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

Clinical Significance of CBC and WBC Morphology in the Diagnosis and Clinical Course of COVID-19 Infection

Objectives

To investigate the clinical significance of numeric and morphologic peripheral blood (PB) changes in coronavirus disease 2019 (COVID-19)–positive patients in predicting the outcome, as well as to compare these changes between critically ill COVID-19–positive and COVID-19–negative patients.

Methods

The study included 90 COVID-19–positive (51 intensive care unit [ICU] and 39 non-ICU) patients and 30 COVID-19–negative ICU patients. We collected CBC parameters (both standard and research) and PB morphologic findings, which were independently scored by two hematopathologists.

Results

All patients with COVID-19 demonstrated striking numeric and morphologic WBC changes, which were different between mild and severe disease states. More severe disease was associated with significant neutrophilia and lymphopenia, which was intensified in critically ill patients. Abnormal WBC morphology, most pronounced in monocytes and lymphocytes, was associated with more mild disease; the changes were lost with disease progression. Between COVID-19–positive and COVID-19–negative ICU patients, significant differences in morphology-associated research parameters were indicative of changes due to the severe acute respiratory syndrome coronavirus 2 virus, including higher RNA content in monocytes, lower RNA content in lymphocytes, and smaller hypogranular neutrophils.

Conclusions

Hospitalized patients with COVID-19 should undergo a comprehensive daily CBC with manual WBC differential to monitor for numerical and morphologic changes predictive of poor outcome and signs of disease progression.

Impact of cardiovascular diseases on severity of COVID-19 patients: A systematic review

ABSTRACT Introduction: Coronavirus disease 2019 (COVID-19) cases are increasing rapidly worldwide. Similar to Middle East respiratory syndrome where cardiovascular diseases were present in nearly 30% of cases, the increased presence of cardiovascular comorbidities remains true for COVID-19 as well. The mechanism of this association remains unclear at this time. Therefore, we reviewed the available literature and tried to find the probable association between cardiovascular disease with disease severity and mortality in COVID-19 patients. Methods: We searched Medline (via PubMed) and Cochrane Central Register of Controlled Trials for articles published until Sept 5, 2020. Nineteen articles were included involving 6,872 COVID-19 patients. Results: The random-effect meta-analysis showed that cardiovascular disease was significantly associated with severity and mortality for COVID-19: odds ratio (OR) 2.89, 95% confidence interval (CI) 1.98–4.21 for severity and OR 3.00, 95% CI 1.67–5.39 for mortality, respectively. Risk of COVID-19 severity was higher in patients having diabetes, hypertension, chronic obstructive pulmonary disease, malignancy, cerebrovascular disease and chronic kidney disease. Similarly, patients with diabetes, hypertension, chronic liver disease, cerebrovascular disease and chronic kidney disease were at higher risk of mortality. Conclusion: Our findings showed that cardiovascular disease has a negative effect on health status of COVID-19 patients. However, large prevalence studies demonstrating the consequences of comorbid cardiovascular disease are urgently needed to understand the extent of these concerning comorbidities. Keywords: Cardiovascular disease, COVID-19, SARS-CoV-2

Serum albumin concentrations are associated with disease severity and outcomes in coronavirus 19 disease (COVID-19): a systematic review and meta-analysis

Coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is responsible for the most threatening pandemic in modern history. The aim of this systematic review and meta-analysis was to investigate the associations between serum albumin concentrations and COVID-19 disease severity and adverse outcomes. A systematic literature search was conducted in PubMed, from inception to October 30, 2020. Sixty-seven studies in 19,760 COVID-19 patients (6141 with severe disease or poor outcome) were selected for analysis. Pooled results showed that serum albumin concentrations were significantly lower in patients with severe disease or poor outcome (standard mean difference, SMD: - 0.99 g/L; 95% CI, - 1.11 to - 0.88, p < 0.001). In multivariate meta-regression analysis, age (t =  - 2.13, p = 0.043), publication geographic area (t = 2.16, p = 0.040), white blood cell count (t =  - 2.77, p = 0.008) and C-reactive protein (t =  - 2.43, p = 0.019) were significant contributors of between-study variance. Therefore, lower serum albumin concentrations are significantly associated with disease severity and adverse outcomes in COVID-19 patients. The assessment of serum albumin concentrations might assist with early risk stratification and selection of appropriate care pathways in this group.

Prognostic Factors at Admission for In-Hospital Mortality from COVID-19 Infection in an Older Rural Population in Central Spain

BACKGROUND

Risk factors for in-hospital mortality from severe coronavirus disease 2019 (COVID-19) infection have been identified in studies mainly carried out in urban-based teaching hospitals. However, there is little data for rural populations attending community hospitals during the first wave of the pandemic.

METHODS

A retrospective, single-center cohort study was undertaken among inpatients at a rural community hospital in Spain. Electronic medical records of the 444 patients (56.5% males) admitted due to severe SARS-CoV-2 infection during 26 February 2020-31 May 2020 were reviewed.

RESULTS

Mean age was 71.2 ± 14.6 years (rank 22-98), with 69.8% over 65. At least one comorbidity was present in 410 patients (92.3%), with chronic obstructive pulmonary disease (COPD) present in 21.7%. Overall in-hospital mortality was 32%. Multivariate analysis of factors associated with death identified patients' age (with a cumulative effect per decade), COPD as a comorbidity, and respiratory insufficiency at the point of admission. No additional comorbid conditions proved significant. Among analytical values, increased serum creatinine, LDH > 500 mg/dL, thrombocytopenia (<150 × 109/per L), and lymphopenia (<1000 cells/µL) were all independently associated with mortality during admission.

CONCLUSIONS

Age remained the major determinant for COVID-19-caused mortality; COPD was the only comorbidity independently associated with in-hospital death, together with respiratory insufficiency and analytical markers at admission.

Outcomes in Patients With COVID-19 Disease and High Oxygen Requirements

Background

Approximately 19% of people infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) progress to severe or critical stages of the coronavirus disease 2019 (COVID-19) with a mortality rate exceeding 50%. We aimed to examine the characteristics, mortality rates, intubation rate, and length of stay (LOS) of patients hospitalized with COVID-19 disease with high oxygen requirements (critically ill).

Methods

We conducted a retrospective analysis in a single center in Brooklyn, New York. Adult hospitalized patients with confirmed COVID-19 disease and high oxygen requirements were included. We performed multivariate logistic regression analyses for statistically significant variables to reduce any confounding.

Results

A total of 398 patients were identified between March 19th and April 25th, 2020 who met the inclusion criteria, of which 247 (62.1%) required intubation. The overall mortality rate in our study was 57.3% (n = 228). The mean hospital LOS was 19.1 ± 17.4 days. Patients who survived to hospital discharge had a longer mean LOS compared to those who died during hospitalization (25.4 ± 22.03 days versus10.7 ± 1.74 days). In the multivariate analysis, increased age, intubation and increased lactate dehydrogenase (LDH) were each independently associated with increased odds of mortality. Diarrhea was associated with decreased mortality (OR 0.4; CI 0.16, 0.99). Obesity and use of vasopressors were each independently associated with increased intubation.

Conclusions

In patients with COVID-19 disease and high oxygen requirements, advanced age, intubation, and higher LDH levels were associated with increased mortality, while diarrhea was associated with decreased mortality. Gender, diabetes, and hypertension did not have any association with mortality or length of hospital stay.

A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19)

This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 465 serial chest CT scans were involved, including 61 moderate patients (moderate group, 319 chest CT scans) and 34 severe patients (severe group, 146 chest CT scans). Conventional CT scoring and deep learning-based quantification were performed for all chest CT scans for two study goals: (1) Correlation between these two estimations; (2) Exploring the dynamic patterns using these two estimations between moderate and severe groups. The Spearman's correlation coefficient between these two estimation methods was 0.920 (p < 0.001). predicted pulmonary involvement (CT score and percent of pulmonary lesions calculated using deep learning-based quantification) increased more rapidly and reached a higher peak on 23rd days from symptom onset in severe group, which reached a peak on 18th days in moderate group with faster absorption of the lesions. The deep learning-based quantification for COVID-19 showed a good correlation with the conventional CT scoring and demonstrated a potential benefit in the estimation of disease severities of COVID-19.

Illuminating the immunopathology of SARS-CoV-2

Over a remarkably short period of time, a great deal of knowledge about severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection has been acquired, through the focused and cooperative effort of the international scientific community. Much has become known about how the immune response is coordinated to fight infection, and how it becomes dysregulated in severe disease. In this review, we take an in-depth look at the many immune features associated with the host response to SARS-CoV2, as well as those that appear to mark severe disease.

Radiology departments as COVID-19 entry-door might improve healthcare efficacy and efficiency, and emergency department safety

Background

Possible COVID-19 pneumonia patients (ppCOVID-19) generally overwhelmed emergency departments (EDs) during the first COVID-19 wave. Home-confinement and primary-care phone follow-up was the first-level regional policy for preventing EDs to collapse. But when X-rays were needed, the traditional outpatient workflow at the radiology department was inefficient and potential interpersonal infections were of concern. We aimed to assess the efficiency of a primary-care high-resolution radiology service (pcHRRS) for ppCOVID-19 in terms of time at hospital and decision’s reliability.

Methods

We assessed 849 consecutive ppCOVID-19 patients, 418 through the pcHRRS (home-confined ppCOVID-19 with negative—group 1- and positive—group 2-X-rays) and 431 arriving with respiratory symptoms to the ED by themselves (group 3). The pcHRRS provided X-rays and oximetry in an only-one-patient agenda. Radiologists made next-step decisions (group 1: pneumonia negative, home-confinement follow-up; group 2: pneumonia positive, ED assessment) according to X-ray results. We used ANOVA and Bonferroni correction, Student T, Chi² tests to analyse changes in the ED workload, time-to-decision differences between groups, potential delays in patients acceding through the ED, and pcHRRS performance for deciding admission.

Results

The pcHRRS halved ED respiratory patients (49.2%), allowed faster decisions (group 1 vs. home-discharged group 2 and group 3 patients: 0:41 ± 1:05 h; 3:36 ± 2:58 h; 3:50 ± 3:16 h; group 1 vs. all group 2 and group 3 patients: 0:41 ± 1:05 h; 5.25 ± 3.08; 5:36 ± 4:36 h; group 2 vs. group 3 admitted patients: 5:27 ± 3:08 h vs. 7:42 ± 5:02 h; all p < 0.001) and prompted admission (84/93, 90.3%) while maintaining time response for ED patients.

Conclusions

Our pcHRRS may be a more efficient entry-door for ppCOVID-19 by decreasing ED patients and making expedited decisions while guaranteeing social distance.

Predictors of death in severe COVID-19 patients at millennium COVID-19 care center in Ethiopia: a case-control study

INTRODUCTION

as the number of new cases and death due to COVID-19 is increasing, understanding the characteristics of severe COVID-19 patients and identifying characteristics that lead to death is a key to make an informed decision. In Ethiopia, as of September 27^th 2020, a total of 72,700 cases and 1165 deaths were reported. Therefore, the study aimed to assess the determinants of death in severe COVID-19 patients admitted to millennium COVID-19 care center in Ethiopia.

METHODS

a case-control study of 147 severe COVID-19 patients (49 deaths and 98 discharged alive cases) was conducted from August to September 2020. A comparison of underlying characteristics between cases (death) and controls (alive) was assessed using a chi-square test and an independent t-test with a p-value of <0.05 considered as having a statistically significant difference. Multivariable binary logistic regression was used to identify predictors of severe COVID-19 outcome (alive vs death) where adjusted odds ratio (AOR), 95% confidence interval (CIs) for AOR, and P-values were used for testing significance and interpretation of results.

RESULTS

having diabetes mellitus (AOR= 3.257, 95%CI=1.348,7.867, p-value <0.001), fever (AOR=0.328, 95%CI= 0.123,0.878, p-value=0.027) and shortness of breath (AOR=4.034, 95%CI=1.481,10.988, p-value=0.006) were found to be significant predictors of death in severe COVID-19 patients.

CONCLUSION

the outcome of death in severe COVID-19 patients is found to be associated with exposures to being diabetic and having shortness of breath at admission. On the other hand, having a fever at admission was associated with a favorable outcome of being discharged alive.

COVID-19 and Thromboinflammation: Is There a Role for Statins?

The novel coronavirus disease (COVID-19) showed increased morbidity and mortality rates and worse prognosis in individuals with underlying chronic diseases, especially cardiovascular disease and its risk factors, such as hypertension, diabetes, and obesity. There is also evidence of possible links among COVID-19, myocardial infarction, and stroke. Emerging evidence suggests a pro-inflammatory milieu and hypercoagulable state in patients with this infection. Despite anticoagulation, a large proportion of patients requiring intensive care may develop life-threatening thrombotic complications. Indeed, the levels of some markers of hemostatic activation, such as D-dimer, are commonly elevated in COVID-19, indicating potential risk of deep vein thrombosis and pulmonary thromboembolism. In this review, we critically examine and discuss aspects of hypercoagulability and inflammation in COVID-19 and the possible benefits of statins in this scenario, with emphasis on their underlying molecular mechanisms. Moreover, we present recommendations on the use of antiviral drugs in combination with statins.

Risk factors related to the severity of COVID-19 in Wuhan

Objective: To evaluate the characteristics at admission of patients with moderate COVID-19 in Wuhan and to explore risk factors associated with the severe prognosis of the disease for prognostic prediction. Methods: In this retrospective study, moderate and severe disease was defined according to the report of the WHO-China Joint Mission on COVID-19. Clinical characteristics and laboratory findings of 172 patients with laboratory-confirmed moderate COVID-19 were collected when they were admitted to the Cancer Center of Wuhan Union Hospital between February 13, 2020 and February 25, 2020. This cohort was followed to March 14, 2020. The outcomes, being discharged as mild cases or developing into severe cases, were categorized into two groups. The data were compared and analyzed with univariate logistic regression to identify the features that differed significantly between the two groups. Based on machine learning algorithms, a further feature selection procedure was performed to identify the features that can contribute the most to the prediction of disease severity. Results: Of the 172 patients, 112 were discharged as mild cases, and 60 developed into severe cases. Four clinical characteristics and 18 laboratory findings showed significant differences between the two groups in the statistical test (P<0.01) and univariate logistic regression analysis (P<0.01). In the further feature selection procedure, six features were chosen to obtain the best performance in discriminating the two groups with a linear kernel support vector machine. The mean accuracy was 91.38%, with a sensitivity of 0.90 and a specificity of 0.94. The six features included interleukin-6, high-sensitivity cardiac troponin I, procalcitonin, high-sensitivity C-reactive protein, chest distress and calcium level. Conclusions: With the data collected at admission, the combination of one clinical characteristic and five laboratory findings contributed the most to the discrimination between the two groups with a linear kernel support vector machine classifier. These factors may be risk factors that can be used to perform a prognostic prediction regarding the severity of the disease for patients with moderate COVID-19 in the early stage of the disease.

Predictive Value of C-reactive Protein, Lactate Dehydrogenase, Ferritin and D-dimer Levels in Diagnosing COVID-19 Patients: a Retrospective Study

Background:

Since December 2019. millions of people in the world have been affected with the novel Coronavirus disease-2019 (COVID-19) pandemic, and high economic impact has affect many countries especially low socioeconomic one like Iraq due to the high cost and limited availability of RT-PCR for diagnosis of COVID-19, so there should be predictive low cost easily available laboratory tests that can be used before proceeding to the high cost techniques.

Objective:

In this retrospective study we aimed to evaluate the diagnostic accuracy of CRP, ferritin, LDH and D-dimer in predicting positive cases of COVID-19 in Iraq.

Methods:

It is a retrospective observational cohort study based on STARD guidelines to determine the diagnostic accuracy of (CRP, LDH, ferritin and D dimer) for COVID-19 of electronic medical records of private medical center in Najaf city, at which 566 individuals were recruited. The investigated subjects were either in close contact with previously COVID-19 positive patients or have one or more symptoms of COVID-19. They were categorized into 2 groups, 205 subjects diagnosed with RT-PCR as COVID-19 negative, and 361 COVID-19 positive patients, results of study variables of the cohort were recruited from the medical records.

Results:

Combining of these parameters had the following findings: CRP + ferritin; AUC: 0.77 with 55% sensitivity and 97% specificity, Ferritin + LDH; AUC: 0.83 with 65% sensitivity and 92% specificity, CRP+LDH; AUC: 0.78 with 56% sensitivity and 98% specificity, CRP + LDH + ferritin; AUC: 0.85, with 73% sensitivity and 88% specificity, CRP + LDH + ferritin + D dimer; AUC: 0.85 75% sensitivity and 87% specificity.

Conclusion:

Combination of routine laboratory biomarkers (CRP, LDH and ferritin ±D dimer) can be used to predict the diagnosis of COVID-19 with an accepted sensitivity and specificity before proceeding to definitive diagnosis by RT-PCR.

Prognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review

BACKGROUND

This study is aimed at reviewing the published literature on the prognostic role of serum procalcitonin (PCT) in COVID-19 cases.

DATA RETRIEVAL

We systematically reviewed the literature available on PubMed, MEDLINE, LitCovid NLM, and WHO: to assess the utility of PCT in prognosis of coronavirus disease. Scrutiny for eligible studies comprising articles that have evaluated the prognostic utility of PCT and data compilation was undertaken by two separate investigators. Original articles in human subjects reporting the prognostic role of PCT in adult COVID-19 patients were included. The Quality in Prognosis Studies (QUIPS) tool was utilized to assess the strength of evidence. Results were reported as narrative syntheses.

RESULTS

Out of the total 426 citations, 52 articles passed through screening. The quality of evidence and methodology of included studies was overall acceptable. The total sample size of the studies comprised of 15,296 COVID-19-positive subjects. Majority of the studies were from China, i.e., 40 (77%). The PCT cut-off utilized was 0.05 ng/mL by 18 (35%) studies, followed by 0.5 ng/mL by 9 (17.5%). Eighty five percent (n = 44) studies reported statistically significant association (p value < 0.05) between PCT and severity.

CONCLUSION

Procalcitonin appears as a promising prognostic biomarker of COVID-19 progression in conjunction with the clinical context.

HOW TO CITE THIS ARTICLE

Ahmed S, Jafri L, Hoodbhoy Z, Siddiqui I. Prognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review. Indian J Crit Care Med 2021;25(1):77-84.

Oxygen saturation as a predictor of mortality in hospitalized adult patients with COVID-19 in a public hospital in Lima, Peru

INTRODUCTION

Peru is among the top ten countries with the highest number of coronavirus disease 2019 (COVID-19) cases worldwide. The aim of the study was to describe the clinical features of hospitalized adult patients with COVID-19 and to determine the prognostic factors associated with in-hospital mortality.

METHODS

We conducted a retrospective cohort study among adult patients with COVID-19 admitted to Hospital Cayetano Heredia; a tertiary care hospital in Lima, Peru. The primary outcome was in-hospital mortality. Multivariate Cox proportional hazards regression was used to identify factors independently associated with in-hospital mortality.

RESULTS

A total of 369 patients (median age 59 years [IQR:49-68]; 241 (65.31%) male) were included. Most patients (68.56%) reported at least one comorbidity; more frequently: obesity (42.55%), diabetes mellitus (21.95%), and hypertension (21.68%). The median duration of symptoms prior to hospital admission was 7 days (IQR: 5-10). Reported in-hospital mortality was 49.59%. By multiple Cox regression, oxygen saturation (SaO2) values of less than 90% on admission correlated with mortality, presenting 1.86 (95%CI: 1.02-3.39), 4.44 (95%CI: 2.46-8.02) and 7.74 (95%CI: 4.54-13.19) times greater risk of death for SaO2 of 89-85%, 84-80% and <80%, respectively, when compared to patients with SaO2 >90%. Additionally, age >60 years was associated with 1.88 times greater mortality.

CONCLUSIONS

Oxygen saturation below 90% on admission is a strong predictor of in-hospital mortality in patients with COVID-19. In settings with limited resources, efforts to reduce mortality in COVID-19 should focus on early identification of hypoxemia and timely access to hospital care.

B Cell Subsets as Severity-Associated Signatures in COVID-19 Patients

Background

SARS-CoV-2 infection represents a global health problem that has affected millions of people. The fine host immune response and its association with the disease course have not yet been fully elucidated. Consequently, we analyze circulating B cell subsets and their possible relationship with COVID-19 features and severity.

Methods

Using a multiparametric flow cytometric approach, we determined B cell subsets frequencies from 52 COVID-19 patients, grouped them by hierarchical cluster analysis, and correlated their values with clinical data.

Results

The frequency of CD19⁺ B cells is increased in severe COVID-19 compared to mild cases. Specific subset frequencies such as transitional B cell subsets increase in mild/moderate cases but decrease with the severity of the disease. Memory B compartment decreased in severe and critical cases, and antibody-secreting cells are increased according to the severity of the disease. Other non-typical subsets such as double-negative B cells also showed significant changes according to disease severity. Globally, these differences allow us to identify severity-associated patient clusters with specific altered subsets. Finally, respiratory parameters, biomarkers of inflammation, and clinical scores exhibited correlations with some of these subpopulations.

Conclusions

The severity of COVID-19 is accompanied by changes in the B cell subpopulations, either immature or terminally differentiated. Furthermore, the existing relationship of B cell subset frequencies with clinical and laboratory parameters suggest that these lymphocytes could serve as potential biomarkers and even active participants in the adaptive antiviral response mounted against SARS-CoV-2.

The potential association between common comorbidities and severity and mortality of coronavirus disease 2019: A pooled analysis

BACKGROUD

The association between underlying comorbidities and cardiac injury and the prognosis in coronavirus disease 2019 (COVID-19) patients was assessed in this study.

HYPOTHESIS

The underlying comorbidities and cardiac injury may be associated with the prognosis in COVID-19 patients.

METHODS

A systematic search was conducted in PubMed, EMBASE, Web of science, and The Cochrane library from December 2019 to July 2020. The odds ratio (OR) and 95% confidence intervals (95% CI) were used to estimate the probability of comorbidities and cardiac injury in COVID-19 patients with or without severe type, or in survivors vs nonsurvivors of COVID-19 patients.

RESULTS

A total of 124 studies were included in this analysis. A higher risk for severity was observed in COVID-19 patients with comorbidities. The pooled result in patients with hypertension (OR 2.57, 95% CI: 2.12-3.11), diabetes (OR 2.54, 95% CI: 1.89-3.41), cardiovascular diseases (OR 3.86, 95% CI: 2.70-5.52), chronic obstractive pulmonary disease (OR 2.71, 95% CI: 1.98-3.70), chronic kidney disease (OR 2.20, 95% CI: 1.27-3.80), and cancer (OR 2.42, 95% CI: 1.81-3.22) respectively. All the comorbidities presented a higher risk of mortality. Moreover, the prevalence of acute cardiac injury is higher in severe group than in nonsevere group, and acute cardiac injury is associated with an increased risk for in-hospital mortality.

CONCLUSION

Comorbidities and acute cardiac injury are closely associated with poor prognosis in COVID-19 patients. It is necessary to continuously monitor related clinical indicators of organs injury and concern comorbidities in COVID-19 patients.

A Cytokine Circus with a Viral Ringleader: SARS-CoV-2-Associated Cytokine Storm Syndromes

An unbridled host immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is likely to underlie severe cases of the disease and has been labeled a 'cytokine storm syndrome' (CSS). Here, we emphasize that categorization of syndromes triggered by a completely novel pathogen based on other seemingly similar, but potentially distinct, known entities is an inherently risky endeavor.

Sex Disparities in COVID-19 Severity and Outcome: Are Men Weaker or Women Stronger?

The coronavirus disease 2019 (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global public health issue which has profound effects on most aspects of societal well-being, including physical and mental health. A plethora of studies globally have suggested the existence of a sex disparity in the severity and outcome of COVID-19 patients, mainly due to mechanisms of virus infection, immune response to the virus, development of systemic inflammation, and consequent systemic complications, particularly thromboembolism. Epidemiological data report a sex difference in the severity of COVID-19, with a more favorable course of the disease in women compared to men regardless of age, although the rate of SARS-CoV-2 infection seems to be similar in both sexes. Sex hormones, including androgens and estrogens, may not only impact virus entry and load, but also shape the clinical manifestations, complications, and ultimately the outcome of the disease. The current review comprehensively summarizes the current literature on sex disparities in susceptibility and outcome of COVID-19 as well as the literature underpinning the pathophysiological and molecular mechanisms, which may provide a rationale to a sex disparity. These mechanisms include sex hormone influence on factors that facilitate virus entry and priming, immune and inflammatory response, as well as coagulation and thrombosis diathesis. Based on present evidence, women appear to be relatively protected from COVID-19 because of a more effective immune response and a less pronounced systemic inflammation, with consequent moderate clinical manifestations of the disease, together with a lesser predisposition to thromboembolism. Conversely, men appear to be particularly susceptible to COVID-19 because of a less effective immune response with consequent severe clinical manifestations of the disease, together with a greater predisposition to thromboembolism. In the elderly, generally characterized by the phenomenon of inflammaging, sex disparities in overall mortality following SARS-CoV-2 infection are even more palpable as elderly men appear to be more prone to severe COVID-19 because of a greater predisposition to infections, a weaker immune defense, and an enhanced thrombotic state compared to women. The information revealed from the review highlights potential novel therapeutic approaches employing the administration of hormonal or antihormonal therapy in combination with antiviral drugs in COVID-19 patients.

Knowledge of COVID-19 and Its Influence on Mindfulness, Cognitive Emotion Regulation and Psychological Flexibility in the Indian Community

The current global pandemic caused by COVID-19 has brought about an immense effect on the mental health of the general public. Considering the escalation in number of cases, mankind is facing a myriad of psychological problems, ranging from those related to taking precautions and maintaining safety to the ones caused by separation and bereavement. The current study aims to explore whether there is a significant difference between individuals with excellent, good, fair and vague knowledge of COVID-19 with respect to depression, anxiety, stress, level of mindfulness, specific cognitive emotion regulation strategies and psychological flexibility; to find out whether there is any significant relationship among these variables; and to determine whether knowledge of COVID-19, level of mindfulness, specific cognitive emotion regulation strategies and psychological flexibility are significant predictors of depression, anxiety and stress in the sample of the current study. The sample consisted of 402 individuals selected from the community following the research criteria. Data was collected using digital consent form, information schedule and questionnaires, from 3rd May to 13th May, 2020. The questionnaires consisted of a semi-structured interview schedule to assess knowledge of COVID-19, Depression, Anxiety and Stress Scale - 21, Five Facet Mindfulness Questionnaire-Short Form, Cognitive Emotion Regulation Questionnaire-Short version and Acceptance and Action Questionnaire-II. The data was statistically analyzed using analysis of variance test, correlational analysis and linear regression. The findings show that significant differences were present among individuals having varying degrees of knowledge of COVID-19 with respect to anxiety, level of mindfulness and psychological flexibility. Significant relationships were found to be present among the variables of the present study, having differing trends brought forward by the COVID-19 crisis. Certain socio-demographic characteristics and study variables were found to significantly predict the existing levels of depression, anxiety and stress in the current sample. The study suggests the necessity to formulate and implement appropriate mindfulness-based therapeutic interventions to address the mental health concerns arising as a result of the pandemic.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

Background

COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19.

Methods

We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies.

Results

Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 - 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73).

Discussion

This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors in predicting severe COVID-19 outcomes and will inform decision analytical tools to support clinical decision-making.

Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions

OBJECTIVE

Risk factors for in-hospital mortality in confirmed COVID-19 patients have been summarized in numerous meta-analyses, but it is still unclear whether they vary according to the age, sex and health conditions of the studied populations. This study explored these variables as potential mortality predictors.

METHODS

A systematic review was conducted by searching the MEDLINE, Scopus, and Web of Science databases of studies available through July 27, 2020. The pooled risk was estimated with the odds ratio (p-OR) or effect size (p-ES) obtained through random-effects meta-analyses. Subgroup analyses and meta-regression were applied to explore differences by age, sex and health conditions. The MOOSE guidelines were strictly followed.

RESULTS

The meta-analysis included 60 studies, with a total of 51,225 patients (12,458 [24.3%] deaths) from hospitals in 13 countries. A higher in-hospital mortality risk was found for dyspnoea (p-OR = 2.5), smoking (p-OR = 1.6) and several comorbidities (p-OR range: 1.8 to 4.7) and laboratory parameters (p-ES range: 0.3 to -2.6). Age was the main source of heterogeneity, followed by sex and health condition. The following predictors were more markedly associated with mortality in studies with patients with a mean age ≤60 years: dyspnoea (p-OR = 4.3), smoking (p-OR = 2.8), kidney disease (p-OR = 3.8), hypertension (p-OR = 3.7), malignancy (p-OR = 3.7), diabetes (p-OR = 3.2), pulmonary disease (p-OR = 3.1), decreased platelet count (p-ES = -1.7), decreased haemoglobin concentration (p-ES = -0.6), increased creatinine (p-ES = 2.4), increased interleukin-6 (p-ES = 2.4) and increased cardiac troponin I (p-ES = 0.7). On the other hand, in addition to comorbidities, the most important mortality predictors in studies with older patients were albumin (p-ES = -3.1), total bilirubin (p-ES = 0.7), AST (p-ES = 1.8), ALT (p-ES = 0.4), urea nitrogen (p-ES), C-reactive protein (p-ES = 2.7), LDH (p-ES = 2.4) and ferritin (p-ES = 1.7). Obesity was associated with increased mortality only in studies with fewer chronic or critical patients (p-OR = 1.8).

CONCLUSION

The prognostic effect of clinical conditions on COVID-19 mortality vary substantially according to the mean age of patients.

PROSPERO REGISTRATION NUMBER

CRD42020176595.

Mortality in COVID-19 patients with acute respiratory distress syndrome and corticosteroids use: a systematic review and meta-analysis

OBJECTIVES

The acute respiratory distress syndrome (ARDS) secondary to viral pneumonitis is one of the main causes of high mortality in patients with COVID-19 (novel coronavirus disease 2019). We systematically reviewed mortality in COVID-19 patients with ARDS and the potential role of systemic corticosteroids in COVID-19 patients.

METHODS

Electronic databases and country-specific healthcare databases were searched to identify relevant studies/reports. The quality assessment of individual studies was conducted using the Newcastle-Ottawa Scale. Country-specific proportion of individuals with COVID-19 who developed ARDS and reported death were combined in a random-effect meta-analysis to give a pooled mortality estimate of ARDS.

RESULTS

The overall pooled mortality estimate among 10,815 ARDS cases in COVID-19 patients was 39% (95% CI: 23-56%). The pooled mortality estimate for China was 69% (95% CI: 67-72%). In Europe, the highest mortality estimate among COVID-19 patients with ARDS was reported in Poland (73%; 95% CI: 58-86%) while Germany had the lowest mortality estimate (13%; 95% CI: 2-29%) among COVID-19 patients with ARDS. The median crude mortality rate of COVID-19 patients with reported corticosteroid use was 28.0% (lower quartile: 13.9%; upper quartile: 53.6%).

CONCLUSIONS

The high mortality in COVID-19 associated ARDS necessitates a prompt and aggressive treatment strategy which includes corticosteroids. Most of the studies included no information on the dosing regimen of corticosteroid therapy, however, low-dose corticosteroid therapy or pulse corticosteroid therapy appears to have a beneficial role in the management of severely ill COVID-19 patients.

Neutrophil-to-lymphocyte ratio, a critical predictor for assessment of disease severity in patients with COVID-19

INTRODUCTION

Monitoring of laboratory indicators is important for predicting changes in disease severity and clinical outcomes. We aimed to identify the critical predictors that can effectively assess the disease conditions of patients with COVID-19 by analyzing the clinical characteristics and laboratory findings of patients with SARS-CoV-2 infection.

METHODS

All consecutive patients (n = 294) with confirmed SARS-CoV-2 infection admitted to the General Hospital of Central Theater Command of the PLA from February 6 to February 21, 2020, were enrolled. These patients were divided into the severe group and the nonsevere group according to disease severity during hospitalization.

RESULTS

The median neutrophil-to-lymphocyte ratio (NLR) value of the severe patients was dramatically higher than that of the nonsevere patients (10.4 vs 2.6; P < .001). The NLR value equal to 5 was a boundary value worthy of reference, because more than 80% severe patients had an NLR value greater than 5 and over 80% nonsevere patients had an NLR value less than 5. The NLR value of these COVID-19 patients was positively and respectively correlated with the values of C-reactive protein (R = .5921, P < .001), lactate dehydrogenase (R = .4509, P < .001), procalcitonin (R = .5504, P < .001), fibrinogen (R = .4710, P < .001), and D-dimers (R = .4425, P < .001). However, the NLR value was merely and positively correlated with the interleukin-6 value (R = .3594, P < .05), but had no correlations with the values of interleukin-10, interleukin-4, interleukin-17, interferon-γ, and tumor necrosis factor-α (P > .05).

DISCUSSION

Neutrophil-to-lymphocyte ratio is a critical predictor for assessment of disease severity in patients with COVID-19, and it has a close relation with the laboratory indicators related to disease conditions.

Clinical laboratory characteristics in patients with suspected COVID-19: One single-institution experience

OBJECTIVES

Since December 2019, the outbreak of coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. The aim of the study is to investigate the demographic, clinical, and laboratory characteristics in suspected COVID-19 patients in our institution.

METHODS

In this retrospective study, we investigated suspected COVID-19 patients admitted to the University of Alabama at Birmingham with a request for an interleukin-6 send-out test, from March 28 to June 27, 2020. Patients' demographic, clinical, and laboratory characteristics were collected by chart review.

RESULTS

Fifty patients suspected with COVID-19 were included in our study, of whom 24 patients were positive with severe acute respiratory syndrome coronavirus-2 infection and 26 were negative. During the observation period, 30 patients were discharged, 17 died during hospitalization, and three remained in hospital. Compared to non-COVID-19 patients, COVID-19 patients had older age, more comorbidities, and elevated levels of inflammation markers such as erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), serum ferritin, and lactate dehydrogenase (LDH). However, there was no significant difference in laboratory data between survivors and nonsurvivors in COVID-19 patients in our study.

CONCLUSION

This study indicated that potential risk factors of older age, multiple comorbidities, and high levels of ESR, CRP, serum ferritin, and LDH could help the clinician to identify potential COVID-19 patients. However, this data needs to be further validated in a larger population.

Association of Peripheral Lymphocyte and the Subset Levels With the Progression and Mortality of COVID-19: A Systematic Review and Meta-Analysis

Current evidence is controversial in the association between peripheral lymphocyte levels and the progression and mortality of Corona Virus Disease 2019 (COVID-19), and this meta-analysis aimed to clarify the association. A systematic search was conducted in public databases to identify all relevant studies, and the study-specific odds ratio (OR) and 95% confidence intervals (CI) were pooled. Finally, 16 studies were identified with a total of 1,873 progressive COVID-19 cases and 5,177 stable COVID-19 cases. In COVID-19 progression, lymphocyte levels showed a significant negative correlation (OR: 0.68, 95% CI: 0.51-0.89), but it was not significant in the subsets of CD3+ T cells (OR: 0.97, 95% CI: 0.93-1.02), CD4+ T cells (OR: 0.93, 95% CI: 0.80-1.08), CD8+ T cells (OR: 0.96, 95% CI: 0.92-1.00), B cells (OR: 0.98, 95% CI: 0.92-1.04), or NK cells (OR: 0.80, 95% CI: 0.61-1.04). In COVID-19 mortality, lymphocyte levels showed a significant negative correlation (OR: 0.41, 95% CI: 0.20-0.85), but it was not significant in the subsets of CD3+ T cells (OR: 0.95, 95% CI: 0.86-1.05), CD4+ T cells (OR: 1.06, 95% CI: 0.86-1.31), CD8+ T cells (OR: 0.38, 95% CI: 0.14-1.01), B cells (OR: 0.98, 95% CI: 0.92-1.04), or NK cells (OR: 0.80, 95% CI: 0.61-1.04). In conclusion, current evidence suggests a significant negative association of peripheral lymphocyte levels with COVID-19 progression and mortality, but it was not significant in the subsets of CD3+ T cells, CD4+ T cells, CD8+ T cells, B cells, and NK cells.

Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study

OBJECTIVES

Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.

DESIGN

The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.

SETTING

3 secondary and tertiary level centres in Greater Manchester, the UK.

PARTICIPANTS

392 hospitalised patients with a diagnosis of COVID-19.

RESULTS

392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.

CONCLUSIONS

This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.