A novel simple scoring model for predicting severity of patients with SARS-CoV-2 infection

4C mortality score and COVID-19 mortality risk score: an analysis in four different age groups of an Italian population

To evaluate the prognostic stratification ability of 4C Mortality Score and COVID-19 Mortality Risk Score in different age groups. Retrospective study, including all patients, presented to the Emergency Department of the University Hospital Careggi, between February, 2020 and May, 2021, and admitted for SARS-CoV2. Patients were divided into four subgroups based on the quartiles of age distribution: patients < 57 years (G1, n = 546), 57-71 years (G2, n = 508), 72-81 years (G3, n = 552), and > 82 years (G4, n = 578). We calculated the 4C Mortality Score and COVID-19 Mortality Risk Score. The end-point was in-hospital mortality. In the whole population (age 68 ± 16 years), the mortality rate was 19% (n = 424), and increased with increasing age (G1: 4%, G2: 11%, G3: 22%, and G4: 39%, p < 0.001). Both scores were higher among non-survivors than survivors in all subgroups (4C-MS, G1: 6 [3-7] vs 3 [2-5]; G2: 10 [7-11] vs 7 [5-8]; G3: 11 [10-14] vs 10 [8-11]; G4: 13 [12-15] vs 11 [10-13], all p < 0.001; COVID-19 MRS, G1: 8 [7-9] vs 9 [9-11], G2: 10 [8-11] vs 11 [10-12]; G3: 11 [10-12] vs 12 [11-13]; G4: 11 [10-13] vs 13 [12-14], all p < 0.01). The ability of both scores to identify patients at higher risk of in-hospital mortality, was similar in different age groups (4C-MS: G1 0.77, G2 0.76, G3 0.68, G4 0.72; COVID-19 MRS: G1 0.67, G2 0.69, G3 0.69, G4 0.72, all p for comparisons between subgroups = NS). Both scores confirmed their good performance in predicting in-hospital mortality in all age groups, despite their different mortality rate.

Mixed storm in SARS-CoV-2 infection: A narrative review and new term in the Covid-19 era

Coronavirus disease 2019 (Covid-19) is caused by a novel severe acute respiratory syndrome coronavirus virus type 2 (SARS-CoV-2) leading to the global pandemic worldwide. Systemic complications in Covid-19 are mainly related to the direct SARS-CoV-2 cytopathic effects, associated hyperinflammation, hypercytokinemia, and the development of cytokine storm (CS). As well, Covid-19 complications are developed due to the propagation of oxidative and thrombotic events which may progress to a severe state called oxidative storm and thrombotic storm (TS), respectively. In addition, inflammatory and lipid storms are also developed in Covid-19 due to the activation of inflammatory cells and the release of bioactive lipids correspondingly. Therefore, the present narrative review aimed to elucidate the interrelated relationship between different storm types in Covid-19 and the development of the mixed storm (MS). In conclusion, SARS-CoV-2 infection induces various storm types including CS, inflammatory storm, lipid storm, TS and oxidative storm. These storms are not developing alone since there is a close relationship between them. Therefore, the MS seems to be more appropriate to be related to severe Covid-19 than CS, since it develops in Covid-19 due to the intricate interface between reactive oxygen species, proinflammatory cytokines, complement activation, coagulation disorders, and activated inflammatory signaling pathway.

Serum amyloid A—A potential therapeutic target for hyper-inflammatory syndrome associated with COVID-19

Serum amyloid-A (SAA) is associated with inflammatory disorders such as rheumatoid arthritis, Familial Mediterranean Fever, sarcoidosis, and vasculitis. There is accumulating evidence that SAA is a reliable biomarker for these autoinflammatory and rheumatic diseases and may contribute to their pathophysiology. Hyperinflammatory syndrome associated with COVID-19 is a complex interaction between infection and autoimmunity and elevation of SAA is strongly correlated with severity of the inflammation. In this review we highlight the involvement of SAA in these different inflammatory conditions, consider its potential role and discuss whether it could be a potential target for treatment of the hyperinflammatory state of COVID-19 with many potential advantages and fewer adverse effects. Additional studies linking SAA to the pathophysiology of COVID-19 hyper-inflammation and autoimmunity are needed to establish the causal relationship and the therapeutic potential of inhibitors of SAA activity.

Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: a Systematic Review and Meta-analysis

Abnormal immunological indicators associated with disease severity and mortality in patients with COVID-19 have been reported in several observational studies. However, there are marked heterogeneities in patient characteristics and research methodologies in these studies. We aimed to provide an updated synthesis of the association between immune-related indicators and COVID-19 prognosis. We conducted an electronic search of PubMed, Scopus, Ovid, Willey, Web of Science, Cochrane library, and CNKI for studies reporting immunological and/or immune-related parameters, including hematological, inflammatory, coagulation, and biochemical variables, tested on hospital admission of COVID-19 patients with different severities and outcomes. A total of 145 studies were included in the current meta-analysis, with 26 immunological, 11 hematological, 5 inflammatory, 4 coagulation, and 10 biochemical variables reported. Of them, levels of cytokines, including IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, IFN-γ, IgA, IgG, and CD4+ T/CD8+ T cell ratio, WBC, neutrophil, platelet, ESR, CRP, ferritin, SAA, D-dimer, FIB, and LDH were significantly increased in severely ill patients or non-survivors. Moreover, non-severely ill patients or survivors presented significantly higher counts of lymphocytes, monocytes, lymphocyte/monocyte ratio, eosinophils, CD3+ T,CD4+T and CD8+T cells, B cells, and NK cells. The currently updated meta-analysis primarily identified a hypercytokinemia profile with the severity and mortality of COVID-19 containing IL-1β, IL-1Ra, IL-2R, IL-4, IL-6, IL-8, IL-10, IL-18, TNF-α, and IFN-γ. Impaired innate and adaptive immune responses, reflected by decreased eosinophils, lymphocytes, monocytes, B cells, NK cells, T cells, and their subtype CD4+ and CD8+ T cells, and augmented inflammation, coagulation dysfunction, and nonpulmonary organ injury, were marked features of patients with poor prognosis. Therefore, parameters of immune response dysfunction combined with inflammatory, coagulated, or nonpulmonary organ injury indicators may be more sensitive to predict severe patients and those non-survivors.

A novel scoring system for early assessment of the risk of the COVID-19-associated mortality in hospitalized patients: COVID-19 BURDEN

BACKGROUND

Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission.

METHODS

We retrospectively included 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses.

RESULTS

A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively.

CONCLUSIONS

Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.

A novel intubation prediction model for patients hospitalized with COVID-19: the OTO-COVID-19 scoring model

OBJECTIVE

The method for predicting the risk of intubation in patients with coronavirus disease 2019 (COVID-19) is yet to be standardized. This study aimed to introduce a new disease prognosis scoring model that may predict the intubation risk based on the symptoms, signs, and laboratory tests of patients hospitalized with the diagnosis of COVID-19.

METHOD

This cross-sectional retrospective study analyzed the intubation status of 733 patients hospitalized with COVID-19 diagnosis between March and December 2020 at Ondokuz Mayıs University Faculty of Medicine, Turkey, based on 33 variables. Binary logistic regression analysis was used to select the variables that significantly affect intubation, which constitute the risk factors. The Chi-square Automatic Interaction Detection algorithm, one of the data mining methods, was used to determine the threshold values of the important variables for intubation classification.

RESULTS

The following variables found were mostly associated with intubation: C-reactive protein, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, age, lymphocyte count, and malignancy. The logistic function based on these variables correctly predicted 81.13% of intubated (sensitivity), 99.52% of nonintubated (specificity), and 96.86% of both intubated and nonintubated (accurate classification rate) patients. The scoring model revealed the following risk statuses for the intubated patients: very high risk, 75.47%; moderate risk, 20.75%; and very low risk, 3.77%.

CONCLUSIONS

On the basis of certain variables measured at admission, the OTO-COVID-19 scoring model may help clinicians identify patients at the risk of intubation and subsequently provide a prompt and effective treatment at the earliest.

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (P=0.0017). We also found a nontrivial correlation between predicted severity probability and cycle threshold (r = -0.199). Finally, when divided into severity probability quartiles, the group most likely to experience severe illness (≥75% probability) had a Ct of 16.6 (n = 10), whereas the group least likely to experience severe illness (<25% probability) had a Ct of 21.4 (n = 350) (P=0.0045). Taken together, our results suggest that the severity predicted by a genome-based algorithm can be related to clinical diagnostic tests and that relative severity may be inferred from diagnostic values.

Epidemiological Consequences of Viral Interference: A Mathematical Modeling Study of Two Interacting Viruses

Some viruses have the ability to block or suppress growth of other viruses when simultaneously present in the same host. This type of viral interference or viral block has been suggested as a potential interaction between some respiratory viruses including SARS-CoV-2 and other co-circulating respiratory viruses. We explore how one virus' ability to block infection with another within a single host affects spread of the viruses within a susceptible population using a compartmental epidemiological model. We find that population-level effect of viral block is a decrease in the number of people infected with the suppressed virus. This effect is most pronounced when the viruses have similar epidemiological parameters. We use the model to simulate co-circulating epidemics of SARS-CoV-2 and influenza, respiratory syncytial virus (RSV), and rhinovirus, finding that co-circulation of SARS-CoV-2 and RSV causes the most suppression of SARS-CoV-2. Paradoxically, co-circulation of SARS-CoV-2 and influenza or rhinovirus results in almost no change in the SARS-CoV-2 epidemic, but causes a shift in the timing of the influenza and rhinovirus epidemics.

Urinalysis, but Not Blood Biochemistry, Detects the Early Renal Impairment in Patients with COVID-19

Background: Coronavirus 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2), has created a tremendous economic and medical burden. The prevalence and prognostic value of SARS-CoV-2-induced kidney impairment remain controversial. The current study aimed to provide additional evidence on the incidence of acute kidney injury (AKI) in COVID-19 patients and propose the use of urinalysis as a tool for screening kidney impairment. Methods: 178 patients with confirmed COVID-19 were enrolled in this retrospective cohort study. The laboratory examinations included routine blood tests, blood biochemical analyses (liver function, renal function, lipids, and glucose), blood coagulation index, lymphocyte subset and cytokine analysis, urine routine test, C-reactive protein, erythrocyte sedimentation, and serum ferritin. Results: No patient exhibited a rise in serum creatinine or Cystatin C and occurrence of AKI, and only 2.8% of patients were recorded with an elevated level of blood urea nitrogen among all cases. On the contrary, 54.2% of patients who underwent routine urine testing presented with an abnormal urinalysis as featured by proteinuria, hematuria, and leucocyturia. Conclusions: Kidney impairment is prevalent among COVID-19 patients, with an abnormal urinalysis as a clinical manifestation, implying that a routine urine test is a stronger indication of prospective kidney complication than a blood biochemistry test.

Potential Use of Serum Proteomics for Monitoring COVID-19 Progression to Complement RT-PCR Detection

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.

Hemostatic System (Fibrinogen Level, D-Dimer, and FDP) in Severe and Non-Severe Patients With COVID-19: A Systematic Review and Meta-Analysis

SARS-CoV-2 in COVID-19 triggers abnormalities in coagulation parameters that can contribute to thrombosis. The goals of this research were to determine the levels of fibrinogen, D-dimer and FDP in COVID-19 patients. Following a systematic study, among 1198 articles, 35 studies were included in the meta-analysis of fibrinogen levels in both severe and non-severe groups. The funnel plot, Egger’s regression asymmetry test, and Begg’s test used to measure the bias of publications. All meta-analysis performed by comprehensive meta-analysis version 2 (CMA2). The pooled findings of fibrinogen levels revealed a significant rise in fibrinogen levels in severe COVID-19 than non-severe patients with COVID-19. The D-dimer and FDP levels were significantly higher in severe patients than non-severe patients with COVID-19 were. The levels of fibrinogen, D-dimer, and FDP have increased significantly in ICU patients compared to non-ICU patients. Although, levels of clotting parameters do not always correlate with the severity of disease, these findings showed the diagnostic importance for fibrinogen, D-dimer, and FDP in COVID-19. The presence of a continuous rise in serial measurements of fibrinogen, D-dimer, and FDP may predict that patients with COVID-19 may become critically ill.

Effectiveness of COVID-19 vaccines and their challenges (Review)

At the end of 2019, a new disease recognized such as severe acute respiratory syndrome (SARS), was reported in Wuhan, China. This disease was caused by an unknown SARS coronavirus 2 (SARS-CoV-2); a virus is characterized by high infectivity among humans. In some cases, this disease can be asymptomatic, while in other cases can induce flu-like symptoms or acute respiratory distress syndrome, pneumonia and death. For this reason, the World Health Organization and Public Health Emergency of International Concern declared a pandemic status in January 2020. Currently, numerous countries have been involved in the development of effective vaccines to protect humans against SARS-CoV-2 infection. The present review will discuss the four vaccines, AZD1222 (AstraZeneca or Vaxzevria), Janssen (Ad26.COV2.S), Moderna/mRNA-1273 and BioNTech/Fosun/Pfizer BNT162b1, that are currently in use worldwide to understand their efficacy, but also evaluate the difficulties and challenges of vaccine development. Although several questions should be addressed regarding these vaccines, the current review will examine the viral elements used in the coronavirus-19 vaccine that can play a crucial role in inducing a strong immune response, as well as the different adverse effects that they can cause to individuals.

Serum hydroxybutyrate dehydrogenase and COVID-19 severity and mortality: a systematic review and meta-analysis with meta-regression

Alterations in cardiac and renal biomarkers have been reported in coronavirus disease 19 (COVID-19). We conducted a systematic review and meta-analysis to investigate serum concentrations of hydroxybutyrate dehydrogenase (HBDH), a combined marker of myocardial and renal injury, in hospitalized COVID-19 patients with different disease severity and survival status. We searched PubMed, Web of Science and Scopus, between December 2019 and April 2021, for studies reporting HBDH in COVID-19. Risk of bias was assessed using the Newcastle–Ottawa scale, publication bias was assessed with the Begg’s and Egger’s tests, and certainty of evidence was assessed using GRADE. In 22 studies in 15,019 COVID-19 patients, serum HBDH concentrations on admission were significantly higher in patients with high disease severity or non-survivor status when compared to patients with low severity or survivor status (standardized mean difference, SMD = 0.90, 95% CI 0.74 to 1.07, p < 0.001; moderate certainty of evidence). Extreme between-study heterogeneity was observed (I² = 93.5%, p < 0.001). Sensitivity analysis, performed by sequentially removing each study and re-assessing the pooled estimates, showed that the magnitude and the direction of the effect size were not substantially modified. A significant publication bias was observed. In meta-regression, the SMD of HBDH concentrations was significantly associated with markers of inflammation, sepsis, liver damage, non-specific tissue damage, myocardial injury, and renal function. Higher HBDH concentrations were significantly associated with higher COVID-19 severity and mortality. This biomarker of cardiac and renal injury might be useful for risk stratification in COVID-19. (PROSPERO registration number: CRD42021258123).

D-dimer, disease severity, and deaths (3D-study) in patients with COVID-19: a systematic review and meta-analysis of 100 studies

Hypercoagulability and the need for prioritizing coagulation markers for prognostic abilities have been highlighted in COVID-19. We aimed to quantify the associations of D-dimer with disease progression in patients with COVID-19. This systematic review and meta-analysis was registered with PROSPERO, CRD42020186661.We included 113 studies in our systematic review, of which 100 records (n = 38,310) with D-dimer data) were considered for meta-analysis. Across 68 unadjusted (n = 26,960) and 39 adjusted studies (n = 15,653) reporting initial D-dimer, a significant association was found in patients with higher D-dimer for the risk of overall disease progression (unadjusted odds ratio (uOR) 3.15; adjusted odds ratio (aOR) 1.64). The time-to-event outcomes were pooled across 19 unadjusted (n = 9743) and 21 adjusted studies (n = 13,287); a strong association was found in patients with higher D-dimers for the risk of overall disease progression (unadjusted hazard ratio (uHR) 1.41; adjusted hazard ratio (aHR) 1.10). The prognostic use of higher D-dimer was found to be promising for predicting overall disease progression (studies 68, area under curve 0.75) in COVID-19. Our study showed that higher D-dimer levels provide prognostic information useful for clinicians to early assess COVID-19 patients at risk for disease progression and mortality outcomes. This study, recommends rapid assessment of D-dimer for predicting adverse outcomes in COVID-19.

Development and validation of a clinical prediction model to estimate the risk of critical patients with COVID-19

The outbreak of coronavirus disease 2019 (COVID‐19) has globally strained medical resources and caused significant mortality. This study was aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID‐19 at admission progressing to critical patients. Patients admitted to the hospital between January 16, 2020, and March 10, 2020, were retrospectively enrolled, and they were observed for at least 14 days after admission to determine whether they developed into severe pneumonia. According to the clinical symptoms, all patients were divided into four groups: mild, normal, severe, and critical. A total of 390 patients with COVID‐19 pneumonia were identified, including 212 severe patients and 178 nonsevere patients. The least absolute shrinkage and selection operator (LASSO) regression reduced the variables in the model to 6, which are age, number of comorbidities, computed tomography severity score, lymphocyte count, aspartate aminotransferase, and albumin. The area under curve of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%. The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID‐19 patients at admission, which is instructive for clinical diagnosis.

Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.

INR and COVID-19 severity and mortality: A systematic review with meta-analysis and meta-regression

Objectives

D-dimer elevations, suggesting a pro-thrombotic state and coagulopathy, predict adverse outcomes in coronavirus disease 2019 (COVID-19). However, the clinical significance of other coagulation markers, particularly the international normalized ratio (INR), is not well established. We conducted a systematic review and meta-analysis of the INR in COVID-19.

Methods

A literature search was conducted in PubMed, Web of Science and Scopus, between January 2020 and February 2021, for studies reporting INR values, measures of COVID-19 severity, and mortality (PROSPERO registration number: CRD42021241468).

Results

Thirty-eight studies in 7440 COVID-19 patients with low disease severity or survivor status during follow up (50 ​% males, mean age 57 years) and 2331 with high severity or non-survivor status (60 ​% males, mean age 69 years) were identified. The INR was significantly prolonged in patients with severe disease or non-survivor status than in patients with mild disease or survivor status (standard mean difference, SMD, 0.60; 95 ​% confidence interval, CI 0.42 to 0.77; p ​< ​0.001). There was extreme between-study heterogeneity (I² ​= ​90.2 ​%; p ​< ​0.001). Sensitivity analysis, performed by sequentially removing each study and re-assessing the pooled estimates, showed that the magnitude and direction of the effect size was not modified. The Begg's and Egger's t-tests did not show publication bias. In meta-regression, the SMD of the INR was significantly associated with C-reactive protein (p ​= ​0.048) and D-dimer (p ​= ​0.001).

Conclusions

Prolonged INR values were significantly associated with COVID-19 severity and mortality. Both INR prolongation and D-dimer elevations can be useful in diagnosing COVID-19-associated coagulopathy and predicting clinical outcomes.

Role of Peptides in Diagnostics

The specificity of a diagnostic assay depends upon the purity of the biomolecules used as a probe. To get specific and accurate information of a disease, the use of synthetic peptides in diagnostics have increased in the last few decades, because of their high purity profile and ability to get modified chemically. The discovered peptide probes are used either in imaging diagnostics or in non-imaging diagnostics. In non-imaging diagnostics, techniques such as Enzyme-Linked Immunosorbent Assay (ELISA), lateral flow devices (i.e., point-of-care testing), or microarray or LC-MS/MS are used for direct analysis of biofluids. Among all, peptide-based ELISA is considered to be the most preferred technology platform. Similarly, peptides can also be used as probes for imaging techniques, such as single-photon emission computed tomography (SPECT) and positron emission tomography (PET). The role of radiolabeled peptides, such as somatostatin receptors, interleukin 2 receptor, prostate specific membrane antigen, αβ3 integrin receptor, gastrin-releasing peptide, chemokine receptor 4, and urokinase-type plasminogen receptor, are well established tools for targeted molecular imaging ortumor receptor imaging. Low molecular weight peptides allow a rapid clearance from the blood and result in favorable target-to-non-target ratios. It also displays a good tissue penetration and non-immunogenicity. The only drawback of using peptides is their potential low metabolic stability. In this review article, we have discussed and evaluated the role of peptides in imaging and non-imaging diagnostics. The most popular non-imaging and imaging diagnostic platforms are discussed, categorized, and ranked, as per their scientific contribution on PUBMED. Moreover, the applicability of peptide-based diagnostics in deadly diseases, mainly COVID-19 and cancer, is also discussed in detail.

Severity-associated markers and assessment model for predicting the severity of COVID-19: a retrospective study in Hangzhou, China

BACKGROUND

The severity of COVID-19 associates with the clinical decision making and the prognosis of COVID-19 patients, therefore, early identification of patients who are likely to develop severe or critical COVID-19 is critical in clinical practice. The aim of this study was to screen severity-associated markers and construct an assessment model for predicting the severity of COVID-19.

METHODS

172 confirmed COVID-19 patients were enrolled from two designated hospitals in Hangzhou, China. Ordinal logistic regression was used to screen severity-associated markers. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed for further feature selection. Assessment models were constructed using logistic regression, ridge regression, support vector machine and random forest. The area under the receiver operator characteristic curve (AUROC) was used to evaluate the performance of different models. Internal validation was performed by using bootstrap with 500 re-sampling in the training set, and external validation was performed in the validation set for the four models, respectively.

RESULTS

Age, comorbidity, fever, and 18 laboratory markers were associated with the severity of COVID-19 (all P values < 0.05). By LASSO regression, eight markers were included for the assessment model construction. The ridge regression model had the best performance with AUROCs of 0.930 (95% CI, 0.914-0.943) and 0.827 (95% CI, 0.716-0.921) in the internal and external validations, respectively. A risk score, established based on the ridge regression model, had good discrimination in all patients with an AUROC of 0.897 (95% CI 0.845-0.940), and a well-fitted calibration curve. Using the optimal cutoff value of 71, the sensitivity and specificity were 87.1% and 78.1%, respectively. A web-based assessment system was developed based on the risk score.

CONCLUSIONS

Eight clinical markers of lactate dehydrogenase, C-reactive protein, albumin, comorbidity, electrolyte disturbance, coagulation function, eosinophil and lymphocyte counts were associated with the severity of COVID-19. An assessment model constructed with these eight markers would help the clinician to evaluate the likelihood of developing severity of COVID-19 at admission and early take measures on clinical treatment.

The dynamics of inflammatory markers in coronavirus disease-2019 (COVID-19) patients: A systematic review and meta-analysis

BACKGROUND

Coronavirus disease-2019 (COVID-19) is a global pandemic and high mortality rate among severe or critical COVID-19 is linked with SARS-CoV-2 infection-induced hyperinflammation of the innate and adaptive immune systems and the resulting cytokine storm. This paper attempts to conduct a systematic review and meta-analysis of published articles, to evaluate the association of inflammatory parameters with the severity and mortality in COVID-19 patients.

METHODS

A comprehensive systematic literature search of medical electronic databases including Pubmed/Medline, Europe PMC, and Google Scholar was performed for relevant data published from January 1, 2020 to June 26, 2020. Observational studies reporting clear extractable data on inflammatory parameters in laboratory-confirmed COVID-19 patients were included. Screening of articles, data extraction and quality assessment were carried out by two authors independently. Standardized mean difference (SMD)/mean difference (MD/WMD) and 95% confidence intervals (CIs) were calculated using random or fixed-effects models.

RESULTS

A total of 83 studies were included in the meta-analysis. Of which, 54 studies were grouped by severity, 25 studies were grouped by mortality, and 04 studies were grouped by both severity and mortality. Random effect model results demonstrated that patients with severe COVID-19 group had significantly higher levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), interleukin-6 (IL-6), interleukin-10 (IL-10), interleukin-2R (IL-2R), serum amyloid A (SAA) and neutrophil-to-lymphocyte ratio (NLR) compared to those in the non-severe group. Similarly, the fixed-effect model revealed significant higher ferritin level in the severe group when compared with the non-severe group. Furthermore, the random effect model results demonstrated that the non-survivor group had significantly higher levels of CRP, PCT, IL-6, ferritin, and NLR when compared with the survivor group.

CONCLUSION

In conclusion, the measurement of these inflammatory parameters could help the physicians to rapidly identify severe COVID-19 patients, hence facilitating the early initiation of effective treatment.

PROSPERO REGISTRATION NUMBER

CRD42020193169.

The role of hemogram parameters and C-reactive protein in predicting mortality in COVID-19 infection

AIM

This study aimed to investigate hemogram parameters and C-reactive protein (CRP) that can be used in clinical practice to predict mortality in hospitalized patients with a diagnosis of COVID-19.

METHODS

This cohort study was conducted at University Hospital, which is a designated hospital for COVID-19 patients. Adult patients who were admitted to our hospital emergency department with suspected COVID-19 and who were hospitalized in our institution with a COVID-19 diagnosis were analysed.

RESULTS

There were 148 patients hospitalized with COVID-19. All-cause mortality of follow-up was 12.8%. There were statistically significant results between the two groups (survivors and nonsurvivors), which were classified based on hospital mortality rates, in terms of the lymphocyte to C-reactive protein ratio (LCRP), systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), CRP concentration and comorbid disease. In a receiver operating characteristic (ROC), curve analysis, LCRP, NLR, PLR and SII area under the curve (AUC) for in-hospital mortality were 0.817, 0.816, 0.733 and 0.742, respectively. Based on an LCRP value of 1 for in-hospital mortality, the sensitivity and specificity rates were 100% and 86.8%, respectively. Based on the average SII of 2699 for in-hospital mortality, the sensitivity, specificity and accuracy rates were 68.4%, 77.5% and 76.3%, respectively. A total of 19 patients died during hospitalization. All of these patients had an LCRP level ≤ 1; 14 had an NLR level ≤ 10.8; 13 had an SII ≥ 2699 (Fisher's exact test, P = .000). Independent predictors of in-hospital mortality rates were LCRP < 1, PLR, SII ≥ 2699, white blood cell count, CRP, age, comorbidities, and ICU stay.

CONCLUSIONS

We concluded that inflammatory parameters, such as LRCP, SII and NLR, were associated with disease severity and could be used as potentially important risk factors for COVID-19 progression.

Risk factors for severity on admission and the disease progression during hospitalisation in a large cohort of patients with COVID-19 in Japan

OBJECTIVES

To investigate the risk factors contributing to severity on admission. Additionally, risk factors of worst severity and fatality were studied. Moreover, factors were compared based on three points: early severity, worst severity and fatality.

DESIGN

An observational cohort study using data entered in a Japan nationwide COVID-19 inpatient registry, COVIREGI-JP.

SETTING

As of 28 September 2020, 10480 cases from 802 facilities have been registered. Participating facilities cover a wide range of hospitals where patients with COVID-19 are admitted in Japan.

PARTICIPANTS

Participants who had a positive test result on any applicable SARS-CoV-2 diagnostic tests were admitted to participating healthcare facilities. A total of 3829 cases were identified from 16 January to 31 May 2020, of which 3376 cases were included in this study.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcome was severe or nonsevere on admission, determined by the requirement of mechanical ventilation or oxygen therapy, SpO2 or respiratory rate. Secondary outcome was the worst severity during hospitalisation, judged by the requirement of oxygen and/orinvasive mechanical ventilation/extracorporeal membrane oxygenation.

RESULTS

Risk factors for severity on admission were older age, men, cardiovascular disease, chronic respiratory disease, diabetes, obesity and hypertension. Cerebrovascular disease, liver disease, renal disease or dialysis, solid tumour and hyperlipidaemia did not influence severity on admission; however, it influenced worst severity. Fatality rates for obesity, hypertension and hyperlipidaemia were relatively lower.

CONCLUSIONS

This study segregated the comorbidities influencing severity and death. It is possible that risk factors for severity on admission, worst severity and fatality are not consistent and may be propelled by different factors. Specifically, while hypertension, hyperlipidaemia and obesity had major effect on worst severity, their impact was mild on fatality in the Japanese population. Some studies contradict our results; therefore, detailed analyses, considering in-hospital treatments, are needed for validation.

TRIAL REGISTRATION NUMBER

UMIN000039873. https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000045453.

Predictive Model of Severity in SARS CoV-2 Patients at Hospital Admission Using Blood-Related Parameters

INTRODUCTION

Blood test alterations are crucial in SARS CoV-2 (COVID-19) patients. Blood parameters, such as lymphocytes, C reactive protein (CRP), creatinine, lactate dehydrogenase, or D-dimer, are associated with severity and prognosis of SARS CoV-2 patients. This study aims to identify blood-related predictors of severe hospitalization in patients diagnosed with SARS CoV-2.

METHODS

Observational retrospective study of all rt-PCR and blood-test positive (at 48 hours of hospitalization) SARS CoV-2 diagnosed inpatients between March-May 2020. Deceased and/or ICU inpatients were considered as severe cases, whereas those patients after hospital discharge were considered as non-severe. Multivariate logistic regression was used to identify predictors of severity, based on bivariate contrast between severe and mild inpatients.

RESULTS

The overall sample comprised 540 patients, with 374 mild cases (69.26%), and 166 severe cases (30.75%). The multivariate logistic regression model for predicting SARS CoV-2 severity included lymphocytes, C reactive protein (CRP), creatinine, total protein levels, glucose and aspartate aminotransferase as predictors, showing an area under the curve (AUC) of 0.895 at a threshold of 0.29, with 81.5% of sensitivity and 81% of specificity.

DISCUSSION

Our results suggest that our predictive model allows identifying and stratifying SARS CoV-2 patients in risk of developing severe medical complications based on blood-test parameters easily measured at hospital admission, improving health-care resources management and distribution.

Development and Validation of a Predictive Model for Severe COVID-19: A Case-Control Study in China

Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources.

Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer–Lemeshow test.

Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/.

Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.

Using data mining techniques to fight and control epidemics: A scoping review

The main objective of this survey is to study the published articles to determine the most favorite data mining methods and gap of knowledge. Since the threat of pandemics has raised concerns for public health, data mining techniques were applied by researchers to reveal the hidden knowledge. Web of Science, Scopus, and PubMed databases were selected for systematic searches. Then, all of the retrieved articles were screened in the stepwise process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist to select appropriate articles. All of the results were analyzed and summarized based on some classifications. Out of 335 citations were retrieved, 50 articles were determined as eligible articles through a scoping review. The review results showed that the most favorite DM belonged to Natural language processing (22%) and the most commonly proposed approach was revealing disease characteristics (22%). Regarding diseases, the most addressed disease was COVID-19. The studies show a predominance of applying supervised learning techniques (90%). Concerning healthcare scopes, we found that infectious disease (36%) to be the most frequent, closely followed by epidemiology discipline. The most common software used in the studies was SPSS (22%) and R (20%). The results revealed that some valuable researches conducted by employing the capabilities of knowledge discovery methods to understand the unknown dimensions of diseases in pandemics. But most researches will need in terms of treatment and disease control.

Extracting COVID-19 diagnoses and symptoms from clinical text: A new annotated corpus and neural event extraction framework

Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.

Serum amyloid A concentrations, COVID-19 severity and mortality: An updated systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

An excessive inflammatory response in patients with coronavirus disease 2019 (COVID-19) is associated with high disease severity and mortality. Specific acute phase reactants might be useful for risk stratification. A systematic review and meta-analysis was conducted of studies on serum amyloid A (SAA) in patients with COVID-19.

METHODS

The PubMed, Web of Science, and Scopus databases were searched, covering the period January 2020 to December 2020, for studies reporting SAA concentrations, COVID-19 severity, and survival status.

RESULTS

Nineteen studies involving 5617 COVID-19 patients were included in the meta-analysis. Pooled results showed that SAA concentrations were significantly higher in patients with severe disease and non-survivors (standard mean difference (SMD) 1.20, 95% confidence interval 0.91-1.49, P < 0.001). Extreme between-study heterogeneity was observed (I² = 92.4%, P < 0.001). In the sensitivity analysis, the effect size was not significantly affected when each study was removed in turn (range 1.10-1.29). The Begg test (P = 0.030), but not the Egger test (P = 0.385), revealed the presence of publication bias. Pooled SMD values were significantly and positively associated with sex (t = 2.20, P = 0.047) and aspartate aminotransferase (t = 3.44, P = 0.014).

CONCLUSIONS

SAA concentrations were significantly and positively associated with higher COVID-19 severity and mortality. This acute phase reactant might assist with risk stratification and monitoring in this group.

Level up for culture models - How 3D cell culture models benefit SARS-CoV-2 research

Welcome to a new decade and a new issue of the Biomedical Journal - casting a sorrowful look onto a year that will go down in history as a tombstone etched by the COVID-19 pandemic, but also a hopeful glance into the future, now that multiple vaccination programs against the SARS-CoV-2 virus have started. This issue is dedicated to the continuous effort by researchers all around the globe to understand and counter the pathogen, as well as to be better prepared for future threats. Therefore, we learn about the advantages of complex 3D cell culture models for studying host-virus interactions, and the disease course of COVID-19 in children. Moreover, we discover how neutralising monoclonal antibodies and peptide-based vaccines against SARS-CoV-2 are developed, and the therapeutic potentials of lopinavir/ritonavir, mesenchymal stem cells, as well as plant and algae extracts. Finally, we ponder over the lessons to be learnt from SARS-CoV and MERS, and hear about differences between nucleotide-based SARS-CoV-2 detection methods.

Impact of low molecular weight heparin administration on the clinical course of the COVID-19 disease

Background

Lymphopenia is the most important criterion of mortality and discharging feature for patients infected with coronavirus disease 2019 (COVID-19). This study aimed to investigate the clinical impact of a low molecular weight heparin (LMWH) treatment on the clinical course of COVID-19.

Materials and methods

Patients’ clinical symptoms, radiologic outcomes, hematologic, biochemical, D-dimer, and C-reactive protein (CRP) results were obtained from their medical records. Participants were separated into 2 groups: one was treated with LMWH and the other was not. Improvement in the patients was compared before and after treatment.

Results

Ninety-six patients who were diagnosed with COVID-19 between April and May 2020 were retrospectively analyzed. The multivariable analysis showed that the count of lymphocytes, D-dimer, and CRP levels were significantly improved in the LMWH group, as compared to the control group (OR, (95% CI) 0.628 (0.248–0.965), P < 0.001); OR, (95% CI) 0.356 (0.089–0.674), P < 0.001, respectively). The area under the receiver operating characteristic (ROC) curve analysis was AUC: 0.679 ± 0.055, 0.615 ± 0.058, and 0.633 ± 0.057, respectively; the β-value was found to be –1.032, –0.026, and –0.465, respectively.

Conclusion

The LMWH treatment group demonstrated better laboratory findings, including recovery in the lymphocyte count, CRP, and D-dimer results.

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia

Background

To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness.

Methods

A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC).

Results

The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913–1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757–1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906–1.000).

Conclusions

A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

Pragmatic Recommendations for Identification and Triage of Patients with COVID-19 Disease in Low- and Middle-Income Countries

Effective identification and prognostication of severe COVID-19 patients presenting to healthcare facilities are essential to reducing morbidity and mortality. Low- and middle-income country (LMIC) facilities often suffer from restrictions in availability of human resources, laboratory testing, medications, and imaging during routine functioning, and such shortages may worsen during times of surge. Low- and middle-income country healthcare providers will need contextually appropriate tools to identify and triage potential COVID-19 patients. We report on a series of LMIC-appropriate recommendations and suggestions for screening and triage of COVID-19 patients in LMICs, based on a pragmatic, experience-based appraisal of existing literature. We recommend that all patients be screened upon first contact with the healthcare system using a locally approved questionnaire to identify individuals who have suspected or confirmed COVID-19. We suggest that primary screening tools used to identify individuals who have suspected or confirmed COVID-19 include a broad range of signs and symptoms based on standard case definitions of COVID-19 disease. We recommend that screening include endemic febrile illness per routine protocols upon presentation to a healthcare facility. We recommend that, following screening and implementation of appropriate universal source control measures, suspected COVID-19 patients be triaged with a triage tool appropriate for the setting. We recommend a standardized severity score based on the WHO COVID-19 disease definitions be assigned to all suspected and confirmed COVID-19 patients before their disposition from the emergency unit. We suggest against using diagnostic imaging to improve triage of reverse transcriptase (RT)-PCR–confirmed COVID-19 patients, unless a patient has worsening respiratory status. We suggest against the use of point-of-care lung ultrasound to improve triage of RT-PCR–confirmed COVID-19 patients. We suggest the use of diagnostic imaging to improve sensitivity of appropriate triage in suspected COVID-19 patients who are RT-PCR negative but have moderate to severe symptoms and are suspected of a false-negative RT-PCR with high risk of disease progression. We suggest the use of diagnostic imaging to improve sensitivity of appropriate triage in suspected COVID-19 patients with moderate or severe clinical features who are without access to RT-PCR testing for SARS-CoV-2.

Severe COVID-19: A distinct entity

Severe coronavirus disease-2019 (COVID-19) is a distinct entity that rapidly evolves and may abruptly culminate in to a critical illness. As per Chinese experience, approximately, 15% of patients of COVID-19 progress to severe disease and 5% become critically ill. The incidence of severe and critical illness is higher among men, patients older than 65 years of age and in persons with other medical comorbidities. Cytokine storm cause pronounced lung damage and multiorgan failure. Coagulopathy is a key component of severe COVID-19. Critically ill patients are generally predisposed to a high risk of thromboembolism as well. Lymphopenia predisposes to severe disease. None of the antiviral or immunomodulators has proven efficacy in severe COVID-19. Supplemental oxygen need be administered in patients with hypoxemia. Excessive breathing effort, acute respiratory distress syndrome (ARDS), encephalopathy, and multiorgan failure are indications for mechanical ventilation. In a large number of patients, the overall outcome is poor. Health care workers in intensive care units are exposed to the enormous risk of acquiring hospital acquired SARS-COV-2 infection.

Using Cardiac Magnetic Resonance Imaging to Evaluate Patients with Chest Pain in the Emergency Department

Chest pain is one of the most common presenting symptoms in the emergency department (ED). Among patients with abnormal troponins, it is imperative to quickly and accurately distinguish type 1 acute myocardial infarction (AMI) from other etiologies of myocardial injury. Although high-sensitivity troponin assays introduced a high negative predictive value for AMI, they have exposed the need for diagnostic modalities that can determine the etiology of acute myocardial injury. Cardiac magnetic resonance imaging (CMR) is an effective tool to risk stratifying chest pain among patients in the ED. CMR is non-invasive and has a lower cost of care and shorter length of stay compared to those of invasive coronary angiography. It also provides detailed information on cardiac morphology, function, tissue edema, and location and pattern of tissue damage that can help to differentiate many etiologies of cardiac injury. CMR is particularly useful to distinguish chest pain due to type 1 AMI versus supply-demand mismatch due to acute cardiac noncoronary artery disease. A detailed review of the literature has shown that CMR with stress testing is safe to use in patients presenting to the ED with chest pain, with or without abnormal troponins. CMR is a useful, safe, economical, and effective alternative to the traditional diagnostic tools that are typically used in this patient population. It is a practical tool to risk-stratify patients with possible cardiac pathology and to clarify diagnosis without invasive testing.

Features of α-HBDH in COVID-19 patients: A cohort study

Background

Coronavirus disease‐2019 (COVID‐19) has spread all over the world and brought extremely huge losses. At present, there is a lack of study to systematically analyze the features of hydroxybutyrate dehydrogenase (α‐HBDH) in COVID‐19 patients.

Methods

Electronic medical records including demographics, clinical manifestation, α‐HBDH results and outcomes of all included patients were extracted.

Results

α‐HBDH in COVID‐19 group was higher than that in excluded group (p < 0.001), and there was no significant difference in α‐HBDH before and after the exclusion of 5 patients with comorbidity in heart or kidney (p = 0.671). In COVID‐19 group, the α‐HBDH value in ≥61 years old group, severe group, and critical group, death group all increased at first and then decreased, while no obvious changes were observed in other groups. And there were significant differences of the α‐HBDH value among different age groups (p < 0.001), clinical type groups (p < 0.001), and outcome groups (p < 0.001). The optimal scale regression model showed that α‐HBDH value (p < 0.001) and age (p < 0.001) were related to clinical type.

Conclusions

α‐HBDH was increased in COVID‐19 patients, obviously in ≥61 years old, death and critical group, indicating that patients in these three groups suffer from more serious heart and kidney and other tissues and organs damage, higher α‐HBDH value, and risk of death. The difference between death and survival group in early stage might provide a approach to judge the prognosis. The accuracy of the model to distinguish severe/critical type and other types was 85.84%, suggesting that α‐HBDH could judge the clinical type accurately.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.

Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.

Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.

Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

Where are we with understanding of COVID-19?

Coronavirus disease 2019 caused by SARS-CoV-2 originated from China and spread across every corner of the world. The scientific interest on COVID-19 increased after WHO declared it a pandemic in the early February of 2020. In fact, this pandemic has had a worldwide impact on economy, health, and lifestyle like no other in the last 100 years. SARS-CoV-2 belongs to Coronaviridae family and causes the deadliest clinical manifestations when compared to other viruses in the family. COVID-19 is an emerging zoonotic disease that has resulted in over 383,000 deaths around the world. Scientists are scrambling for ideas to develop treatment and prevention strategies to thwart the disease condition. In this review, we have attempted to summarize the latest information on the virus, disease, prevention, and treatment strategies. The future looks promising.

A systematic meta-analysis of immune signatures in patients with COVID-19

Currently severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) transmission has been on the rise worldwide. Predicting outcome in COVID‐19 remains challenging, and the search for more robust predictors continues. We made a systematic meta‐analysis on the current literature from 1 January 2020 to 15 August 2020 that independently evaluated 32 circulatory immunological signatures that were compared between patients with different disease severity was made. Their roles as predictors of disease severity were determined as well. A total of 149 distinct studies that evaluated ten cytokines, four antibodies, four T cells, B cells, NK cells, neutrophils, monocytes, eosinophils and basophils were included. Compared with the non‐severe patients of COVID‐19, serum levels of Interleukins (IL)‐2, IL‐2R, IL‐4, IL‐6, IL‐8, IL‐10 and tumor necrosis factor α were significantly up‐regulated in severe patients, with the largest inter‐group differences observed for IL‐6 and IL‐10. In contrast, IL‐5, IL‐1β and Interferon (IFN)‐γ did not show significant inter‐group difference. Four mediators of T cells count, including CD3⁺ T, CD4⁺ T, CD8⁺ T, CD4⁺CD25⁺CD127^‐ Treg, together with CD19⁺ B cells count and CD16⁺CD56⁺ NK cells were all consistently and significantly depressed in severe group than in non‐severe group. SARS‐CoV‐2 specific IgA and IgG antibodies were significantly higher in severe group than in non‐severe group, while IgM antibody in the severe patients was slightly lower than those in the non‐severe patients, and IgE antibody showed no significant inter‐group differences. The combination of cytokines, especially IL‐6 and IL‐10, and T cell related immune signatures can be used as robust biomarkers to predict disease severity following SARS‐CoV‐2 infection.

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

Updates on laboratory investigations in coronavirus disease 2019 (COVID-19)

The coronavirus disease 2019 (COVID-19) pandemic is still spreading worldwide, affecting several million people. Unlike the previous two coronavirus outbreaks, COVID-19 has caused several thousand deaths for respiratory and multiple organ failure. As specifically concerns this latest infectious pathology, laboratory medicine can provide a substantial contribution to diagnosing an acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through molecular testing, establishing the presence and extent of an immune response against the virus, mostly through serological testing. However, it can also help to predict the risk of unfavorable disease progression by measuring some conventional laboratory tests and, last but not least, can provide reliable therapeutic guidance. This article is hence aimed at offering recent updates on the important role and value of laboratory investigations in COVID-19, also providing information on some hot topics such as virus RNA detection in different biological samples, causes of recurrent positivity of reverse-transcription polymerase chain reaction (RT-PCR), potential strategies for enhancing the throughput of molecular testing (i.e., pre-test probability assessment, sample pooling, use of rapid tests), as well as pragmatic indications for enhancing the quality and value of serological testing and laboratory-based monitoring.

Where are we with understanding of COVID-19?

Coronavirus disease 2019 caused by SARS-CoV-2 originated from China and spread across every corner of the world. The scientific interest on COVID-19 increased after WHO declared it a pandemic in the early February of 2020. In fact, this pandemic has had a worldwide impact on economy, health, and lifestyle like no other in the last 100 years. SARS-CoV-2 belongs to Coronaviridae family and causes the deadliest clinical manifestations when compared to other viruses in the family. COVID-19 is an emerging zoonotic disease that has resulted in over 383,000 deaths around the world. Scientists are scrambling for ideas to develop treatment and prevention strategies to thwart the disease condition. In this review, we have attempted to summarize the latest information on the virus, disease, prevention, and treatment strategies. The future looks promising.

SARS-CoV-2: characteristics and current advances in research

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection has spread rapidly across the world and become an international public health emergency. Both SARS-CoV-2 and SARS-CoV belong to subfamily Coronavirinae in the family Coronaviridae of the order Nidovirales and they are classified as the SARS-like species while belong to different cluster. Besides, viral structure, epidemiology characteristics and pathological characteristics are also different. We present a comprehensive survey of the latest coronavirus-SARS-CoV-2-from investigating its origin and evolution alongside SARS-CoV. Meanwhile, pathogenesis, cardiovascular disease in COVID-19 patients, myocardial injury and venous thromboembolism induced by SARS-CoV-2 as well as the treatment methods are summarized in this review.

Hemostatic Changes in Patients with COVID-19: A Meta-Analysis with Meta-Regressions

BACKGROUND

Complications of coronavirus disease 2019 (COVID-19) include coagulopathy. We performed a meta-analysis on the association of COVID-19 severity with changes in hemostatic parameters.

METHODS

Data on prothrombin time (PT), activated partial thromboplastin time (aPTT), D-Dimer, platelets (PLT), or fibrinogen in severe versus mild COVID-19 patients, and/or in non-survivors to COVID-19 versus survivors were systematically searched. The standardized mean difference (SMD) was calculated.

RESULTS

Sixty studies comparing 5487 subjects with severe and 9670 subjects with mild COVID-19 documented higher PT (SMD: 0.41; 95%CI: 0.21, 0.60), D-Dimer (SMD: 0.67; 95%CI: 0.52, 0.82), and fibrinogen values (SMD: 1.84; 95%CI: 1.21, 2.47), with lower PLT count (SMD: -0.74; 95%CI: -1.01, -0.47) among severe patients. Twenty-five studies on 1511 COVID-19 non-survivors and 6287 survivors showed higher PT (SMD: 0.67; 95%CI: 0.39, 0.96) and D-Dimer values (SMD: 3.88; 95%CI: 2.70, 5.07), with lower PLT count (SMD: -0.60, 95%CI: -0.82, -0.38) among non-survivors. Regression models showed that C-reactive protein values were directly correlated with the difference in PT and fibrinogen.

CONCLUSIONS

Significant hemostatic changes are associated with COVID-19 severity. Considering the risk of fatal complications with residual chronic disability and poor long-term outcomes, further studies should investigate the prognostic role of hemostatic parameters in COVID-19 patients.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.