outcome of COVID‐19. Lymphopenia and increased levels of cytokines were closely associated with disease severity. The IL‐2R/lymphocyte was a prominent biomarker for early identification of severe COVID‐19 and predicting the clinical progression of the disease.

Using IL‐2R/lymphocytes for predicting the clinical progression of patients with COVID‐19

Abstract Objective To explore the clinical value of immune-inflammatory markers to assess the severity of coronavirus disease 2019 (COVID-19). Methods 127 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study, and classified into non-severe and severe groups. Demographics, symptoms, underlying diseases and laboratory data were collected and assessed for predictive value. Results Of 127 COVID-19 patients, 16 cases (12.60%) were classified into the severe group. High level of interleukin-6 (IL-6), C-reaction protein (CRP) and hypertension were independent risk factors for the severity of COVID-19. The risk model based on IL-6, CRP and hypertension had the highest area under the receiver operator characteristic curve (AUROC). Additionally, the baseline IL-6 was positively correlated with other immune-inflammatory parameters and the dynamic change of IL-6 in the severe cases were parallel to the amelioration of the disease. Conclusion Our study showed that high level of IL-6, CRP and hypertension were independent risk factors for assessing the severity of COVID-19. The risk model established upon IL-6, CRP and hypertension had the highest predictability in this study. Besides, IL-6 played a pivotal role in the severity of COVID-19 and had a potential value for monitoring the process of severe cases.

Clinical value of immune-inflammatory parameters to assess the severity of coronavirus disease 2019

Background: Identification of healthy people at high risk for severe COVID-19 is a global health priority. We investigated whether blood biomarkers measured by high-throughput metabolomics could be predictive of severe pneumonia and COVID-19 hospitalisation years after the blood sampling. Methods: Nuclear magnetic resonance metabolomics was used to quantify a comprehensive biomarker profile in 105,146 plasma samples collected in the UK Biobank during 2007-2010 (age range 39-70). The biomarkers were tested for association with severe pneumonia (2507 cases, defined as diagnosis in hospital or death record occurring during a median of 8.1-year follow-up) and with severe COVID-19 (195 cases, defined as diagnosis in hospital between mid-March to mid-June 2020). A multi-biomarker score was derived for prediction of severe pneumonia based on half of the study population and validated in the other half. We explored how this biomarker score relates to the risk of severe COVID-19. Findings: The biomarker associations with risk of severe COVID-19 followed an overall pattern similar to associations with risk of severe pneumonia (correlation 0.83). The multi-biomarker score, comprised of 25 blood biomarkers including inflammatory proteins, fatty acids, amino acids and advanced lipid measures, was strongly associated with risk of severe pneumonia (odds ratio 1.67 per standard deviation [95% confidence interval 1.59-1.76]; 3.8-fold risk increase for individuals in upper vs lower quintile). The multi-biomarker score was also associated with risk of severe COVID-19 (odds ratio 1.33 [1.17-1.53]; 2.5-fold risk for upper vs lower quintile) and remained significant when adjusting for body mass index, smoking, and existing respiratory and cardiometabolic diseases. Mimicking the decade lag from blood sampling to COVID-19, severe pneumonia events occurring after 7-11 years associated with the multi-biomarker score to a similar magnitude (odds ratio 1.43 [1.29-1.59]; 2.6-fold risk for upper vs lower quintile) as for severe COVID-19. However, the short-term risk of severe pneumonia events associated to the multi-biomarker score at even 3 times higher magnitude (odds ratio 2.21 [1.95-2.50]; 8.0-fold risk for upper vs lower quintile in analysis of the first 2 years after blood sampling). Interpretation: In decade-old blood samples from the UK Biobank, a biomarker score measured by high-throughput metabolomics is indicative of the risk for severe COVID-19. The molecular signature of biomarker changes reflective of risk for severe COVID-19 is similar to that for severe pneumonia, in particular when accounting for the time lag to the COVID-19 pandemic. The even stronger association of the biomarker score with 2-year risk for severe pneumonia lends support to promising screening possibilities for identifying people at high risk for severe COVID-19.

Blood biomarker score identifies individuals at high risk for severe COVID-19 a decade prior to diagnosis: metabolic profiling of 105,000 adults in the UK Biobank

OBJECTIVE: To explore the clinical value of immune-inflammatory markers to assess the severity of coronavirus disease 2019 (COVID-19). METHODS: 127 consecutive hospitalized patients with confirmed COVID-19 were enrolled in this study, and classified into non-severe and severe groups. Demographics, symptoms, underlying diseases and laboratory data were collected and assessed for predictive value. RESULTS: Of 127 COVID-19 patients, 16 cases (12.60%) were classified into the severe group. High level of interleukin-6 (IL-6), C-reaction protein (CRP) and hypertension were independent risk factors for the severity of COVID-19. The risk model based on IL-6, CRP and hypertension had the highest area under the receiver operator characteristic curve (AUROC). Additionally, the baseline IL-6 was positively correlated with other immune-inflammatory parameters and the dynamic change of IL-6 in the severe cases were parallel to the amelioration of the disease. CONCLUSION: Our study showed that high level of IL-6, CRP and hypertension were independent risk factors for assessing the severity of COVID-19. The risk model established upon IL-6, CRP and hypertension had the highest predictability in this study. Besides, IL-6 played a pivotal role in the severity of COVID-19 and had a potential value for monitoring the process of severe cases.

Abstract Background The severity and outcome of COVID-19 cases has been associated with percentage of circulating lymphocytes (LYM%), levels of C-reactive protein (CRP), interleukin 6 (IL-6), procalcitonin (PCT), lactic acid (LA) and viral load (ORF1ab Ct). However, the predictive power of each of these indicators in disease classification and prognosis remains largely unclear. Methods we retrospectively collected information on the above parameters in 142 patients with COVID-19, stratifying them by survival or disease severity. Findings CRP, PCT, IL-6, LYM% and ORF1ab Ct were significantly altered between survivors and non-survivors. LYM%, CRP and IL-6 were the most sensitive and reliable factors in distinguishing between survivors and non-survivors. These indicators were significantly different between critically ill and severe/moderate patients. Only LYM% levels were significantly different between severe and moderate types. Among all the investigated indicators, LYM% was the most sensitive and reliable in discriminating between critically ill, severe and moderate types, and between survivors and non-survivors. Conclusions CRP, PCT, IL-6, LYM% and ORF1ab Ct, but not LA, could predict prognosis and guide classification of COVID-19 patients. LYM% was the most sensitive and reliable predictor for disease typing and prognosis. We recommend that LYM% be further investigated in the management of COVID-19.

Validation of predictors of disease severity and outcomes in COVID-19 patients: a descriptive and retrospective study

The COVID-19 pandemic caused by infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to more than 100,000 deaths in the United States. Several studies have revealed that the hyper-inflammatory response induced by SARS-CoV-2 is a major cause of disease severity and death in infected patients. However, predictive biomarkers of pathogenic inflammation to help guide targetable immune pathways are critically lacking. We implemented a rapid multiplex cytokine assay to measure serum IL-6, IL-8, TNF-, and IL-1{beta} in hospitalized COVID-19 patients upon admission to the Mount Sinai Health System in New York. Patients (n=1484) were followed up to 41 days (median 8 days) and clinical information, laboratory test results and patient outcomes were collected. In 244 patients, cytokine measurements were repeated over time, and effect of drugs could be assessed. Kaplan-Meier methods were used to compare survival by cytokine strata, followed by Cox regression models to evaluate the independent predictive value of baseline cytokines. We found that high serum IL-6, IL-8, and TNF- levels at the time of hospitalization were strong and independent predictors of patient survival. Importantly, when adjusting for disease severity score, common laboratory inflammation markers, hypoxia and other vitals, demographics, and a range of comorbidities, IL-6 and TNF- serum levels remained independent and significant predictors of disease severity and death. We propose that serum IL-6 and TNF- levels should be considered in the management and treatment of COVID-19 patients to stratify prospective clinical trials, guide resource allocation and inform therapeutic options. We also propose that patients with high IL-6 and TNF- levels should be assessed for combinatorial blockade of pathogenic inflammation in this disease.

An inflammatory cytokine signature helps predict COVID-19 severity and death

[Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia]

Urinalysis parameters for predicting severity in coronavirus disease 2019 (COVID-19).

Coronavirus disease 2019 (COVID19) is a life-threatening infection with uncertain progression and outcome. Assessing the severity of the disease for worsening patients is of importance in making decisions related to supportive mechanical ventilation and aggressive treatments. This was a prospective, non-randomized study that included hospitalized patients diagnosed with COVID19. Pro-inflammatory cytokines were assessed during hospitalization, and we calculated a prediction paradigm for 30-day mortality based on the serum levels of interleukin1β (IL1β), interleukin6 (IL6), interleukin8 (IL8), and tumor necrosis factor alpha (TNFα) measured by next-generation ELISA. Data of 71 COVID19 patients, mean age 62 years, SD13.8, 50 males, 21 females, were analyzed. Twelve (16.9%) patients died within 7–39 days of their first COVID19 positive nasopharyngeal test. Levels of IL6 and TNFα were significantly higher in patients that did not survive. IL6 predicted mortality at the cut-off value of 163.4 pg/ml, with a sensitivity of 91.7% and specificity of 57.6%. Our findings demonstrate that IL6 expression is significant for the prediction of 30-day mortality in hospitalized COVID19 patients and, therefore, may assist in treatment decisions.

Cytokine prediction of mortality in COVID19 patients

AIMS: As of the 28th April 2020, the COVID-19 pandemic has infiltrated over 200 countries and affected over three million confirmed people. We review different biomarkers to evaluate if they are able to predict clinical outcomes and correlate with the severity of COVID-19 disease. METHODS: A systematic review of the literature was carried out to identify relevant articles using six different databases. Keywords to refine the search included 'COVID-19', 'SARS-CoV2', 'Biomarkers', among others. Only studies which reported data on pre-defined outcomes were included. KEY FINDINGS: Thirty-four relevant articles were identified which reviewed the following biomarkers: C-reactive protein, serum amyloid A, interleukin-6, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, D-dimer, cardiac troponin, renal biomarkers, lymphocytes and platelet count. Of these, all but two, showed significantly higher levels in patients with severe complications of COVID-19 infection compared to their non-severe counterparts. Lymphocytes and platelet count showed significantly lower levels in severe patients compared to non-severe patients. SIGNIFICANCE: Although research is still in its early stages, the discovery of how different biomarkers behave during the course of the disease could help clinicians in identifying severe disease earlier and subsequently improve prognosis. Nevertheless, we urge for more research across the globe to corroborate these findings.

The role of biomarkers in diagnosis of COVID-19 - A systematic review

OBJECTIVES: The study was aimed at investigating the characteristics of peripheral blood lymphocyte subsets and serum cytokines in children with 2019 novel coronavirus (2019-nCoV) pneumonia. METHODS: Children with 2019-nCoV pneumonia or with respiratory syncytial virus (RSV) pneumonia were included. Data including lymphocyte subsets and serum cytokines were collected and analyzed. RESULTS: 56 patients were included in the study, 40 children with 2019-nCoV pneumonia and 16 children with RSV pneumonia. Compared with children with RSV pneumonia, patients with 2019-nCoV pneumonia had higher count of CD3+8+ lymphocyte, higher percentages of CD3+, CD3+8+ lymphocytes and a lower percentage of CD19+ lymphocyte. The serum IL-10 level was significantly higher in children with RSV pneumonia. One 2019-nCoV pneumonia child who was with an obvious increase of IL-10 developed severe pneumonia. CONCLUSIONS: Immune response played a very important role in the development of 2019-nCoV pneumonia. The effective CD8+ T cell response might influence the severity of 2019-nCoV pneumonia. The adaptable change in IL-10 level might contribute to the relatively mild pneumonia symptoms in children with 2019-nCoV pneumonia and bacterial co-infection might be a risk factor of severe 2019-nCoV pneumonia.

The profile of peripheral blood lymphocyte subsets and serum cytokines in children with 2019 novel coronavirus pneumonia

BACKGROUND: Patients critically ill with coronavirus disease-2019 (COVID-19) feature hyperinflammation, and the associated biomarkers may be beneficial for risk stratification. We aimed to investigate the association between several biomarkers, including serum C-reactive protein (CRP), procalcitonin (PCT), D-dimer, and serum ferritin, and COVID-19 severity. METHODS: We performed a comprehensive systematic literature search through electronic databases. The outcome of interest for this study was the composite poor outcome, which comprises mortality, acute respiratory distress syndrome, need for care in an intensive care unit, and severe COVID-19. RESULTS: A total of 5350 patients were pooled from 25 studies. Elevated CRP was associated with an increased composite poor outcome [risk ratio (RR) 1.84 (1.45, 2.33), p < 0.001; I2: 96%] and its severe COVID-19 (RR 1.41; I2: 93%) subgroup. A CRP â©¾10 mg/L has a 51% sensitivity, 88% specificity, likelihood ratio (LR) + of 4.1, LR- of 0.5, and an area under curve (AUC) of 0.84. An elevated PCT was associated with an increased composite poor outcome [RR 3.92 (2.42, 6.35), p < 0.001; I2: 85%] and its mortality (RR 6.26; I2: 96%) and severe COVID-19 (RR 3.93; I2: 63%) subgroups. A PCT â©¾0.5 ng/ml has an 88% sensitivity, 68% specificity, LR+ of 2.7, LR- of 0.2, and an AUC of 0.88. An elevated D-dimer was associated with an increased composite poor outcome [RR 2.93 (2.14, 4.01), p < 0.001; I2: 77%], including its mortality (RR 4.15; I2: 83%) and severe COVID-19 (RR 2.42; I2: 58%) subgroups. A D-dimer >0.5 mg/L has a 58% sensitivity, 69% specificity, LR+ of 1.8, LR- of 0.6, and an AUC of 0.69. Patients with a composite poor outcome had a higher serum ferritin with a standardized mean difference of 0.90 (0.64, 1.15), p < 0.0001; I2: 76%. CONCLUSION: This meta-analysis showed that an elevated serum CRP, PCT, D-dimer, and ferritin were associated with a poor outcome in COVID-19. The reviews of this paper are available via the supplemental material section.

C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease-2019: a meta-analysis

In this retrospective study, we evaluated the levels of a series of serum biomarkers in coronavirus disease 2019 (COVID-19) patients (mild: 131; severe: 98; critical: 23). We found that there were significant increases in levels of human epididymis protein 4 (HE4) (73.6 ± 38.3 vs 46.5 ± 14.7 pmol/L; P < .001), cytokeratin-19 fragment (CYFRA21-1) (2.2 ± 0.9 vs 1.9 ± 0.8 µg/L; P < .001), carcinoembryonic antigen (CEA) (3.4 ± 2.2 vs 2.1 ± 1.2 µg/L; P < .001), carbohydrate antigens (CA) 125 (18.1 ± 13.5 vs 10.5 ± 4.6 µg/L; P < .001), and 153 (14.4 ± 8.9 vs 10.1 ± 4.4 µg/L; P < .001) in COVID-19 mild cases as compared to normal control subjects; their levels showed continuous and significant increases in severe and critical cases (HE4, CYFRA21-1, and CA125: P < .001; CEA and CA153: P < .01). Squamous cell carcinoma antigen (SCC) and CA199 increased significantly only in critical cases of COVID-19 as compared with mild and severe cases and normal controls (P < .01). There were positive associations between levels of C-reactive protein and levels of HE4 (R = .631; P < .001), CYFRA21-1 (R = .431; P < .001), CEA (R = .316; P < .001), SCC (R = .351; P < .001), CA153 (R = .359; P < .001) and CA125 (R = .223; P = .031). We concluded that elevations of serum cancer biomarkers positively correlated with the pathological progressions of COVID-19, demonstrating diffuse and acute pathophysiological injuries in COVID-19.

Elevations of serum cancer biomarkers correlate with severity of COVID-19

Severe acute respiratory syndrome coronavirus 2-induced direct cytopathic effects against type I and II pneumocytes mediate lung damage. Krebs von den Lungen-6 (KL-6) is mainly produced by damaged or regenerating alveolar type II pneumocytes. This preliminary study analyzed serum concentrations of KL-6 in patients with coronavirus disease (COVID-19) to verify its potential as a prognostic biomarker of severity. Twenty-two patients (median age [interquartile range] 63 [59-68] years, 16 males) with COVID-19 were enrolled prospectively. Patients were divided into mild-moderate and severe groups, according to respiratory impairment and clinical management. KL-6 serum concentrations and lymphocyte subset were obtained. Peripheral natural killer (NK) cells/µL were significantly higher in nonsevere patients than in the severe group (P = .0449) and the best cut-off value was 119 cells/µL. KL-6 serum concentrations were significantly higher in severe patients than the nonsevere group (P = .0118). Receiver operating characteristic analysis distinguished severe and nonsevere patients according to KL-6 serum levels and the best cut-off value was 406.5 U/mL. NK cell analysis and assay of KL-6 in serum can help identify severe COVID-19 patients. Increased KL-6 serum concentrations were observed in patients with severe pulmonary involvement, revealing a prognostic value and supporting the potential usefulness of KL-6 measurement to evaluate COVID-19 patients' prognosis.

Serum KL-6 concentrations as a novel biomarker of severe COVID-19

OBJECTIVE: To explore the indicators for severity in young COVID-19 patients age between 18 to 40. METHODS: This retrospective cohort study includes 65 consecutively admitted COVID-19 patients age between 18 to 40 in Zhongnan Hospital of Wuhan University. Among them, 53 were moderate cases, 12 were severe or critical cases. Epidemiological, clinical and laboratory characteristics and treatment data were collected. A multivariate logistic regression analysis was implemented to explore risk factors. RESULTS: The severe/critical cases have obviously higher BMI (average 29.23 vs. 22.79kg/m2 ) and lower liver CT value (average 50.00 vs. 65.00mU) than moderate cases group. The severe/critical cases have higher fasting glucose, alanine aminotransferase (ALT) , aspartate aminotransferase (AST) , and creatinine (Cr) compared with moderate cases (All P<0.01) . More severe/critical cases (58.33% vs. 1.92%) have positive urine protein. The severe/critical cases will experience a significant process of serum albumin decline. Logistic regression analysis showed that male, high body mass index (especially obesity), elevated fasting blood glucose and urinary protein positive are all risk factors for severe young COVID-19 patients. CONCLUSION: Obesity is an important predictor of severity in young COVID-19 patients. The main mechanism is related to the damage of liver and kidney.

Obesity as a Potential Predictor of Disease Severity in Young COVID-19 Patients: A Retrospective Study

BACKGROUND: Patients critically ill with coronavirus disease-2019 (COVID-19) feature hyperinflammation, and the associated biomarkers may be beneficial for risk stratification. We aimed to investigate the association between several biomarkers, including serum C-reactive protein (CRP), procalcitonin (PCT), D-dimer, and serum ferritin, and COVID-19 severity. METHODS: We performed a comprehensive systematic literature search through electronic databases. The outcome of interest for this study was the composite poor outcome, which comprises mortality, acute respiratory distress syndrome, need for care in an intensive care unit, and severe COVID-19. RESULTS: A total of 5350 patients were pooled from 25 studies. Elevated CRP was associated with an increased composite poor outcome [risk ratio (RR) 1.84 (1.45, 2.33), p < 0.001; I(2): 96%] and its severe COVID-19 (RR 1.41; I(2): 93%) subgroup. A CRP ⩾10 mg/L has a 51% sensitivity, 88% specificity, likelihood ratio (LR) + of 4.1, LR- of 0.5, and an area under curve (AUC) of 0.84. An elevated PCT was associated with an increased composite poor outcome [RR 3.92 (2.42, 6.35), p < 0.001; I(2): 85%] and its mortality (RR 6.26; I(2): 96%) and severe COVID-19 (RR 3.93; I(2): 63%) subgroups. A PCT ⩾0.5 ng/ml has an 88% sensitivity, 68% specificity, LR+ of 2.7, LR- of 0.2, and an AUC of 0.88. An elevated D-dimer was associated with an increased composite poor outcome [RR 2.93 (2.14, 4.01), p < 0.001; I(2): 77%], including its mortality (RR 4.15; I(2): 83%) and severe COVID-19 (RR 2.42; I(2): 58%) subgroups. A D-dimer >0.5 mg/L has a 58% sensitivity, 69% specificity, LR+ of 1.8, LR- of 0.6, and an AUC of 0.69. Patients with a composite poor outcome had a higher serum ferritin with a standardized mean difference of 0.90 (0.64, 1.15), p < 0.0001; I(2): 76%. CONCLUSION: This meta-analysis showed that an elevated serum CRP, PCT, D-dimer, and ferritin were associated with a poor outcome in COVID-19. The reviews of this paper are available via the supplemental material section.

Urinalysis parameters for predicting severity in coronavirus disease 2019 (COVID-19)

Aim: We aimed to explore the biomarkers for disease progression or the risk of nonsurvivors. Materials & methods: This study included 134 hospitalized patients with confirmed COVID-19 infection. The outcome of moderate versus severe versus critically ill patients and survivors versus nonsurvivors were compared. Results: An increase in the severity of COVID-19 pneumonia was positively associated with lower levels of platelets and albumin (all p < 0.05). In the critical group, the plasma levels of albumin continued to have a significant association for the risk of nonsurvivors (p < 0.05), even after adjusting for confounding factors. Conclusion: Albumin levels could be used as an independent predictor of the risk of nonsurvivors in critically ill patients with COVID-19.

Plasma albumin levels predict risk for nonsurvivors in critically ill patients with COVID-19

In this retrospective study, we evaluated the levels of a series of serum biomarkers in coronavirus disease 2019 (COVID‐19) patients (mild: 131; severe: 98; critical: 23). We found that there were significant increases in levels of human epididymis protein 4 (HE4) (73.6 ± 38.3 versus 46.5 ± 14.7, p<0.001), cytokeratin‐19 fragment (CYFRA21‐1) (2.2 ± 0.9 versus 1.9 ± 0.8, p<0.001), carcinoembryonic antigen (CEA) (3.4 ± 2.2 versus 2.1 ± 1.2, p<0.001), carbohydrate antigens (CA) 125 (18.1 ± 13.5 versus 10.5 ± 4.6, p<0.001) and 153 (14.4 ± 8.9 versus 10.1 ± 4.4, p<0.001) in COVID‐19 mild cases as compared to normal control subjects; their levels showed continuous and significant increases in severe and critical cases (HE4, CYFRA21‐1 and CA125: p<0.001; CEA and CA153: p<0.01). Squamous cell carcinoma antigen (SCC) and CA199 increased significantly only in critical cases of COVID‐19 as compared with mild and severe cases and normal controls (p<0.01). There were positive associations between levels of C‐reactive protein and levels of HE4 (R= 0.631, p<0.001), CYFRA21‐1(R= 0.431, p<0.001), CEA (R= 0.316, p<0.001), SCC (R= 0.351, p<0.001), CA153 (R= 0.359, p<0.001) and CA125 (R= 0.223, p=0.031). We concluded that elevations of serum cancer biomarkers positively correlated with the pathological progressions of COVID‐19, demonstrating diffuse and acute lung injuries. This article is protected by copyright. All rights reserved.

Elevations of serum cancer biomarkers correlate with severity of COVID‐19

COVID-19 has developed into a worldwide pandemic; early identification of severe illness is critical for controlling it and improving the prognosis of patients with limited medical resources. The present study aimed to analyze the characteristics of severe COVID-19 and identify biomarkers for differential diagnosis and prognosis prediction. In total, 27 consecutive patients with COVID-19 and 75 patients with flu were retrospectively enrolled. Clinical parameters were collected from electronic medical records. The disease course was divided into four stages: initial, progression, peak, and recovery stages, according to computed tomography (CT) progress. to mild COVID-19, the lymphocytes in the severe COVID-19 progressively decreased at the progression and the peak stages, but rebound in the recovery stage. The levels of C-reactive protein (CRP) in the severe group at the initial and progression stages were higher than those in the mild group. Correlation analysis showed that CRP (R = .62; P < .01), erythrocyte sedimentation rate (R = .55; P < .01) and granulocyte/lymphocyte ratio (R = .49; P < .01) were positively associated with the CT severity scores. In contrast, the number of lymphocytes (R = -.37; P < .01) was negatively correlated with the CT severity scores. The receiver-operating characteristic analysis demonstrated that area under the curve of CRP on the first visit for predicting severe COVID-19 was 0.87 (95% CI 0.10-1.00) at 20.42 mg/L cut-off, with sensitivity and specificity 83% and 91%, respectively. CRP in severe COVID-19 patients increased significantly at the initial stage, before CT findings. Importantly, CRP, which was associated with disease development, predicted early severe COVID-19.

C-reactive protein correlates with computed tomographic findings and predicts severe COVID-19 early

Abstract: Objective: Coronavirus disease 2019 (COVID-19) is an escalating global epidemic caused by SARS-CoV-2, with a high mortality in critical patients. Effective indicators for predicting disease severity in SARS-CoV-2 infected patients are urgently needed. Methods: In this study, 43 COVID-19 patients admitted in Chongqing Public Health Medical Center were involved. Demographic data, clinical features, and laboratory examinations were obtained through electronic medical records. Peripheral blood specimens were collected from COVID-19 patients and examined for lymphocyte subsets and cytokine profiles by flow cytometry. Potential contributing factors for prediction of disease severity were further analyzed. Results: A total of 43 COVID-19 patients were included in this study, including 29 mild patients and 14 sever patients. Severe patients were significantly older (61.9+/-9.4 vs 44.4+/-15.9) and had higher incidence in co-infection with bacteria compared to mild group (85.7%vs27.6%). Significantly more severe patients had the clinical symptoms of anhelation (78.6%) and asthma (71.4%). For laboratory examination, 57.1% severe cases showed significant reduction in lymphocyte count. The levels of Interluekin-6 (IL6), IL10, erythrocyte sedimentation rate (ESR) and D-Dimer (D-D) were significantly higher in severe patients than mild patients, while the level of albumin (ALB) was remarkably lower in severe patients. Further analysis demonstrated that ESR, D-D, age, ALB and IL6 were the major contributing factors for distinguishing severe patients from mild patients. Moreover, ESR was identified as the most powerful factor to predict disease progression of COVID-19 patients. Conclusion: Age and the levels of ESR, D-D, ALB and IL6 are closely related to the disease severity of COVID-19 patients. ESR can be used as a valuable indicator for distinguishing severe COVID-19 patients in early stage, so as to increase the survival of severe patients. Keyword: COVID-19, erythrocyte sedimentation rate, cytokines, lymphocytes

Potential Factors for Prediction of Disease Severity of COVID-19 Patients

BACKGROUND AND AIMS: Cardiac biomarkers like cardiac troponins and natriuretic peptides are elevated in a substantial proportion of patients with coronavirus disease 2019 (COVID-19). We propose an algorithmic approach using cardiac biomarkers to triage, risk-stratify and prognosticate patients with severe COVID-19. METHODS: We systematically searched the PubMed and Google Scholar databases until May 31st, 2020, and accessed the available data on the role of cardiac biomarkers in patients with COVID-19. RESULTS: COVID-19 is associated with acute cardiac injury in around 7–28% of patients, significantly increasing its associated complications and mortality. Patients with underlying cardiovascular disease are more prone to develop acute cardiac injury as a result of COVID-19. The use of cardiac biomarkers may aid in differentiating the cardiac cause of dyspnea in patients with severe COVID-19. Cardiac biomarkers may also aid in triaging, risk-stratification, clinical decision-making, and prognostication of patients with COVID-19. However, there are concerns that routine testing in all patients with COVID-19 irrespective of severity, may result in unnecessary downstream investigations which may be misleading. In this brief review, using an algorithmic approach, we have tried to rationalize the use of cardiac biomarkers among patients with severe COVID-19. This approach is also likely to lessen the infection exposure risk to the cardiovascular team attending patients with severe COVID-19. CONCLUSION: It appears beneficial to triage, risk-stratify, and prognosticate patients with COVID-19 based on the evidence of myocardial injury and the presence of underlying cardiovascular disease. Future research studies are, however, needed to validate these proposed benefits.

Cardiac biomarker-based risk stratification algorithm in patients with severe COVID-19

Background Approximately 15-20% of COVID-19 patients will develop severe pneumonia, about 10 % of which will die if not properly managed. Methods 125 COVID-19 patients enrolled in this study were classified into mild (93 cases) and severe (32 cases) groups, basing on their 3 to 7-days clinical outcomes. Patients' gender, age, comorbid with underlying diseases, epidemiological history, clinical manifestations, and laboratory tests on admission were collected and subsequently analyzed with single-factor and multivariate logistic regression methods. Finally, we evaluate their prognostic values with the receiver operating characteristic curve (ROC) analysis. Results Seventeen factors on admission differed significantly between mild and severe groups. Next, only four factors, including the comorbid with underlying diseases, increased respiratory rate (>24/min), elevated C-reactive protein (CRP >10mg/liter), and lactate dehydrogenase (LDH >250U/liter), were found to be independently associated with the later disease development. Prognostic value analysis by ROC indicated that individual factors could not confidently predict the occurrence of severe pneumonia, but that the combination of fast respiratory rate and elevated LDH significantly increase the predictive confidence (AUC= 0.944, sensitivity= 0.941, and specificity= 0.902). Three- or four-factors combinations, including elevated LDH and fast respiratory rate, further increased the prognostic value. Additionally, measurable serum viral RNA post-admission could independently predict the severe illness occurrence. Conclusions General clinical characteristics and laboratory tests, such as combinations consisting of elevated LDH and fast respiratory rate, and detectable viral RNA in serum post-admission could provide high confident prognostic value for identifying potential severe COVID-19 pneumonia patients.

Prognostic factors for COVID-19 pneumonia progression to severe symptom based on the earlier clinical features: a retrospective analysis

Abstract Background: A new virus broke out in Wuhan, Hubei, China, and was later named 2019 novel coronavirus (2019-nCoV). The clinical characteristics of severe pneumonia caused by 2019-nCoV are still not clear. Objectives: The aim of this study was to explore the clinical characteristics and risk factors of the severe pneumonia caused by the 2019-nCoV in Wuhan, China. Method: The study included patients hospitalized at the central hospital of Wuhan who had been diagnosed with a pneumonia caused by the novel coronavirus. Clinical features, chronic co-morbidities, demographic data, laboratory examinations, and chest computed tomography (CT) scans were reviewed through electronic medical records. SPSS was used for data analysis to explore the clinical characteristics and risk factors of the patients with the severe pneumonia. Results: A total of 110 patients diagnosed with 2019 novel coronavirus pneumonia were included in the study, including 38 with severe pneumonia and 72 with non-severe pneumonia. Statistical analysis showed that advanced age, an increase of D-dimer, and a decrease of lymphocytes were characteristics of the patients with severe pneumonia. Moreover, in the early stage of the disease, chest CT scans of patients with the severe pneumonia showed the illness can progress rapidly. Conclusions: Advanced age, lymphocyte decline, and D-dimer elevation are important characteristics of patients with severe pneumonia. Clinicians should focus on these characteristics to identify high-risk patients at an early stage.

Clinical Characteristics of Patients with Severe Pneumonia Caused by the 2019 Novel Coronavirus in Wuhan, China

AIMS: As of the 28th April 2020, the COVID-19 pandemic has infiltrated over 200 countries and affected over three million confirmed people. We review different biomarkers to evaluate if they are able to predict clinical outcomes and correlate with the severity of COVID-19 disease. METHODS: A systematic review of the literature was carried out to identify relevant articles using six different databases. Keywords to refine the search included ‘COVID-19’, ‘SARS-CoV2’, ‘Biomarkers’, among others. Only studies which reported data on pre-defined outcomes were included. KEY FINDINGS: Thirty-four relevant articles were identified which reviewed the following biomarkers: C-reactive protein, serum amyloid A, interleukin-6, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, D-dimer, cardiac troponin, renal biomarkers, lymphocytes and platelet count. Of these, all but two, showed significantly higher levels in patients with severe complications of COVID-19 infection compared to their non-severe counterparts. Lymphocytes and platelet count showed significantly lower levels in severe patients compared to non-severe patients. SIGNIFICANCE: Although research is still in its early stages, the discovery of how different biomarkers behave during the course of the disease could help clinicians in identifying severe disease earlier and subsequently improve prognosis. Nevertheless, we urge for more research across the globe to corroborate these findings.

The role of biomarkers in diagnosis of COVID-19 – A systematic review

BACKGROUND AND AIMS: Cardiac biomarkers like cardiac troponins and natriuretic peptides are elevated in a substantial proportion of patients with coronavirus disease 2019 (COVID-19). We propose an algorithmic approach using cardiac biomarkers to triage, risk-stratify and prognosticate patients with severe COVID-19. METHODS: We systematically searched the PubMed and Google Scholar databases until May 31st, 2020, and accessed the available data on the role of cardiac biomarkers in patients with COVID-19. RESULTS: COVID-19 is associated with acute cardiac injury in around 7-28% of patients, significantly increasing its associated complications and mortality. Patients with underlying cardiovascular disease are more prone to develop acute cardiac injury as a result of COVID-19. The use of cardiac biomarkers may aid in differentiating the cardiac cause of dyspnea in patients with severe COVID-19. Cardiac biomarkers may also aid in triaging, risk-stratification, clinical decision-making, and prognostication of patients with COVID-19. However, there are concerns that routine testing in all patients with COVID-19 irrespective of severity, may result in unnecessary downstream investigations which may be misleading. In this brief review, using an algorithmic approach, we have tried to rationalize the use of cardiac biomarkers among patients with severe COVID-19. This approach is also likely to lessen the infection exposure risk to the cardiovascular team attending patients with severe COVID-19. CONCLUSION: It appears beneficial to triage, risk-stratify, and prognosticate patients with COVID-19 based on the evidence of myocardial injury and the presence of underlying cardiovascular disease. Future research studies are, however, needed to validate these proposed benefits.

Among 417 COVID-19 patients in Shenzhen, demographic characteristics, clinical manifestations and baseline laboratory tests showed significant differences between mild-moderate cohort and severe-critical cohort.Based on these differences, a convenient mathematical model was established to predict the illness severity of COVID-19. The model includes four parameters: age, BMI, CD4(+) lymphocytes and IL-6 levels. The AUC of the model is 0.911.The high risk factors for developing to severe COVID-19 are: age ≥ 55 years, BMI > 27 kg / m(2), IL-6 ≥ 20 pg / ml, CD4(+) T cell ≤ 400 count / μ L.Among 249 discharged COVID-19 patients, those who recovered after 20 days had a lower count of platelet, a higher level of estimated glomerular filtration rate, and higher level of interleukin-6 and myoglobin than those who recovered within 20 days.

Predicting Illness Severity and Short-Term Outcomes of COVID-19: A Retrospective Cohort Study in China

BACKGROUND: The emerging infection of the 2019 novel coronavirus (2019-nCoV) in late December, 2019 in Wuhan, China, has caused an extreme health concern, with many patients having progressed to acute respiratory disease or other complications in a short period. Meanwhile, the risk factors associated with the disease progression still remain elusive. METHODS: A cohort of 17 patients with laboratory-confirmed 2019-nCoV infections who were admitted to the Ninth Hospital of Nanchang between January 28 and February 6, 2020, were enrolled in this study. All the patients received standardized treatment. The disease progression was evaluated every 7 days after admission. The clinical, radiologic, and laboratory characteristics were retrospectively analyzed, and the factors associated with the disease progression were screened by binary logistic regression analysis. RESULTS: The cohort comprised 11 women (64.7%) and 6 men (35.3%) between the ages of 18 to 70 years old. All patients had a reported history of contact with infection-confirmed patients. Fever (11/64.7%) and cough (8/47.1%) were the most common symptoms, whereas dyspnea (2/11.8%) and fatigue (3/17.6%) were rare, and there was no patient with diarrhea symptoms. There were 5 patients with aggravated disease at the first disease progression evaluation, and no patient received mechanical ventilation, transferred to the intensive care unit (ICU), or progressed to acute respiratory distress syndrome, septic shock, refractory metabolic acidosis, coagulation dysfunction, or death. Based on the disease progression, patients were divided into the non-aggravation group (12 cases) and the aggravation group (5 cases). There were no significant differences between the 2 groups with respect to their clinical characteristics. Chest computed tomography (CT) on admission revealed there were 8 patients (47.1%) with invasive lesions found bilaterally on the lungs on multiple lobes, 4 patients (23.5%) with invasive lesions on 1 lobe, and 5 patients (29.4%) with normal chest CT. The aggravation group had1 patient (20.0%) with invasive lesions on one lobe, 3 (60.0%) with invasive lesions on multiple lobes, bilaterally, and 1 (20.0%) with normal chest CT; meanwhile, the nonaggravation group had 3 patients (25.0%) with invasive lesions on one lobe, 5 (41.7%) with invasive lesions on multiple lobes, bilaterally, and 4 (33.3%) with normal chest CT. No significant difference was found between the 2 groups. In the aggravation group, the total lymphocyte counts significantly decreased in comparison to that in the non-aggravation group. Further analysis showed that the CD4+ T cell count but not the CD8+ T cell count of the aggravation group was significantly lower than that of the non-aggravation group. Correlation analysis indicated total lymphocyte count was positively correlated with CD4+ T cell count, and no significant differences were found between the 2 groups in other laboratory measurements, including those of white blood cell (WBC) count, C-reactive protein (CRP), albumin, lactate dehydrogenase (LDH), and D-dimer. Finally, a binary logistic regression model was used to identify the factors associated with the disease progression. It was found that total lymphocyte count was a risk factor associated with disease progression in patients infected with 2019-nCoV. CONCLUSIONS: A higher cell count of total lymphocytes may indicate a better outcome of the disease, and immune response may be a vital factor for directing disease progression in the early stage of 2019-nCoV infection.

Risk factors associated with disease progression in a cohort of patients infected with the 2019 novel coronavirus

OBJECTIVE: To explore the indicators for severity in young COVID‐19 patients age between 18 to 40. METHODS: This retrospective cohort study includes 65 consecutively admitted COVID‐19 patients age between 18 to 40 in Zhongnan Hospital of Wuhan University. Among them, 53 were moderate cases, 12 were severe or critical cases. Epidemiological, clinical and laboratory characteristics and treatment data were collected. A multivariate logistic regression analysis was implemented to explore risk factors. RESULTS: The severe/critical cases have obviously higher BMI (average 29.23 vs. 22.79kg/m(2)) and lower liver CT value (average 50.00 vs. 65.00mU) than moderate cases group. The severe/critical cases have higher fasting glucose, alanine aminotransferase (ALT) , aspartate aminotransferase (AST) , and creatinine (Cr) compared with moderate cases (All P<0.01) . More severe/critical cases (58.33% vs. 1.92%) have positive urine protein. The severe/critical cases will experience a significant process of serum albumin decline. Logistic regression analysis showed that male, high body mass index (especially obesity), elevated fasting blood glucose and urinary protein positive are all risk factors for severe young COVID‐19 patients. CONCLUSION: Obesity is an important predictor of severity in young COVID‐19 patients. The main mechanism is related to the damage of liver and kidney.

Obesity as a Potential Predictor of Disease Severity in Young COVID‐19 Patients: A Retrospective Study

Since March 11, 2020, the World Health Organization (WHO) defined Coronavirus disease 2019 (COVID-19) as a pandemic, with a series of confirmed cases that currently exceeded 300,000 people worldwide and with approximately 14,500 deaths. Accumulated evidence suggests that a subgroup of patients with severe COVID-19 could have a dysregulation of the immune response that allows the development of viral hyperinflammation. Thus, all patients with severe COVID-19 should be screened for hyperinflammation using laboratory parameters in order to improve mortality. Neutrophil-to-Lymphocyte ratio (NLR) and Lymphocyte-to-C-reactive protein ratio (LCR) are established inflammation markers that reflect systemic inflammatory response, and both are available in almost all laboratories. In this study, a meta-analysis was performed to investigate whether NLR and LCR values can help predict clinical severity in patients with COVID-19. This article is protected by copyright. All rights reserved.

Neutrophil‐to‐lymphocyte ratio and lymphocyte‐to‐C‐reactive protein ratio in patients with severe coronavirus disease 2019 (COVID‐19): A meta‐analysis

Current pandemic caused by SARS-CoV-2 inducing viral COVID-19 pneumonia, is categorized in 3 stages. Some biomarkers could be assigned to one of these stages, showing a correlation to mortality in COVID-19 patients. Laboratory findings in COVID-19, especially when serially evaluated, may represent individual disease severity and prognosis. These may help planning and controlling therapeutic interventions. Biomarkers for myocardial injury (high sensitive cardiac troponin, hsTn) or hemodynamic stress (NTproBNP) may occur in COVID-19 pneumonia such as in other pneumonias, correlating with severity and prognosis of the underlying disease. In hospitalized COVID-19 patients' mild increases of hsTn or NTproBNP may be explained by cardiovascular comorbidities and direct or indirect cardiac damage or stress caused by or during COVID-19 pneumonia. In case of suspected NSTE-ACS and COVID-19, indications for echocardiography or reperfusion strategy should be carefully considered against the risk of contamination.

[Cardiac biomarkers and COVID-19 - Phenotypes and Interpretation].

COVID‐19 has developed into a worldwide pandemic; early identification of severe illness is critical for controlling it and improving the prognosis of patients with limited medical resources. The present study aimed to analyze the characteristics of severe COVID‐19 and identify biomarkers for differential diagnosis and prognosis prediction. In total, 27 consecutive patients with COVID‐19 and 75 patients with flu were retrospectively enrolled. Clinical parameters were collected from electronic medical records. The disease course was divided into four stages: initial, progression, peak, and recovery stages, according to computed tomography (CT) progress. to mild COVID‐19, the lymphocytes in the severe COVID‐19 progressively decreased at the progression and the peak stages, but rebound in the recovery stage. The levels of C‐reactive protein (CRP) in the severe group at the initial and progression stages were higher than those in the mild group. Correlation analysis showed that CRP (R = .62; P < .01), erythrocyte sedimentation rate (R = .55; P < .01) and granulocyte/lymphocyte ratio (R = .49; P < .01) were positively associated with the CT severity scores. In contrast, the number of lymphocytes (R = −.37; P < .01) was negatively correlated with the CT severity scores. The receiver‐operating characteristic analysis demonstrated that area under the curve of CRP on the first visit for predicting severe COVID‐19 was 0.87 (95% CI 0.10–1.00) at 20.42 mg/L cut‐off, with sensitivity and specificity 83% and 91%, respectively. CRP in severe COVID‐19 patients increased significantly at the initial stage, before CT findings. Importantly, CRP, which was associated with disease development, predicted early severe COVID‐19.

C‐reactive protein correlates with computed tomographic findings and predicts severe COVID‐19 early

Abstract Objectives The study was aimed at investigating the characteristics of peripheral blood lymphocyte subsets and serum cytokines in children with 2019 novel coronavirus (2019-nCoV) pneumonia. Methods Children with 2019-nCoV pneumonia or with respiratory syncytial virus (RSV) pneumonia were included. Data including lymphocyte subsets and serum cytokines were collected and analyzed. Results : 56 patients were included in the study, 40 children with 2019-nCoV pneumonia and 16 children with RSV pneumonia. Compared with children with RSV pneumonia, patients with 2019-nCoV pneumonia had higher count of CD3+8+ lymphocyte, higher percentages of CD3+, CD3+8+ lymphocytes and a lower percentage of CD19+ lymphocyte. The serum IL-10 level was significantly higher in children with RSV pneumonia. One 2019-nCoV pneumonia child who was with an obvious increase of IL-10 developed severe pneumonia. Conclusions Immune response played a very important role in the development of 2019-nCoV pneumonia. The effective CD8+ T cell response might influence the severity of 2019-nCoV pneumonia. The adaptable change in IL-10 level might contribute to the relatively mild pneumonia symptoms in children with 2019-nCoV pneumonia and bacterial co-infection might be a risk factor of severe 2019-nCoV pneumonia.

The profile of peripheral blood lymphocyte subsets and serum cytokines in children with 2019 novel coronavirus pneumonia

Rationale: Up to date, the exploration of clinical features in severe COVID-19 patients were mostly from the same center in Wuhan, China. The clinical data in other centers is limited. This study aims to explore the feasible parameters which could be used in clinical practice to predict the prognosis in hospitalized patients with severe coronavirus disease-19 (COVID-19). Methods: In this case-control study, patients with severe COVID-19 in this newly established isolation center on admission between 27 January 2020 to 19 March 2020 were divided to discharge group and death event group. Clinical information was collected and analyzed for the following objectives: 1. Comparisons of basic characteristics between two groups; 2. Risk factors for death on admission using logistic regression; 3. Dynamic changes of radiographic and laboratory parameters between two groups in the course. Results: 124 patients with severe COVID-19 on admission were included and divided into discharge group (n=35) and death event group (n=89). Sex, SpO2, breath rate, diastolic pressure, neutrophil, lymphocyte, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and D-dimer were significantly correlated with death events identified using bivariate logistic regression. Further multivariate logistic regression demonstrated a significant model fitting with C-index of 0.845 (p<0.001), in which SpO2≤89%, lymphocyte≤0.64×10(9)/L, CRP>77.35mg/L, PCT>0.20μg/L, and LDH>481U/L were the independent risk factors with the ORs of 2.959, 4.015, 2.852, 3.554, and 3.185, respectively (p<0.04). In the course, persistently lower lymphocyte with higher levels of CRP, PCT, IL-6, neutrophil, LDH, D-dimer, cardiac troponin I (cTnI), brain natriuretic peptide (BNP), and increased CD4+/CD8+ T-lymphocyte ratio and were observed in death events group, while these parameters stayed stable or improved in discharge group. Conclusions: On admission, the levels of SpO2, lymphocyte, CRP, PCT, and LDH could predict the prognosis of severe COVID-19 patients. Systematic inflammation with induced cardiac dysfunction was likely a primary reason for death events in severe COVID-19 except for acute respiratory distress syndrome.

Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study

OBJECTIVE To examine if baseline soluble urokinase plasminogen activator receptor (suPAR) can predict whether patients with COVID-19 symptoms will need mechanical ventilation during a 14-day follow-up. Furthermore, to examine differences in demographics, clinical signs, and biomarkers in patients tested either positive or negative for SARS-CoV-2. DESIGN Prospective cohort study including patients presenting with symptoms of COVID-19. SETTING Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark. PARTICIPANTS 407 patients presenting with symptoms of COVID-19 were included from the Emergency Department (ED). Patients were included from March 19 to April 3 and follow-up data was collected until April 17, 2020. MAIN OUTCOME MEASURES Primary outcomes were respiratory failure in patients presenting with symptoms of COVID-19 and in those with a positive SARS-CoV-2 RT-PCR test, respectively. Furthermore, we analysed differences between patients testing positive and negative for SARS-CoV-2, and disease severity outcomes in SARS-CoV-2 positive patients according to baseline suPAR. BACKGROUND Patients admitted to ED with clinical signs or symptoms of COVID-19 infection need a safe and quick triage, in order to determine if an in-hospital stay is necessary or if the patient can safely be isolated in their own home with relevant precautions. suPAR is a biomarker previously shown to be associated with adverse outcomes in acute medical patients. We aimed to examine if suPAR at baseline presentation is predictive of respiratory failure in patients presenting with symptoms of COVID-19. Furthermore, we examined demographic, clinical, and biochemical differences between SARS-CoV-2-positive and negative patients. RESULTS Among the 407 symptomatic patients, the median (interquartile range) age was 64 years (47-77), 58% were women, and median suPAR was 4.2 ng/ml (2.7-6.4). suPAR level below 4.75 ng/ml at admission ruled out respiratory failure during follow-up with an area under the curve (95% CI) of 0.89 (0.85-0.94) and a negative predictive value of 99.5%. Of the 407 symptomatic patients, 117 (28.8%) had a positive RT-PCR test for SARS-CoV-2 and presented with significant differences in vital signs, cell counts, and biomarkers compared to SARS-CoV-2 negative patients. In SARS-CoV-2 positive patients eligible for mechanical ventilation (N=87), 26 (30%) developed respiratory failure. Best baseline predictors of respiratory failure were suPAR with an area under the curve (95% CI) of 0.88 (0.80-0.95), EWS 0.84 (0.75-0.93), lactate dehydrogenase 0.82 (0.71-0.93), and C-reactive protein 0.80 (0.70-0.89). CONCLUSION SARS-CoV-2 affects several patient parameters underpinning the severe impact of the infection. A low suPAR level (<4.75 ng/ml) at baseline is a useful biomarker for aiding clinical decisions including discharge of patients presenting with symptoms of COVID-19.

Low levels of the prognostic biomarker suPAR are predictive of mild outcome in patients with symptoms of COVID-19 - a prospective cohort study

INTRODUCTION Since December 2019, an outbreak of coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome - Coronavirus 2 (SARS-CoV-2) emerged in China and has become a global threat. Comparison of hematological parameters between mild and severe cases of SARS-CoV 2 is so far limited, but significant differences in parameters such as interleukin-6, d-dimers, glucose, fibrinogen and C-reactive protein have been already reported. PURPOSE In this study we analyzed the changes observed in easily measured blood biomarkers in the patients and provided evidence of how these markers can be used as prognostic factors of the disease. METHODS Demographic characteristics, detailed medical history, and laboratory findings of all enrolled SARS-CoV 2 infection positive patients who were referred to Patras University Hospital from the period of March 4th 2020 (when first confirmed case in Greece appeared in our hospital) until April 4th 2020 were extracted from electronic medical records and analyzed. RESULTS We provided evidence that some very common laboratory values can be used as independent predictive factors in SARS-CoV 2 infection. Despite the retrospective nature of this study and the small number of subjects analyzed, we showed that NLR, LDH, d-dimers, CRP, fibrinogen and ferritin can be used early at the patient's first visit for SARS-CoV 2 infection symptoms and can predict the severity of infection. CONCLUSION More studies are warranted to further objectively confirm the clinical value of prognostic factors related to SARS-CoV 2 and establish an easy-to-get panel of laboratory findings for evaluating the disease severity.

Prognosis of COVID-19: Changes in laboratory parameters.

Mortality is high among severe patients with 2019 novel coronavirus-infected disease (COVID-19). Early prediction of progression to severe cases is needed. We retrospectively collected patients with COVID-19 in two hospital of Chongqing from 1st January to 29th February 2020. At admission, we collected the demographics and laboratory tests to predict whether the patient would progress to severe cases in hospitalization. Severe case was confirmed when one of the following criteria occurred: (a) dyspnea, respiratory rate ≥30 breaths/min, (b) blood oxygen saturation ≤93%, and (c) PaO2 /FiO2 ≤ 300 mm Hg. At admission, 348 mild cases were enrolled in this study. Of them, 20 (5.7%) patients progressed to severe cases after median 4.0 days (interquartile range: 2.3-6.0). Pulmonary inflammation index, platelet counts, sodium, C-reactive protein, prealbumin, and PaCO2 showed good distinguishing power to predict progression to severe cases (each area under the curve of receiver operating characteristics [AUC] ≥ 0.8). Age, heart rate, chlorine, alanine aminotransferase, aspartate aminotransferase, procalcitonin, creatine kinase, pH, CD3 counts, and CD4 counts showed moderate distinguishing power (each AUC between 0.7-0.8). And potassium, creatinine, temperature, and D-dimer showed mild distinguishing power (each AUC between 0.6-0.7). In addition, higher C-reactive protein was associated with shorter time to progress to severe cases (r = -0.62). Several easily obtained variables at admission are associated with progression to severe cases during hospitalization. These variables provide a reference for the medical staffs when they manage the patients with COVID-19.

Correlation between the variables collected at admission and progression to severe cases during hospitalization among patients with COVID-19 in Chongqing

BACKGROUND The emerging infection of the 2019 novel coronavirus (2019-nCoV) in late December, 2019 in Wuhan, China, has caused an extreme health concern, with many patients having progressed to acute respiratory disease or other complications in a short period. Meanwhile, the risk factors associated with the disease progression still remain elusive. METHODS A cohort of 17 patients with laboratory-confirmed 2019-nCoV infections who were admitted to the Ninth Hospital of Nanchang between January 28 and February 6, 2020, were enrolled in this study. All the patients received standardized treatment. The disease progression was evaluated every 7 days after admission. The clinical, radiologic, and laboratory characteristics were retrospectively analyzed, and the factors associated with the disease progression were screened by binary logistic regression analysis. RESULTS The cohort comprised 11 women (64.7%) and 6 men (35.3%) between the ages of 18 to 70 years old. All patients had a reported history of contact with infection-confirmed patients. Fever (11/64.7%) and cough (8/47.1%) were the most common symptoms, whereas dyspnea (2/11.8%) and fatigue (3/17.6%) were rare, and there was no patient with diarrhea symptoms. There were 5 patients with aggravated disease at the first disease progression evaluation, and no patient received mechanical ventilation, transferred to the intensive care unit (ICU), or progressed to acute respiratory distress syndrome, septic shock, refractory metabolic acidosis, coagulation dysfunction, or death. Based on the disease progression, patients were divided into the non-aggravation group (12 cases) and the aggravation group (5 cases). There were no significant differences between the 2 groups with respect to their clinical characteristics. Chest computed tomography (CT) on admission revealed there were 8 patients (47.1%) with invasive lesions found bilaterally on the lungs on multiple lobes, 4 patients (23.5%) with invasive lesions on 1 lobe, and 5 patients (29.4%) with normal chest CT. The aggravation group had1 patient (20.0%) with invasive lesions on one lobe, 3 (60.0%) with invasive lesions on multiple lobes, bilaterally, and 1 (20.0%) with normal chest CT; meanwhile, the nonaggravation group had 3 patients (25.0%) with invasive lesions on one lobe, 5 (41.7%) with invasive lesions on multiple lobes, bilaterally, and 4 (33.3%) with normal chest CT. No significant difference was found between the 2 groups. In the aggravation group, the total lymphocyte counts significantly decreased in comparison to that in the non-aggravation group. Further analysis showed that the CD4+ T cell count but not the CD8+ T cell count of the aggravation group was significantly lower than that of the non-aggravation group. Correlation analysis indicated total lymphocyte count was positively correlated with CD4+ T cell count, and no significant differences were found between the 2 groups in other laboratory measurements, including those of white blood cell (WBC) count, C-reactive protein (CRP), albumin, lactate dehydrogenase (LDH), and D-dimer. Finally, a binary logistic regression model was used to identify the factors associated with the disease progression. It was found that total lymphocyte count was a risk factor associated with disease progression in patients infected with 2019-nCoV. CONCLUSIONS A higher cell count of total lymphocytes may indicate a better outcome of the disease, and immune response may be a vital factor for directing disease progression in the early stage of 2019-nCoV infection.

Risk factors associated with disease progression in a cohort of patients infected with the 2019 novel coronavirus.

BACKGROUND Severe acute respiratory coronavirus 2 (SARS-CoV-2) caused coronavirus disease 2019 (COVID-19) has become a pandemic. This study addressed the clinical and immunopathological characteristics of severe COVID-19. METHODS Sixty-nine COVID-19 patients were classified into as severe and non-severe groups to analyze their clinical and laboratory characteristics. A panel of blood cytokines was quantified over time. Biopsy specimens from two deceased cases were obtained for immunopathological, ultrastructural, and in situ hybridization examinations. RESULTS Circulating cytokines, including IL8, IL6, TNFα, IP10, MCP1, and RANTES, were significantly elevated in severe COVID-19 patients. Dynamic IL6 and IL8 were associated with disease progression. SARS-CoV-2 was demonstrated to infect type II, type I pneumocytes and endothelial cells, leading to severe lung damage through cell pyroptosis and apoptosis. In severe cases, lymphopenia, neutrophilia, depletion of CD4+ and CD8+ T lymphocytes, and massive macrophage and neutrophil infiltrates were observed in both blood and lung tissues. CONCLUSIONS A panel of circulating cytokines could be used to predict disease deterioration and inform clinical interventions. Severe pulmonary damage was predominantly attributed to both SARS-CoV-2 caused cytopathy and immunopathologic damage. Strategies that encourage pulmonary recruitment and overactivation of inflammatory cells by suppressing cytokine storm might improve the outcomes of severe COVID-19 patients.

Clinical and pathological investigation of severe COVID-19 patients.

Coronavirus disease 2019 (COVID-19) has spread rapidly around the world since its emergence in humans last December. Previous studies suggested that numerous markers of inflammation were elevated in patients in with severe disease relative to patients with milder conditions, and an elevated level of interleukin-6 (IL-6) was associated with a high case fatality of COVID-19 infection. This article is protected by copyright. All rights reserved.

Elevated interleukin-6 is associated with severity of COVID-19: a meta-analysis

Mortality is high among severe patients with 2019 novel coronavirus‐infected disease (COVID‐19). Early prediction of progression to severe cases is needed. We retrospectively collected patients with COVID‐19 in two hospital of Chongqing from 1st January to 29th February 2020. At admission, we collected the demographics and laboratory tests to predict whether the patient would progress to severe cases in hospitalization. Severe case was confirmed when one of the following criteria occurred: (a) dyspnea, respiratory rate ≥30 breaths/min, (b) blood oxygen saturation ≤93%, and (c) PaO(2)/FiO(2) ≤ 300 mm Hg. At admission, 348 mild cases were enrolled in this study. Of them, 20 (5.7%) patients progressed to severe cases after median 4.0 days (interquartile range: 2.3‐6.0). Pulmonary inflammation index, platelet counts, sodium, C‐reactive protein, prealbumin, and PaCO(2) showed good distinguishing power to predict progression to severe cases (each area under the curve of receiver operating characteristics [AUC] ≥ 0.8). Age, heart rate, chlorine, alanine aminotransferase, aspartate aminotransferase, procalcitonin, creatine kinase, pH, CD3 counts, and CD4 counts showed moderate distinguishing power (each AUC between 0.7‐0.8). And potassium, creatinine, temperature, and D‐dimer showed mild distinguishing power (each AUC between 0.6‐0.7). In addition, higher C‐reactive protein was associated with shorter time to progress to severe cases (r = −0.62). Several easily obtained variables at admission are associated with progression to severe cases during hospitalization. These variables provide a reference for the medical staffs when they manage the patients with COVID‐19.

Correlation between the variables collected at admission and progression to severe cases during hospitalization among patients with COVID‐19 in Chongqing

Elevated interleukin‐6, interleukin‐10 and neutrophil : lymphocyte ratio as identifiers of severe coronavirus disease 2019

Objectives In December 2019, there was an outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, China, and since then, the disease has been increasingly spread throughout the world. Unfortunately, the information about early prediction factors for disease progression is relatively limited. Therefore, there is an urgent need to investigate the risk factors of developing severe disease. The objective of the study was to reveal the risk factors of developing severe disease by comparing the differences in the hemocyte count and dynamic profiles in patients with severe and non-severe COVID-19. Methods In this retrospectively analyzed cohort, 141 confirmed COVID-19 patients were enrolled in Taizhou Public Health Medical Center, Taizhou Hospital, Zhejiang Province, China, from January 17, 2020 to February 26, 2020. Clinical characteristics and hemocyte counts of severe and non-severe COVID patients were collected. The differences in the hemocyte counts and dynamic profiles in patients with severe and non-severe COVID-19 were compared. Multivariate Cox regression analysis was performed to identify potential biomarkers for predicting disease progression. A concordance index (C-index), calibration curve, decision curve and the clinical impact curve were calculated to assess the predictive accuracy. Results The data showed that the white blood cell count, neutrophil count and platelet count were normal on the day of hospital admission in most COVID-19 patients (87.9%, 85.1% and 88.7%, respectively). A total of 82.8% of severe patients had lymphopenia after the onset of symptoms, and as the disease progressed, there was marked lymphopenia. Multivariate Cox analysis showed that the neutrophil count (hazard ratio [HR] = 4.441, 95% CI = 1.954-10.090, p = 0.000), lymphocyte count (HR = 0.255, 95% CI = 0.097-0.669, p = 0.006) and platelet count (HR = 0.244, 95% CI = 0.111-0.537, p = 0.000) were independent risk factors for disease progression. The C-index (0.821 [95% CI, 0.746-0.896]), calibration curve, decision curve and the clinical impact curve showed that the nomogram can be used to predict the disease progression in COVID-19 patients accurately. In addition, the data involving the neutrophil count, lymphocyte count and platelet count (NLP score) have something to do with improving risk stratification and management of COVID-19 patients. Conclusions We designed a clinically predictive tool which is easy to use for assessing the progression risk of COVID-19, and the NLP score could be used to facilitate patient stratification management.

The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study

BACKGROUND: Several previously healthy young adults have developed Coronavirus Disease 2019 (COVID-19), and a few of them progressed to the severe stage. However, the factors are not yet determined. METHOD: We retrospectively analyzed 123 previously healthy young adults diagnosed with COVID-19 from January to March 2020 in a tertiary hospital in Wuhan. Patients were classified as having mild or severe COVID-19 based on their respiratory rate, SpO(2), and PaO(2)/FiO(2) levels. Patients’ symptoms, computer tomography (CT) images, preadmission drugs received, and the serum biochemical examination on admission were compared between the mild and severe groups. Significant variables were enrolled into logistic regression model to predict the factors affecting disease severity. A receiver operating characteristic (ROC) curve was applied to validate the predictive value of predictors. RESULT: Age; temperature; anorexia; and white blood cell count, neutrophil percentage, platelet count, lymphocyte count, C-reactive protein, aspartate transaminase, creatine kinase, albumin, and fibrinogen values were significantly different between patients with mild and severe COVID-19 (P < 0.05). Logistic regression analysis confirmed that lymphopenia (P = 0.010) indicated severe prognosis in previously healthy young adults with COVID-19, with the area under the curve (AUC) was 0.791(95% Confidence Interval (CI) 0.704–0.877)(P < 0.001). CONCLUSION: For previously healthy young adults with COVID-19, lymphopenia on admission can predict severe prognosis.

Predictive factors of severe coronavirus disease 2019 in previously healthy young adults: a single-center, retrospective study

The hemocyte counts as a potential biomarker for predicting disease progression in COVID-19: a retrospective study.

• Fibrosis indicators were early warning markers for critical COVID-19 patients. • Dynamic changes of lymphocyte count and CRP levels were related to the prognosis. • Rapid pulmonary consolidation was the major feature for died COVID-19 patients. • Pathological changes included inflammatory exudation and formation of granulomatous nodule.

Correlation analysis of the severity and clinical prognosis of 32 cases of patients with COVID-19

Abstract Background COVID-19 can manifest as a viral induced hyperinflammation with multi-organ involvement. Such patients often experience rapid deterioration and need for mechanical ventilation. Currently, no prospectively validated biomarker of impending respiratory failure is available. Objective We aimed to identify and prospectively validate biomarkers that allow the identification of patients in need of impending mechanical ventilation. Methods Patients with COVID-19 hospitalized from February 29th to April 09th, 2020 were analyzed for baseline clinical and laboratory findings at admission and during the disease. Data from 89 evaluable patients were available for the purpose of analysis comprising an initial evaluation cohort (n=40) followed by a temporally separated validation cohort (n=49). Results We identified markers of inflammation, LDH and creatinine as most predictive variables of respiratory failure in the evaluation cohort. Maximal interleukin-6 (IL-6) levels before intubation showed the strongest association with the need of mechanical ventilation followed by maximal CRP. Respective AUC values for IL-6 and CRP in the evaluation cohort were 0.97 and 0.86 and similar in the validation cohort 0.90 and 0.83. The calculated optimal cutoff values in the course of disease from the evaluation cohort (IL-6> 80 pg/ml and CRP> 97 mg/l) both correctly classified 80% of patients in the validation cohort regarding their risk of respiratory failure. Conclusion Maximal levels of IL-6 followed by CRP were highly predictive of the need for mechanical ventilation. This suggests the possibility of using IL-6 or CRP levels to guide escalation of treatment in patients with COVID-19 related hyperinflammatory syndrome.

Elevated levels of interleukin-6 and CRP predict the need for mechanical ventilation in COVID-19

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2. Methods: Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation. Results: A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036-1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180-1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129-1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013-1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%. Conclusions: A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

A subgroup of COVID-19 patients develop very severe disease with requirement for ICU treatment, ventilation, and ECMO therapy. Laboratory tests indicate that the immune and clotting system show marked alterations with hyper-activation, hyper-inflammation, cytokine storm development. Furthermore, organ-specific biomarkers demonstrate the involvement of cardiac muscle, kidney, and liver dysfunction in many patients. In this article the use of laboratory biomarkers is discussed with regard to their use for diagnosis, disease progression, and risk assessment.

Laboratory characteristics of patients infected with the novel SARS-CoV-2 virus

Rationale: Up to date, the exploration of clinical features in severe COVID-19 patients were mostly from the same center in Wuhan, China. The clinical data in other centers is limited. This study aims to explore the feasible parameters which could be used in clinical practice to predict the prognosis in hospitalized patients with severe coronavirus disease-19 (COVID-19). Methods: In this case-control study, patients with severe COVID-19 in this newly established isolation center on admission between 27 January 2020 to 19 March 2020 were divided to discharge group and death event group. Clinical information was collected and analyzed for the following objectives: 1. Comparisons of basic characteristics between two groups; 2. Risk factors for death on admission using logistic regression; 3. Dynamic changes of radiographic and laboratory parameters between two groups in the course. Results: 124 patients with severe COVID-19 on admission were included and divided into discharge group (n=35) and death event group (n=89). Sex, SpO2, breath rate, diastolic pressure, neutrophil, lymphocyte, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and D-dimer were significantly correlated with death events identified using bivariate logistic regression. Further multivariate logistic regression demonstrated a significant model fitting with C-index of 0.845 (p<0.001), in which SpO2≤89%, lymphocyte≤0.64×109/L, CRP>77.35mg/L, PCT>0.20µg/L, and LDH>481U/L were the independent risk factors with the ORs of 2.959, 4.015, 2.852, 3.554, and 3.185, respectively (p<0.04). In the course, persistently lower lymphocyte with higher levels of CRP, PCT, IL-6, neutrophil, LDH, D-dimer, cardiac troponin I (cTnI), brain natriuretic peptide (BNP), and increased CD4+/CD8+ T-lymphocyte ratio and were observed in death events group, while these parameters stayed stable or improved in discharge group. Conclusions: On admission, the levels of SpO2, lymphocyte, CRP, PCT, and LDH could predict the prognosis of severe COVID-19 patients. Systematic inflammation with induced cardiac dysfunction was likely a primary reason for death events in severe COVID-19 except for acute respiratory distress syndrome.

Objective: To determine the predictive value of CT and clinical characteristics for short-term disease progression in patients with 2019 novel coronavirus pneumonia (NCP). Materials and Methods: 224 patients with confirmed 2019 novel coronavirus (COVID-19) infection outside Wuhan who had chest CT examinations were retrospectively screened. Clinical data were obtained from electronic medical records. CT images were reviewed and scored for lesion distribution, lobe and segment involvement, ground-glass opacities, consolidation, and interstitial thickening. All included patients with moderate NCP were observed for at least 14 days from admission to determine whether they exacerbated to severe NCP (progressive group) or not (stable group). CT and clinical characteristics between the two groups were compared, and multivariate logistic regression and sensitivity analyses were performed to identify the risk factors for developing severe NCP. Results: A total of 141 patients with moderate NCP were included, of which 15 (10.6%) patients developed severe NCP during hospitalization and assigned to the progressive group. Multivariate logistic regression analysis showed that higher neutrophil-to-lymphocyte ratio (NLR) (odds ratio [OR] and 95% confidence interval [CI], 1.26 [1.04-1.53]; P = 0.018) and CT severity score (OR and 95% CI, 1.25 [1.08-1.46]; P = 0.004) on admission were independent predictors for progression to severe NCP, and sensitivity analysis confirmed the consistent results in nonimported patients but not in imported patients. However, no significant difference in lung involvement was found on CT between imported and nonimported patients (all P > 0.05). Patients who were admitted more than 4 days from symptom onset tended to have more severe lung involvement. Spearman correlation analysis showed the close association between CT severity score and inflammatory indexes (r = 0.17~0.47, all P < 0.05). Conclusion: CT severity score was associated with inflammatory levels and higher NLR and CT severity score on admission were independent risk factors for short-term progression in patients with NCP outside Wuhan. Furthermore, early admission and surveillance by CT should be recommended to improve clinical outcomes.

Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics

BACKGROUND: Coronavirus disease‐2019 (COVID‐19) has a deleterious effect on several systems, including the cardiovascular system. We aim to systematically explore the association of COVID‐19 severity and mortality rate with the history of cardiovascular diseases and/or other comorbidities and cardiac injury laboratory markers. METHODS: The standardized mean difference (SMD) or odds ratio (OR) and 95% confidence intervals (CI) were applied to estimate pooled results from the 56 studies. The prognostic performance of cardiac markers for predicting adverse outcomes and to select the best cutoff threshold was estimated by ROC curve analysis. Decision tree analysis by combining cardiac markers with demographic and clinical features was applied to predict mortality and severity in COVID‐19 patients. RESULTS: A meta‐analysis of 17,794 patients showed patients with high cardiac troponin I (OR=5.22, 95%CI=3.73‐7.31, p<0.001) and AST levels (OR=3.64, 95%CI=2.84‐4.66, p<0.001) were more likely to develop adverse outcomes. High troponin I >13.75 ng/L combined with either advanced age >60 years or elevated AST level >27.72 U/L was the best model to predict poor outcomes. CONCLUSIONS: COVID‐19 severity and mortality are complicated by myocardial injury. Assessment of cardiac injury biomarkers may improve the identification of those patients at the highest risk and potentially lead to improved therapeutic approaches. This article is protected by copyright. All rights reserved.

Association of cardiac biomarkers and comorbidities with increased mortality, severity, and cardiac injury in COVID‐19 patients: A meta‐regression and Decision tree analysis