Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis

Chitinase-3-like protein-1 at hospital admission predicts COVID-19 outcome: a prospective cohort study

Infectious and inflammatory stimuli elicit the generation of chitinase-3-like protein-1 (CHI3L1), involved in tissue damage, repair and remodeling. We evaluated whether plasma CHI3L1 at disease onset predicts clinical outcome of patients with Coronavirus 2019 (COVID-19) disease. Blood from 191 prospectively followed COVID-19 patients were collected at hospital admission between March 18th and May 5th, 2020. Plasma from 80 survivors was collected one month post-discharge. Forty age- and sex-matched healthy volunteers served as controls. Primary outcome was transfer to intensive care unit (ICU) or death. CHI3L1 was higher in COVID-19 patients than controls (p < 0.0001). Patients with unfavorable outcome (41 patients admitted to ICU, 47 died) had significantly higher CHI3L1 levels than non-ICU survivors (p < 0.0001). CHI3L1 levels abated in survivors one month post-discharge, regardless of initial disease severity (p < 0.0001), although remaining higher than controls (p < 0.05). Cox regression analysis revealed that CHI3L1 levels predict primary outcome independently of age, sex, comorbidities, degree of respiratory insufficiency and systemic inflammation or time from symptom onset to sampling (p < 0.0001). Kaplan-Meier curve analysis confirmed that patients with CHI3L1 levels above the median (361 ng/mL) had a poorer prognosis (log rank test, p < 0.0001). Plasma CHI3L1 is increased in COVID-19 patients and predicts adverse outcome.

The role of blood urea nitrogen to serum albumin ratio in the prediction of severity and 30‐day mortality in patients with COVID‐19

Background

Considering the role of higher blood urea nitrogen and lower serum albumin (SA) levels in deceased coronavirus disease 2019 (COVID‐19) patients, an increased blood urea nitrogen to SA (B/A) ratio may help to determine those at higher risk of critical illness. This study aimed to evaluate the correlation of the B/A ratio with severity and 30‐day mortality in COVID‐19 patients.

Methods

A total of 433 adult patients with COVID‐19 were enrolled. The laboratory markers were measured on admission. Disease severity was categorized into mild disease, severe pneumonia, acute respiratory distress syndrome (ARDS), sepsis, and septic shock. The mortality was followed for 30 days after admission. χ² test, Fisher's exact test, and Mann–Whitney U test were performed, as appropriate. Also, logistic regression and the receiver operating characteristic (ROC) curve for the B/A ratio are included.

Results

Thirty‐day mortality rate was 27.25%. The frequency of mild, severe pneumonia, ARDS, sepsis, and septic shock was 30.72%, 36.95%, 24.02%, 6.00%, and 2.31%, respectively. B/A ratio and SA levels were statistically different between alive and deceased patients. The mean B/A ratio was different among classified disease severities, except for mild disease. Logistic regression revealed the B/A ratio as an independent risk factor for sepsis after adjusting for age and sex. ROC analysis showed B/A ratio had an area under the curve (AUC) of 0.733 for mortality at the cutpoint of 4.944. AUC for sepsis was 0.617 which was greater than other disease severities.

Conclusion

The results showed that B/A ratio and SA levels are associated with mortality of COVID‐19 patients. A higher B/A ratio is, additionally, associated with COVID‐19 severity, except in mild cases and it can act as an independent risk factor in sepsis. However, a greater B/A ratio is not a significant predictor of COVID‐19 severity, but it can predict mortality. Therefore, we suggest this marker for clinical assessment of patients with severe COVID‐19.

Coronavirus Disease 2019 and Cardiometabolic Disease

Cardiometabolic disease describes a combination of metabolic abnormalities that increases the risk of type 2 diabetes and cardiovascular diseases, including pathological changes such as insulin resistance, hyperglycemia, dyslipidemia, abdominal obesity, and hypertension, and environmental risk factors such as smoking, sedentary lifestyle, poor diet, and poverty. As the number of coronavirus disease 2019 (COVID-19) patients continues to rise, type 2 diabetes, cardiovascular disease, hypertension, and obesity, all components of, or sequelae of cardiometabolic disease, were identified among others as key risk factors associated with increased mortality in these patients. Numerous studies have been done to further elucidate this relationship between COVID-19 and cardiometabolic disease. Cardiometabolic disease is associated with both increased susceptibility to COVID-19 and worse outcomes of COVID-19, including intensive care, mechanical ventilation, and death. The proinflammatory state of cardiometabolic disease specifically obesity, has been associated with a worse prognosis in COVID-19 patients. There has been no evidence to suggest that antihypertensives and antidiabetic medications should be discontinued in COVID-19 patients but these patients should be closely monitored to ensure that their blood pressure and blood glucose levels are stable. Assessment of vaccination efficacy in cardiometabolic disease patients is also discussed.

Socio-demographic, lifestyle and health characteristics as predictors of self-reported Covid-19 history among older adults: 2006-2020 Health and Retirement Study

Background

To identify key socio-demographic, lifestyle, and health predictors of self-reported coronavirus disease 2019 (Covid-19) history, examine cardiometabolic health characteristics as predictors of self-reported Covid-19 history and compare groups with and without a history of Covid-19 on trajectories in cardiometabolic health and blood pressure measurements over time, among United States (U.S.) older adults.

Methods

Nationally representative longitudinal data on U.S. older adults from the 2006-2020 Health and Retirement Study were analyzed using logistic and mixed-effects logistic regression models.

Results

Based on logistic regression, number of household members (OR=1.26, 95% CI: 1.05, 1.52), depressive symptoms score (OR = 1.21, 95% CI: 1.04, 1.42) and number of cardiometabolic risk factors or chronic conditions (“1-2” vs “0”) (OR = 0.27, 95% CI: 0.11, 0.67) were significant predictors of self-reported Covid-19 history. Based on mixed-effects logistic regression, several statistically significant predictors of Covid-19 history were identified, including female sex (OR = 3.06, 95% CI: 1.57, 5.96), other race (OR = 5.85, 95% CI: 2.37, 14.43), Hispanic ethnicity (OR = 2.66, 95% CI: 1.15, 6.17), number of household members (OR = 1.25, 95% CI: 1.10, 1.42), moderate-to-vigorous physical activity (1-4 times per month vs never) (OR = 0.38, 95% CI: 0.18, 0.78) and number of cardiometabolic risk factors or chronic conditions (“1-2” vs “0”) (OR = 0.34, 95% CI: 0.19, 0.60).

Conclusions

Number of household members, depressive symptoms and number of cardiometabolic risk factors or chronic conditions may be key predictors for self-reported Covid-19 history among U.S. older adults. In-depth analyses are needed to confirm preliminary findings.

Antibody levels remain high to one-year's follow-up after moderate and severe COVID-19, but not after mild cases

BACKGROUND

Understanding the longevity of antibodies against SARS-CoV-2 infection is of utmost importance in predicting the further course of the pandemic and to plan vaccination strategies. Here we report a cohort of COVID-19 patients with different disease severities whose antibody dynamics we evaluated during one-year of follow-up.

METHODS

We conducted a longitudinal study of 123 COVID-19 patients and 45 SARS CoV-2 negative outpatients with upper respiratory tract infection. We analyzed the demographic and clinical features of the patients with COVID-19 in relation to different disease severities according to the WHO classification. The antibody response was evaluated by a Luciferase Immunoprecipitation System (LIPS) assay at 3, 6, and 12 months after the acute infection.

RESULTS

Amongst the enrolled COVID-19 patients, 15 (12%) had mild, 42 (34%) had moderate, 39 (32%) had severe and 27 (22%) had critical disease courses; 79% of the patients were hospitalized. During follow-up, all patients had anti-SARS RBD-IgG levels above the cut-off value on all visits, but the antibody levels varied significantly between the different disease severity groups. Between the six- and 12-month follow-up visits, 41% of patients were vaccinated, which enhanced their antibody levels significantly.

CONCLUSION

Our data demonstrate sustained antibody levels at one-year after moderate and severe COVID-19 infection. Vaccination of patients with the mild disease is important to raise the antibody levels to a protective level.

Immediate and antecedent causes of mortality in hospitalised Indian patients with COVID-19

It is of interest to assess the immediate and antecedent causes of mortality amongst adult COVID-19 infected patients with or without comorbidities admitted in an exclusive COVID-19 hospital was conducted the between August 2020 to May 2021. The immediate and antecedent causes were collected from the medical certificate of cause of death (MCCD). Remaining data was extracted from the hospital's record. ICMR protocol was used to grade severity of illness at admission into mild, moderate and severe categories. Clinical status during hospitalisation and most recent radiographic and laboratory data were used to assess disease progression and outcome. This study includes data from 571 people, who died at our centre between August 2020 and May 2021. Patients registered without any co-morbidity were 146 with mean age of 57.53 years; (33/146) were females and (110/46) males. Hypertension (274, 47.99%) was found in a moderately large number of patients followed by diabetes (225, 39.4%) and anaemia (199, 34.6%). Increase in risk of mortality of COVID-19 was found maximum in patients with acute respiratory distress syndrome (72.33%), followed by secondary infections (6.83%). Mortality recorded in this study was mainly in males of older age (50 years and above) with at least one co-morbidity. Anaemia was also prevalent amongst these patients and considered as an independent factor for mortality. Hence, recording of comorbidities and haemoglobin levels may help as a guideline to develop risk stratification and management of patients with COVID-19 to reduce overall mortality.

Chromogranin A plasma levels predict mortality in COVID-19

Background

Chromogranin A (CgA) and its fragment vasostatin I (VS-I) are secreted in the blood by endocrine/neuroendocrine cells and regulate stress responses. Their involvement in Coronavirus 2019 disease (COVID-19) has not been investigated.

Methods

CgA and VS-I plasma concentrations were measured at hospital admission from March to May 2020 in 190 patients. 40 age- and sex-matched healthy volunteers served as controls. CgA and VS-I levels relationship with demographics, comorbidities and disease severity was assessed through Mann Whitney U test or Spearman correlation test. Cox regression analysis and Kaplan Meier survival curves were performed to investigate the impact of the CgA and VS-I levels on in-hospital mortality.

Results

Median CgA and VS-I levels were higher in patients than in healthy controls (CgA: 0.558 nM [interquartile range, IQR 0.358–1.046] vs 0.368 nM [IQR 0.288–0.490] respectively, p = 0.0017; VS-I: 0.357 nM [IQR 0.196–0.465] vs 0.144 nM [0.144–0.156] respectively, p<0.0001). Concentration of CgA, but not of VS-I, significantly increased in patients who died (n = 47) than in survivors (n = 143) (median 0.948 nM [IQR 0.514–1.754] vs 0.507 nM [IQR 0.343–0.785], p = 0.00026). Levels of CgA were independent predictors of in-hospital mortality (hazard ratio 1.28 [95% confidence interval 1.077–1.522], p = 0.005) when adjusted for age, number of comorbidities, respiratory insufficiency degree, C-reactive protein levels and time from symptom onset to sampling. Kaplan Meier curves revealed a significantly increased mortality rate in patients with CgA levels above 0.558 nM (median value, log rank test, p = 0.001).

Conclusion

Plasma CgA levels increase in COVID-19 patients and represent an early independent predictor of mortality.

Prediction of SARS-CoV-2-Related Lung Inflammation Spreading by V:ERITAS (Vanvitelli Early Recognition of Inflamed Thoracic Areas Spreading)

Background Coronavirus disease 2019 (COVID-19) can be complicated by interstitial pneumonia, possibly leading to severe acute respiratory failure and death. Because of variable evolution ranging from asymptomatic cases to the need for invasive ventilation, COVID-19 outcomes cannot be precisely predicted on admission. The aim of this study was to provide a simple tool able to predict the outcome of COVID-19 pneumonia on admission to a low-intensity ward in order to better plan management strategies for these patients. Methods The clinical records of 123 eligible patients were reviewed. The following variables were analyzed on admission: chest computed tomography severity score (CTSS), PaO2/FiO2 ratio, lactate dehydrogenase (LDH), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio, C-reactive protein (CRP), fibrinogen, D-dimer, aspartate aminotransferase (AST), alanine aminotransferase, alkaline phosphatase, and albumin. The main outcome was the intensity of respiratory support (RS). To simplify the statistical analysis, patients were split into two main groups: those requiring no or low/moderate oxygen support (group 1); and those needing subintensive/intensive RS up to mechanical ventilation (group 2). Results The RS intensity was significantly associated with higher CTSS and NLR scores; lower PaO2/FiO2 ratios; and higher serum levels of LDH, CRP, D-dimer, and AST. After multivariate logistic regression and ROC curve analysis, CTSS and LDH were shown to be the best predictors of respiratory function worsening. Conclusions Two easy-to-obtain parameters (CTSS and LDH) were able to reliably predict a worse evolution of COVID-19 pneumonia with values of >7 and >328 U/L, respectively.

Factors Associated with Death of Covid-19 Patients: Case Series

This aim of this study was to identify potential factors associated with survival in patients with coronavirus disease 2019 (COVID- 19). This study was designed as a case series. It included patients hospitalized in Clinical Centre Kragujevac between March 10, 2020, and June 15, 2020, due to COVID-19. Variables with significant influence on the cure of patients were identified by multivariate logistic regression. A total of 234 patients were included. The average age of the patients was 57.2 ± 15.8 years. Thirty-five patients died (15.0%) and 199 (85.0%) were discharged as completely cured and with the sustained virological response.

The study variables with significant influence (expressed as odds ratio – OR) on cure of COVID-19 patients after adjustment for effects of other variables were: stay in an intensive care unit – ICU (OR = 0.007; 95% confidence interval – CI 0.001 – 0.086; p = 0.000), previous hospitalization (OR = 7.802; 95% CI 1.198 – 50.924; p = 0.032), increased body temperature on admission (OR = 0.004; 95% CI 0.000 – 0.771; p = 0.040), higher score of the Charlson Comorbidity Index (OR = 0.428; 95% CI 0.235 – 0.780; p = 0.006), and increased values of C-reactive protein (CRP) serum level (OR = 0.978; 95% CI 0.966 – 0.990; p = 0.000). In conclusion, clinicians should pay attention to patients with high body temperature at admission, presence of multiple comorbidities, high CRP, and patients who stay in an ICU, considering that they could be at risk for fatal outcome.

Comparison of Survival and Clinical Profile of Adults with COVID-19 Hospitalized in Two Clinics in Medellín, Colombia

Purpose of Review

This study compares the survival and clinical profile of hospitalized adults with COVID-19 in two clinics in the city of Medellín, Colombia, with a prospective study with 198 patients in clinic A and 201 in clinic B. Comparisons were made with chi-square and Mann–Whitney U, factors associated with survival were identified with a Cox regression.

Recent Findings

The proportion of deaths was 7.1% in clinic A with a mean survival of 51.9 days (95% CI = 45–59); in clinic B 13.9% of patients died with mean survival of 37.8 days (95% CI = 32–43). The most prevalent comorbidities were hypertension (41.6%), diabetes (23.8%), obesity (15.0%), hypothyroidism (13.0%), dyslipidemia (11.0%), and chronic lung disease (10.8%) with similar proportions in both clinics. There were also differences by the clinic in the most prevalent complications: bacterial pneumonia (18.8%), acute renal failure (14.3%), and encephalopathy (9.5%). There were no differences in the days of hospitalization, mechanical ventilation (clinic A 23.7% and clinic B 29.4%) and admission to the ICU (25.3% in A and 32.3% in B).

Summary

We evidence the heterogeneity of the survival and the clinical profile of the patients who are cared for by two institutions of the same city. These findings demonstrate the need to conduct unique studies for each institution, which poses a significant challenge for hospital epidemiology programs due to the impossibility of extrapolating evidence from other healthcare institutions and the need to implement personalized medicine programs given the clinical diversity of patients hospitalized for COVID-19.

Factors Associated for COVID19 Severity Among Patients Treated at Selgalu Treatment Center Assosa in Ethiopia: A Case-Control Study

Background

Most people infected with the SARS-CoV-2 virus experienced mild to moderate respiratory illness and recovered without requiring special treatment. However, some became seriously ill with conditions that require medical attention and intensive care unit (ICU) admission. Severity varies among individuals; this may be due to age differences and the presence of underlying disease conditions.

Objective

To identify factors associated with disease severity among COVID19 patients treated at Selgalu treatment center from November 1, 2020, to April 30, 2021.

Methods

A case–control study was implemented among patients admitted to Selgalu COVID19 treatment center in October 2021. A 210 (70 severe disease and 140 non-severe diseases), cases (disease severity), and controls (non-severe disease). Data entered to Epi data 4.6, exported to SPSS 25. A Chi-square test with a p-value of <0.05 was used as a statistically significant difference between characteristics of disease severity and non-severity of disease. Multivariable binary logistic regression was used to determine variables associated with disease severity based on an adjusted odds ratio with 95% CI and p-value < 0.05.

Results

210 (70 cases and 140 controls) selected charts in this study. Patients age category between 40–59 [AOR: 5.30 (2.27–12.34)], aged 60 or older [AOR: 3.85 (1.39–10.64)], patients with fever [AOR: 3.98 (1.59–9.96)], fatigue [AOR: 3.14 (1.50–6.54)], and hypertensive patients [AOR: 3.68 (1.53–8.82)] were significantly predictors for COVID19 disease severity after adjusting for other variables.

Conclusion

From this study, we conclude that being age 60 or older and 40–59 age groups, having symptoms of fever, fatigue, and underlying comorbid illness hypertension. Were identified a significant predictor of severe COVID-19 disease; despite our limitation of study data highlights the important factors associated with disease severity with covid19 admitted to Selgalu treatment center.

Multimorbidity Profile of COVID-19 Deaths in Portugal during 2020

Background: COVID-19 is caused by SARS-CoV-2 infection and has reached pandemic proportions. Since then, several clinical characteristics have been associated with poor outcomes. This study aimed to describe the morbidity profile of COVID-19 deaths in Portugal. Methods: A study was performed including deaths certificated in Portugal with “COVID-19” (ICD-10: U07.1 or U07.2) coded as the underlying cause of death from the National e-Death Certificates Information System between 16 March and 31 December 2020. Comorbidities were derived from ICD-10 codes using the Charlson and Elixhauser indexes. The resident Portuguese population estimates for 2020 were used. Results: The study included 6701 deaths (death rate: 65.1 deaths/100,000 inhabitants), predominantly males (72.1). The male-to-female mortality ratio was 1.1. The male-to-female mortality rate ratio was 1.2; however, within age groups, it varied 5.0–11.4-fold. COVID-19 deaths in Portugal during 2020 occurred mainly in individuals aged 80 years or older, predominantly in public healthcare institutions. Uncomplicated hypertension, uncomplicated diabetes mellitus, congestive heart failure, renal failure, cardiac arrhythmias, dementia, and cerebrovascular disease were observed among COVID-19 deceased patients, with prevalences higher than 10%. A high prevalence of zero morbidities was registered using both the Elixhauser and Charlson comorbidities lists (above 40.2%). Nevertheless, high multimorbidity was also identified at the time of COVID-19 death (about 36.5%). Higher multimorbidity levels were observed in men, increasing with age up to 80 years old. Zero-morbidity prevalence and high multimorbidity prevalences varied throughout the year 2020, seemingly more elevated in the mortality waves’ peaks, suggesting variation according to the degree of disease incidence at a given period. Conclusions: This study provides detailed sociodemographic and clinical information on all certificated deaths from COVID-19 in Portugal during 2020, showing complex and extreme levels of morbidity (zero-morbidity vs. high multimorbidity) dynamics during the first year of the pandemic in Portugal.

Mortality Predictive Value of APACHE II and SOFA Scores in COVID-19 Patients in the Intensive Care Unit

Background. COVID-19 pandemic has become a global dilemma since December 2019. Are the standard scores, such as acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) score, accurate for predicting the mortality rate of COVID-19 or the need for new scores? We aimed to evaluate the mortality predictive value of APACHE II and SOFA scores in critically ill COVID-19 patients. Methods. In a cohort study, we enrolled 204 confirmed COVID-19 patients admitted to the intensive care units at the Imam Khomeini hospital complex. APACHE II on the first day and daily SOFA scoring were performed. The primary outcome was the mortality rate in the nonsurvived and survived groups, and the secondary outcome was organ dysfunction. Two groups of survived and nonsurvived patients were compared by the chi-square test for categorical variables and an independent sample t-test for continuous variables. We used logistic regression models to estimate the mortality risk of high APACHE II and SOFA scores. Result. Among 204 severe COVID-19 patients, 114 patients (55.9%) expired and 169 patients (82.8%) had at least one comorbidity that 103 (60.9%) of them did not survive ( $P=0.002$ ). Invasive mechanical ventilation and its duration were significantly different between survived and nonsurvived groups ( $P\le 0.001$ and $P=0.002$ , respectively). Mean APACHE II and mean SOFA scores were significantly higher in the nonsurvived than in the survived group (14.4 ± 5.7 vs. 9.5 ± 5.1, $P\le 0.001$ , 7.3 ± 3.1 vs. 3.1 ± 1.1, $P\le 0.001$ , respectively). The area under the curve was 89.5% for SOFA and 73% for the APACHE II score. Respiratory diseases and malignancy were risk factors for the mortality rate ( $P=0.004$ and $P=0.007$ , respectively) against diabetes and hypertension. Conclusion. The daily SOFA was a better mortality predictor than the APACHE II in critically ill COVID-19 patients. But they could not predict death with high accuracy. We need new scoring with consideration of the prognostic factors and daily evaluation of changes in clinical conditions.

Predictors of severity and mortality in COVID-19 patients

Background

Due to limited capacity, health care systems worldwide have been put in challenging situations since the emergence of COVID-19. To prioritize patients who need hospital admission, a better understanding of the clinical predictors of disease severity is required. In the current study, we investigated the predictors of mortality and severity of illness in COVID-19 from a single center in Cairo, Egypt.

Methods

This retrospective cohort study included 175 patients hospitalized with COVID-19 pneumonia and had positive real-time polymerase chain reaction (RT-PCR) results for SARS-CoV-2 from 1 May 2020 to 1 December 2020. Severe COVID-19 was defined as requiring high-flow oxygen (flow rate of more than 8 L/min or use of high flow oxygen cannula), noninvasive ventilation, or invasive mechanical ventilation at any time point during the hospitalization. We used univariate and multivariate regression analyses to examine the differences in patient demographics and clinical and laboratory data collected during the first 24 h of hospitalization related to severe disease or death in all 175 patients.

Results

Sixty-seven (38.3%) of the study subjects had a severe or critical disease. Elevated d-dimer, leukocytosis, and elevated CRP were found to be independent predictors of severe disease. In-hospital mortality occurred in 34 (19.4%) of the cases. Elevated TLC, urea, the use of invasive mechanical ventilation, and the presence of respiratory bacterial co-infection were found to be independently associated with mortality.

Conclusion

Clinical and laboratory data of COVID-19 patients at their hospital admission may aid clinicians in the early identification and triage of high-risk patients.

The Role of Demographic, Clinical, and Laboratory Characteristics in Predicting the In-Hospital Outcomes of Patients With COVID-19

Objective

In this study, we aimed to analyze the role of initial patient characteristics obtained at admission (including sociodemographic, clinical, and laboratory findings) in predicting the outcomes in patients with coronavirus disease 2019 (COVID-19).

Methods

This descriptive, retrospective cohort study included all hospital-admitted COVID-19-confirmed cases at a tertiary academic center in Jeddah, the Kingdom of Saudi Arabia (KSA), from March to June 2020. A total of 656 patients with a mean age of 50 ± 19.4 years were included.

Results

Of all the patients recruited, 19.3% required ICU admission, and 19% required mechanical ventilation. The majority (79.9%) of the patients recovered from COVID-19 and were discharged, while 20.1% of them died. Patients with advanced age (p=0.005), male sex (p=0.009), low platelet counts (p=0.015), low hemoglobin levels (p=0.004), low albumin levels (p=0.003), high alkaline phosphatase levels (p=0.002), high blood urea nitrogen levels (p<0.001), and high lactate dehydrogenase levels (p<0.001) were more likely to die.

Conclusion

Based on our findings, it can be inferred that mortality in COVID-19 is highly associated with advanced age and male gender, low platelet counts, low hemoglobin levels, low albumin levels, high alkaline phosphatase levels, high blood urea nitrogen levels, high lactate dehydrogenase levels, tachypnea, and requirement for mechanical ventilation.

Cardiac Magnetic Resonance in the Evaluation of COVID-19

Cardiovascular involvement following COVID-19 is heterogeneous, prevalent and is often missed by echocardiography and serum biomarkers (such as troponin I and brain natriuretic peptide). Cardiac magnetic resonance (CMR) is the gold standard non-invasive imaging modality to phenotype unique populations after COVID-19, such as competitive athletes with a heightened risk of sudden cardiac death, patients with multisystem inflammatory syndrome, and people suspected of having COVID-19 vaccine-induced myocarditis. This review summarises the key attributes of CMR, reviews the literature that has emerged for using CMR for people who may have COVID-19-related complications after COVID-19, and offers expert opinion regarding future avenues of investigation and the importance of reporting findings.

Predictors of Covid-19 level of concern among older adults from the health and retirement study

The purpose of this longitudinal study is to construct a prediction model for Covid-19 level of concern using established Covid-19 socio-demographic, lifestyle and health risk characteristics and to examine specific contributions of obesity-related cardiometabolic health characteristics as predictors of Covid-19 level of concern among a representative sample of U.S. older adults. We performed secondary analyses of existing data on 2872 2006–2020 Health and Retirement Study participants and examined 19 characteristics in relation to the outcome of interest using logistic regression and machine learning algorithms. In mixed-effects ordinal logistic regression models, a history of diabetes, stroke as well as 1–2 cardiometabolic risk factors and/or chronic conditions were associated with greater Covid-19 level of concern, after controlling for confounders. Female sex, birth cohort, minority race, Hispanic ethnicity and total wealth as well as depressive symptoms were associated with higher level of Covid-19 concern, and education was associated with lower level of Covid-19 concern in fully adjusted mixed-effects ordinal logistic regression models. The selected socio-demographic, lifestyle and health characteristics accounted for < 70% of the variability in Covid-19 level of concern based on machine learning algorithms. Independent risk factors for Covid-19 level of concern among U.S. older adults include socio-demographic characteristics and depressive symptoms. Advanced research is needed to identify relevant predictors and elucidate underlying mechanisms of observed relationships.

Prognostic significance of CHADS2 and CHA2DS2-VASc scores to predict unfavorable outcomes in hospitalized patients with COVID-19

Introduction: Owing to the imposed burden of the coronavirus disease 2019 (COVID-19),the need for stratifying the prognosis of patients has never been timelier. Hence, we aimed to ascertain the value of CHADS2, CHA2DS2-VASc, and CHA2DS2-VASc-M (one point for male instead of female) scores to predict unfavorable outcomes in COVID-19 patients. Methods: We enrolled consecutive patients above 18 years of age with confirmed COVID-19,who were admitted between February 16 and November 1, 2020. The primary endpoint of this study was three-month all-cause mortality. The secondary endpoints were considered four major in-hospital clinical features, including acute respiratory distress syndrome, cardiac injury,acute kidney injury, and mechanical ventilation. Results: A total of 1,406 hospitalized COVID-19 patients were studied, among which 301(21.40%) patients died during the follow-up period. Regarding the risk scores, CHADS 2≥1,CHA2DS2-VASc≥2, and CHA2DS2-VASc-M≥2 were significantly associated with mortality. The performance of all risk scores for predicting mortality was satisfactory (area under the curve:0.668, 0.668, and 0.681, respectively). Appraising secondary endpoints, we found that all three risk scores were associated with increased risk of acute respiratory distress syndrome, cardiac injury, acute kidney injury, and mechanical ventilation. Lastly, we revealed that all risk scores were significantly correlated with serum levels of laboratory biomarkers. Conclusion: Our analysis illustrated that the CHADS2, CHA2DS2-VASc, and CHA2DS2-VASc-Mscores could aid prognostication of unfavorable outcomes in COVID-19 patients. Therefore,these easily calculable methods could be integrated into the overall therapeutic strategy to guide the COVID-19 management more accurately.

xsGastrointestinal symptoms are associated with a lower risk of hospitalization and mortality and Outcomes in COVID-19

Background

We aimed to find the association between gastrointestinal (GI) and respiratory symptoms with mortality and hospitalization among COVID-19 patients.

Methods

We analyzed the registered data of COVID-19 patients from February 20, 2020, to March 10, 2021. Depending on the patients’ disease symptoms, four categories were defined: patients with only GI symptoms, patients with only respiratory symptoms, patients with both symptoms, and patients with other symptoms. Logistic regression analysis was used to assess the association of groups with outcomes.

Results

A total of 42,964 patients from 23 hospitals were included, of which 26.5% patients had at least one or more GI symptoms. Of total patients, 51.58% patients were hospitalized among which 22.8% had at least one or more GI symptoms. GI symptoms significantly decreased the odds of mortality (OR 0.72, 95% CI 0.56–0.92), but respiratory symptoms increased the odds for mortality (1.36: 1.24–1.50), compared with patients with other symptoms. Moreover, the odds ratio of patients who had both respiratory and GI symptoms increased (1.52: 1.31–1.78) compared with patients with other symptoms. The same results were observed for hospitalization as the outcome.

Conclusions

Our study showed that the presence of GI symptoms in COVID-19 at the time of admission was associated with a lower odds of hospitalization and mortality; however, this association had higher odds for respiratory symptoms.

Biomarkers Associated with Cardiovascular Disease in COVID-19

Coronavirus disease-19 (COVID-19) emerged late December 2019 in the city of Wuhan, China and has since spread rapidly all over the world causing a global pandemic. While the respiratory system is the primary target of disease manifestation, COVID-19 has been shown to also affect several other organs, making it a rather complex, multi-system disease. As such, cardiovascular involvement has been a topic of discussion since the beginning of the COVID-19 pandemic, primarily due to early reports of excessive myocardial injury in these patients. Treating physicians are faced with multiple challenges in the management and early triage of patients with COVID-19, as disease severity is highly variable ranging from an asymptomatic infection to critical cases rapidly deteriorating to intensive care treatment or even fatality. Laboratory biomarkers provide important prognostic information which can guide decision making in the emergency department, especially in patients with atypical presentations. Several cardiac biomarkers, most notably high-sensitive cardiac troponin (hs-cTn) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), have emerged as valuable predictors of prognosis in patients with COVID-19. The purpose of this review was to offer a concise summary on prognostic cardiac biomarkers in COVID-19 and discuss whether routine measurements of these biomarkers are warranted upon hospital admission.

Cardiovascular drugs and analysis of potential risk factors associated with mortality in severe coronavirus disease 2019 patients

OBJECTIVES

Cardiovascular diseases are also considered to increase the risk of death in COVID-19 patients. However, real-world data concerning the risk factors for death in patients with severe COVID-19 still remain vague. This study aimed to identify the potential risk factors associated with mortality in severe COVID-19 patients.

METHODS

All consecutive patients admitted to the intensive care unit (ICU) of our institute for COVID-19 for severe COVID-19 pneumonia from April 1, 2020 to July 20, 2020 were included in the analysis. Patient characteristics, including complete medical history and comorbid diseases, blood test results during admission and on day 7, and clinical characteristics were compared between survivors and nonsurvivors.

RESULTS

There was no significant difference between survivors and nonsurvivors regarding age, gender, and preexisting cardiovascular diseases. Moreover, the rate of the medications including angiotensin-converting enzyme (ACE) inhibitor and angiotensin receptor blockers did not differ between survivors and nonsurvivors. The peak C-reactive protein (CRP), procalcitonin, fibrinogen, and d-dimer levels and the rate for chronic renal failure were significantly higher in nonsurvivors compared with survivors. Intubated patients had a higher risk of death than the others had.

CONCLUSIONS

This study failed to demonstrate a significant difference in preexisting cardiovascular diseases and cardiovascular medications between survivors and nonsurvivors who were admitted to ICU for severe COVID-19. Our findings indicate that the presence of chronic renal failure, a high peak ferritin concentration, and the need for invasive mechanical ventilation appear predictive for mortality. We propose that these risk factors should be taken into account in defining the risk status of severe COVID-19 patients admitted to the ICU.

Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection

Background

Reports on medium and long-term sequelae of SARS-CoV-2 infections largely lack quantification of incidence and relative risk. We describe the rationale and methods of the Innovative Support for Patients with SARS-CoV-2 Registry (INSPIRE) that combines patient-reported outcomes with data from digital health records to understand predictors and impacts of SARS-CoV-2 infection.

Methods

INSPIRE is a prospective, multicenter, longitudinal study of individuals with symptoms of SARS-CoV-2 infection in eight regions across the US. Adults are eligible for enrollment if they are fluent in English or Spanish, reported symptoms suggestive of acute SARS-CoV-2 infection, and if they are within 42 days of having a SARS-CoV-2 viral test (i.e., nucleic acid amplification test or antigen test), regardless of test results. Recruitment occurs in-person, by phone or email, and through online advertisement. A secure online platform is used to facilitate the collation of consent-related materials, digital health records, and responses to self-administered surveys. Participants are followed for up to 18 months, with patient-reported outcomes collected every three months via survey and linked to concurrent digital health data; follow-up includes no in-person involvement. Our planned enrollment is 4,800 participants, including 2,400 SARS-CoV-2 positive and 2,400 SARS-CoV-2 negative participants (as a concurrent comparison group). These data will allow assessment of longitudinal outcomes from SARS-CoV-2 infection and comparison of the relative risk of outcomes in individuals with and without infection. Patient-reported outcomes include self-reported health function and status, as well as clinical outcomes including health system encounters and new diagnoses.

Results

Participating sites obtained institutional review board approval. Enrollment and follow-up are ongoing.

Conclusions

This study will characterize medium and long-term sequelae of SARS-CoV-2 infection among a diverse population, predictors of sequelae, and their relative risk compared to persons with similar symptomatology but without SARS-CoV-2 infection. These data may inform clinical interventions for individuals with sequelae of SARS-CoV-2 infection.

Risk factors associated with hospital transfer among mild or asymptomatic COVID-19 patients in isolation facilities in Tokyo: a case-control study

•

Facility-based isolation for COVID-19 in Tokyo involved remote health observation.

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This study was conducted in a prehospital setting using multivariable analysis.

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Older age, male sex, and higher BMI were associated with transfers to hospital.

•

Comorbidities, such as bronchial asthma, increased the risk for hospital transfer.

•

Some patients requiring urgent oxygenation showed few signs of dyspnea (silent hypoxia).

Objectives

The Tokyo Metropolitan Government has been implementing facility-based isolation of asymptomatic/mild coronavirus disease (COVID-19) patients to facilitate timely hospital referral. However, there are only a few published studies in prehospital settings, and the factors associated with hospital transfer are unclear. Our study identified the factors associated with COVID-19 deterioration in a prehospital setting.

Methods

This case-control study assessed the risk factors for hospital transfer from isolation facilities and the need for ambulance transport due to deterioration among COVID-19 patients, using multivariate logistic regression analysis.

Results

In total, 10 590 patients (median age 34 years), with male predominance (61.1%), were included. 367 (3.5%) were transferred to hospital, of whom 44 (12.0%) required ambulance transport. Hypertension, diabetes, and bronchial asthma were prevalent in 704 (6.6%), 195 (1.8%), and 305 (2.9%) patients, respectively. After adjustment, older age, male sex, higher body mass index (BMI), and comorbidities (including diabetes, inflammatory bowel disease, and bronchial asthma) were associated with hospital transfer. Older age, male sex, and higher BMI significantly increased the risk of transfer by ambulance.

Conclusions

Our results may be beneficial for the development of intervention measures for probable future COVID-19 waves.

Risk Factors Associated with In-Hospital Mortality in Iranian Patients with COVID-19: Application of Machine Learning

Introduction: Predicting the mortality risk of COVID-19 patients based on patient’s physiological conditions and demographic characteristics can help optimize resource consumption along with the provision of effective medical services for patients. In the current study, we aimed to develop several machine learning models to forecast the mortality risk in COVID-19 patients, evaluate their performance, and select the model with the highest predictive power.

Material and methods: We conducted a retrospective analysis of the records belonging to COVID-19 patients admitted to one of the main hospitals of Qazvin located in the northwest of Iran over 12 months period. We selected 29 variables for developing machine learning models incorporating demographic factors, physical symptoms, comorbidities, and laboratory test results. The outcome variable was mortality as a binary variable. Logistic regression analysis was conducted to identify risk factors of in-hospital death.

Results: In prediction of mortality, Ensemble demonstrated the maximum values of accuracy (0.8071, 95%CI: 0.7787, 0.8356), F1-score (0.8121 95%CI: 0.7900, 0.8341), and AUROC (0.8079, 95%CI: 0.7800, 0.8358). Including fourteen top-scored features identified by maximum relevance minimum redundancy algorithm into the subset of predictors of ensemble classifier such as BUN level, shortness of breath, seizure, disease history, fever, gender, body pain, WBC, diarrhea, sore throat, blood oxygen level, muscular pain, lack of taste and history of drug (medication) use are sufficient for this classifier to reach to its best predictive power for prediction of mortality risk of COVID-19 patients.

Conclusions: Study findings revealed that old age, lower oxygen saturation level, underlying medical conditions, shortness of breath, seizure, fever, sore throat, and body pain, besides serum BUN, WBC, and CRP levels, were significantly associated with increased mortality risk of COVID-19 patients. Machine learning algorithms can help healthcare systems by predicting and reduction of the mortality risk of COVID-19 patients.

Patient-level and hospital-level variation and related time trends in COVID-19 case fatality rates during the first pandemic wave in England: multilevel modelling analysis of routine data

BACKGROUND

A report suggesting large between-hospital variations in mortality after admission for COVID-19 in England attracted much media attention but used crude rates. We aimed to quantify these variations between hospitals and over time during England's first wave (March to July 2020) and assess available patient-level and hospital-level predictors to explain those variations.

METHODS

We used administrative data for England, augmented by hospital-level information. Admissions were extracted with COVID-19 codes. In-hospital death was the primary outcome. Risk-adjusted mortality ratios (standardised mortality ratios) and interhospital variation were calculated using multilevel logistic regression. Early-wave (March to April) and late-wave (May to July) periods were compared.

RESULTS

74 781 admissions had a primary diagnosis of COVID-19, with 21 984 in-hospital deaths (29.4%); the 30-day total mortality rate was 28.8%. The crude in-hospital death rate fell in all ages and overall from 32.9% in March to 13.4% in July. Patient-level predictors included age, male gender, non-white ethnic group (early period only) and several comorbidities (obesity early period only). The only significant hospital-level predictor was daily COVID-19 admissions in the late period; we did not find a relation with staff absences for COVID-19, mechanical ventilation bed occupancies, total bed occupancies or bed occupancies for COVID-19 admissions in either period. Just 4 (3%) and 2 (2%) hospitals were high, and 5 (4%) and 0 hospitals were low funnel plot mortality outliers at 3 SD for early and late periods, respectively, after risk adjustment. We found no strong correlation between early and late hospital-level mortality (r=0.17, p=0.06).

CONCLUSIONS

There was modest variation in mortality following admission for COVID-19 between English hospitals after adjustment for risk and random variation, in marked contrast to early media reports. Early-period mortality did not predict late-period mortality.

Predictors of mortality in children admitted with SARS-CoV-2 infection to a tertiary care hospital in North India

AIM

To compare the demographic, clinical, laboratory and radiological parameters of patients with different clinical outcomes (death or discharge) and analyse them to find out the potential predictors for mortality in children hospitalised with SARS-CoV-2 infection.

METHODS

Retrospective chart review of all patients less than 18 years of age with laboratory-confirmed SARS-CoV-2 infection and requiring hospital admission between 16 April 2020 and 31 October 2020.

RESULTS

Of 255 children with SARS-CoV-2 infection, 100 patients (median age 62.5 months, 59% males, 70% with moderate to severe disease) were hospitalised, of whom 27 died (median age 72 months, 59% males and 30% severely underweight). The subgroup with comorbidities (n = 14) was older (median age 126 months) and had longer duration of stay (median 10 days). Fever and respiratory symptoms were comparable while gastrointestinal symptoms were more common among non-survivors. Hypoxia at admission (odds ratio (OR) 5.48, P = 0.001), multiorgan dysfunction (OR 75.42, P = 0.001), presence of acute kidney injury (OR 11.66, P = 0.001), thrombocytopenia (OR 4.40, P = 0.003) and raised serum C-reactive protein (CRP) (OR 4.69, P = 0.02) were independently associated with mortality. The median time from hospitalisation to death was 3 days. The deceased group had significantly higher median levels of inflammatory parameters and a higher incidence of complications (myocarditis, encephalitis, acute respiratory distress syndrome and shock).

CONCLUSIONS

Hypoxia at admission, involvement of three or more organ systems, presence of acute kidney injury, thrombocytopenia and raised serum C-reactive protein were found to be independently associated with increased odds of in-hospital mortality in children admitted with SARS-CoV-2 infection.

Characteristics associated with poor COVID-19 outcomes in individuals with systemic lupus erythematosus: data from the COVID-19 Global Rheumatology Alliance

Aim

To determine characteristics associated with more severe outcomes in a global registry of people with systemic lupus erythematosus (SLE) and COVID-19.

Methods

People with SLE and COVID-19 reported in the COVID-19 Global Rheumatology Alliance registry from March 2020 to June 2021 were included. The ordinal outcome was defined as: (1) not hospitalised, (2) hospitalised with no oxygenation, (3) hospitalised with any ventilation or oxygenation and (4) death. A multivariable ordinal logistic regression model was constructed to assess the relationship between COVID-19 severity and demographic characteristics, comorbidities, medications and disease activity.

Results

A total of 1606 people with SLE were included. In the multivariable model, older age (OR 1.03, 95% CI 1.02 to 1.04), male sex (1.50, 1.01 to 2.23), prednisone dose (1–5 mg/day 1.86, 1.20 to 2.66, 6–9 mg/day 2.47, 1.24 to 4.86 and ≥10 mg/day 1.95, 1.27 to 2.99), no current treatment (1.80, 1.17 to 2.75), comorbidities (eg, kidney disease 3.51, 2.42 to 5.09, cardiovascular disease/hypertension 1.69, 1.25 to 2.29) and moderate or high SLE disease activity (vs remission; 1.61, 1.02 to 2.54 and 3.94, 2.11 to 7.34, respectively) were associated with more severe outcomes. In age-adjusted and sex-adjusted models, mycophenolate, rituximab and cyclophosphamide were associated with worse outcomes compared with hydroxychloroquine; outcomes were more favourable with methotrexate and belimumab.

Conclusions

More severe COVID-19 outcomes in individuals with SLE are largely driven by demographic factors, comorbidities and untreated or active SLE. Patients using glucocorticoids also experienced more severe outcomes.

Risk Factors for COVID-19: Diabetes, Hypertension, and Obesity

INTRODUCTION

The recent global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has involved more than 7 million people worldwide and been associated with more than 400,000 deaths. No clear information is known about all the potential risk factors for COVID-19 or what factors adversely influence its clinical course and mortality. Therefore, we analyzed the role of obesity, type 2 diabetes, and hypertension as risk factors for COVID-19.

METHODS

We identified articles for inclusion by searching PubMed and Google Scholar (last accessed 15 June 2020). Retrospective review of literature. Analysis of epidemiological data concerning obesity prevalence and COVID-19 incidence, particularly in Italy and the USA.

RESULTS

Data from several retrospective studies of prevalence showed that patients with hypertension, type 2 diabetes, and obesity may have more severe COVID-19, intensive care unit admission, and higher mortality rates, but it is not definitively clear if this is an independent association. In general, the prevalence of obesity in patients with COVID-19 seems to be the same as that of the general population throughout the world; however, obesity seems to be associated with more severe disease and mortality in younger (< 60 years) patients. Similar effects seem to occur in patients with diabetes and/or hypertension but at older ages (> 60 years). In strict connection, it has been proposed that the use of drugs inhibiting angiotensin-converting enzyme 2 (ACE-2) or dipeptidyl dipeptidase 4 (DPP-4) might influence viral activity and disease severity since ACE-2 and DPP-4 receptors mediate SARS-CoV-2 entry into the host cells; however, no evidence exists to date that shows that this may be the case.

CONCLUSION

Overall, diabetes, hypertension, and obesity seem to negatively affect the clinical course and disease outcome in patients with COVID-19. However, these data need further confirmation by studies with more accurate data registration.

An analytical model of population level chronic conditions and COVID-19 related hospitalization in the United States

Background

The surge in the COVID-19 related hospitalization has been straining the US health system. COVID-19 patients with underlying chronic conditions have a disproportionately higher risk of hospitalization and intensive care unit (ICU) admission. We developed a retrospective analytical model of COVID-19 related hospitalizations and ICU admissions linked to each of the three major chronic conditions – hypertension, diabetes, and cardiovascular diseases (CVD).

Methods

Based on the differential probability of hospitalization of the COVID-19 patients with and without a chronic condition, we estimate a baseline cumulative hospitalization rate and ICU admission rate using the population level chronic condition prevalence from the 2019 Behavioral Risk Factor Surveillance System survey. Next, we estimate the hospitalization and ICU admission rates under an alternative scenario of a lower prevalence of the same chronic condition, aligned with the World Health Organization target of 25% relative reduction of prevalence by 2025. We then compare the outcomes of the baseline and the alternative scenarios.

Results

We estimate that the lower prevalence of hypertension would have lowered the cumulative hospitalization and ICU admission rates by more than 2.5%. The lower prevalence of diabetes and CVD would lower the cumulative hospitalization rate by 0.6% and 1.4% respectively. The decrease in the rates would have been relatively higher among Black and elderly (age 55+).

Conclusions

Our model, thus, provides evidence on the importance of prevention, control, and management of chronic conditions to lessen the overwhelming financial and public health burden on the health system during a pandemic like the COVID-19.

COVID-19 pandemic impact on trajectories in cardiometabolic health, physical activity and functioning among adults from the 2006-2020 Health and Retirement Study

Background

The aim of this study was to evaluate the impact of the COVID-19 pandemic on trajectories in cardiometabolic health, physical activity, and functioning among U.S. older adults, overall and according to selected baseline sociodemographic characteristics.

Methods

We performed secondary analyses using longitudinal data on 1,372 participants from the 2006–2020 Health and Retirement Study. Pre-post COVID-19 pandemic onset was examined in relation to body mass index (BMI), number of cardiometabolic risk factors and/or chronic conditions, physical activity, Activities of Daily Living (ADL), and Instrumental Activities of Daily Living (IADL) using mixed-effects regression models and group-based trajectory models.

Results

The COVID-19 pandemic was associated with significantly increased BMI (β = 1.39, 95% confidence interval [CI]: 0.74, 2.03). Furthermore, the odds of having at least one cardiometabolic risk factor and/or chronic disease increased pre-post COVID-19 onset (odds ratio 1.16, 95% CI: 1.00, 1.36), whereas physical functioning worsened pre-post COVID-19 onset (ADL: β = 1.11, 95% CI: 0.94, 1.28; IADL: β = 0.59, 95% CI: 0.46, 0.73). The pre-post COVID-19 period (2018–2020) showed a stable group of trajectories, with low, medium and high levels of the selected health indicators. Health disparities according to sex, race/ethnicity, educational level, work status, and total wealth are highlighted.

Conclusions

The COVID-19 pandemic onset appears to worsen cardiometabolic health and physical functioning among U.S. older adults, with clusters of individuals defined by selected sociodemographic characteristics experiencing distinct trajectories pre-post COVID-19 pandemic onset.

Coronavirus Disease 2019 in Saudi Arabia: A Nationwide Epidemiological Characterization Study

Background

On March 11th, 2020, The World Health Organization (WHO) declared that the COVID-19 is a pandemic due to its worldwide spread. The COVID-19 pandemic has extended its impact to Saudi Arabia. By mid-February 2021, The Kingdom of Saudi Arabia has reported more than 373,000 COVID-19 cases impacting different population categories (i.e., male, female, different age groups, comorbidities status). The objective of this nationwide study was to describe and explore the characteristics of hospitalized patients diagnosed with COVID-19 in Saudi Arabia.

Methods

This study was an observational epidemiological study based on collected clinical data from ten health institutions across all regions in Saudi Arabia. The study was conducted during the period from March 2nd, 2020, to January 31st, 2021. The data were collected included demographics, medical information, medications, and laboratory and diagnostic. More detailed information on usually missing factors such as smoking status, comorbidities, length of hospital stay were also collected. Both descriptive and inferential analyses were conducted using the statistical analysis software “SAS®” version 9.4.

Results

During the study period, 5286 patients were included in this study. Of these, (79.15%) were male. Of all 5286 patients, quite a high number of the studied population 2010 (38.02%) were smokers. The majority of the patients 3436 (65%) were reported to have comorbidities, with hypertension being the most common disease 1725 (32.6%), followed by diabetes 1641(31.04%). A high proportion of the patients, 2220 patients (41.99%), were admitted to the intensive care unit; of these, (33.52%) were on mechanical ventilation. Most patients received anticoagulant prophylaxis medications (n = 4414, 83.5%). All patients were given more than one antibiotic prophylaxis. Overall, the median hospital stay was 5.5 days, and the median length in the intensive care unit was 4.26 days. Around (89.14%) of patients were discharged from the hospital, and (10.8%) died.

Conclusion

In this real-world study utilizing a large sample size, this study provides confirmatory results on the COVID-19 patients characteristics that are similar to other populations. Healthcare professionals need to give COVID-19 patients with specific characteristics including smoking, diabetes mellitus and cardiac disease more care to avoid losing these patients.

Role of Polypeptide Inflammatory Biomarkers in the Diagnosis and Monitoring of COVID-19

The COVID-19 (coronavirus disease 2019) pandemic that took over the world in December 2019 has had everlasting devastating impacts on the lives of people globally. It manifests a huge symptom spectrum ranging from asymptomatic to critically ill patients with an unpredictable outcome. Timely diagnosis and assessment of disease severity is imperative for effective treatment. Possibilities exist that by the time symptoms appear the viral load might increase beyond control. However, it is advisable to get adequately diagnosed as soon as the first symptom appears. There is an immediate requirement of reliable biomarkers of COVID-19 manifesting an early onset for effective clinical management, stratification of high risk patients and ensuring ideal resource allocation. In this review, we attempt to explore and describe important polypeptide inflammatory biomarkers, namely C-reactive protein, Procalcitonin, Ferritin, Lactate Dehydrogenase, Serum amyloid A, Interleukin-6, Tumor necrosis factor-alpha and LIGHT used in the detection and management of COVID-19. Viral pathogenesis and the role of these inflammatory biomarkers is highlighted, based on the evidences available till date. An integrative data monitoring along with their correlation with the natural disease progression is of utmost importance in the management of COVID-19. So further research and in-depth analysis of these biomarkers is warranted in the present scenario.

Cardiac Manifestations in Patients with COVID-19: A Scoping Review

Background:

Coronavirus disease 2019 (COVID-19), commonly affects the lungs, but the involvement of other organs, particularly the heart, is highly prevalent as has been reported in several studies. The overall aim of this review was to provide an in-depth description of the available literature related to the cardiac system and COVID-19 infection. It focuses on type and the frequency of cardiac manifestations, clinical parameters and cardiac biomarkers that support the prognosis of COVID-19 patients, and the cardiac adverse events and outcomes related to pharmacotherapy.

Methods:

A scoping review was conducted searching Embase, PubMed, Epistomonikos, Medrxiv, BioRxiv databases, up to November 2020, for systematic reviews relevant to cardiac manifestations in adult COVID-19 patients. Relevant articles were screened and extracted to summarize key outcomes and findings.

Results:

A total of 63 systematic reviews met the inclusion criteria. The overall frequency of acute cardiac injury ranged from 15% to 33% in the reporting studies. The main cardiac complications were arrhythmias (3.1% to 6.9% in non-severe patients, 33.0% to 48.0% in severe disease), acute coronary syndromes (6% to 33% in severe disease), and myocarditis. Most studies found no association with the use of Renin-angiotensin-aldosterone system inhibitors (RAASI) with COVID-19 outcomes such as susceptibility to infection, hospitalization, severity, and mortality.

Conclusion:

This study provided an overview of the several cardiac complications associated with Covid-19. Cardiac injury, arrhythmias, myocarditis, cardiac failure, and acute coronary syndrome, are prevalent and clinically significant and associated with COVID-19 disease severity and mortality. Other studies are needed to clearly identify what is the part of viral heart infection and what is the part of cardiac injury secondary to acute respiratory failure and inflammation. In the therapeutic field, these systematic reviews gave heterogenous results. This underlines the importance of randomized trials to determine the right therapeutic approach.

Predictors of Mortality in Hemodialyzed Patients after SARS-CoV-2 Infection

Introduction: The determinants of COVID-19 mortality are well-characterized in the general population. Less numerous and inconsistent data are among the maintenance hemodialysis (HD) patients, who are the population most at risk of an unfavorable prognosis. Methods: In this retrospective cohort study we included all adult HD patients from the Pomeranian Voivodeship, Poland, with laboratory-confirmed SARS-CoV-2 infection hospitalized between 6 October 2020 and 28 February 2021, both those who survived, and also those who died. Demographic, clinical, treatment, and laboratory data on admission, were extracted from the electronic medical records of the dedicated hospital and patients’ dialysis unit, and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with 3-month all-cause mortality. Results: The 133 patients (53.38% males) aged 73.0 (67–79) years, with a median duration of hemodialysis of 42.0 (17–86) months, were included in this study. At diagnosis, the majority were considered to have a mild course (34 of 133 patients were asymptomatic, another 63 subjects presented mild symptoms), while 36 (27.07%) patients had low blood oxygen saturation and required oxygen supplementation. Three-month mortality was 39.08% including an in-hospital case fatality rate of 33.08%. Multivariable logistic regression showed that the frailty clinical index of 4 or greater (OR 8.36, 95%CI 1.81–38.6; p < 0.01), D-Dimer of 1500 ng/mL or greater (6.00, 1.94–18.53; p < 0.01), and CRP of >118 mg/L at admission (3.77 1.09–13.01; p = 0.04) were found to be predictive of mortality. Conclusion: Very high 3-month all-cause mortality in hospitalized HD patients was determined mainly by frailty. High CRP and D-dimer levels upon admission further confer mortality risk.

Predictive determinants of overall survival among re-infected COVID-19 patients using the elastic-net regularized Cox proportional hazards model: a machine-learning algorithm

BACKGROUND

Narrowing a large set of features to a smaller one can improve our understanding of the main risk factors for in-hospital mortality in patients with COVID-19. This study aimed to derive a parsimonious model for predicting overall survival (OS) among re-infected COVID-19 patients using machine-learning algorithms.

METHODS

The retrospective data of 283 re-infected COVID-19 patients admitted to twenty-six medical centers (affiliated with Shiraz University of Medical Sciences) from 10 June to 26 December 2020 were reviewed and analyzed. An elastic-net regularized Cox proportional hazards (PH) regression and model approximation via backward elimination were utilized to optimize a predictive model of time to in-hospital death. The model was further reduced to its core features to maximize simplicity and generalizability.

RESULTS

The empirical in-hospital mortality rate among the re-infected COVID-19 patients was 9.5%. In addition, the mortality rate among the intubated patients was 83.5%. Using the Kaplan-Meier approach, the OS (95% CI) rates for days 7, 14, and 21 were 87.5% (81.6-91.6%), 78.3% (65.0-87.0%), and 52.2% (20.3-76.7%), respectively. The elastic-net Cox PH regression retained 8 out of 35 candidate features of death. Transfer by Emergency Medical Services (EMS) (HR=3.90, 95% CI: 1.63-9.48), SpO2≤85% (HR=8.10, 95% CI: 2.97-22.00), increased serum creatinine (HR=1.85, 95% CI: 1.48-2.30), and increased white blood cells (WBC) count (HR=1.10, 95% CI: 1.03-1.15) were associated with higher in-hospital mortality rates in the re-infected COVID-19 patients.

CONCLUSION

The results of the machine-learning analysis demonstrated that transfer by EMS, profound hypoxemia (SpO2≤85%), increased serum creatinine (more than 1.6 mg/dL), and increased WBC count (more than 8.5 (×109 cells/L)) reduced the OS of the re-infected COVID-19 patients. We recommend that future machine-learning studies should further investigate these relationships and the associated factors in these patients for a better prediction of OS.

The impact of COVID-19 on Diabetic Ketoacidosis patients

BACKGROUND AND AIM

Describe the prevalence/outcomes of Diabetic Ketoacidosis (DKA) patients comparing pre- (March-April 2019) and pandemic (March-April 2020) periods.

METHODS

Retrospective cohort of admitted pandemic DKA/COVID-19+ patients comparing prevalence/outcomes to pre-pandemic DKA patients that takes place in Eleven hospitals of New York City Health & Hospitals. Our included participants during the pandemic period were admitted COVID-19+ patients (>18 years) and during the pre-pandemic period were admissions (>18 years) selected through the medical record. We excluded transfers during both periods. The intervention was COVID-19+ by PCR testing. The main outcome measured was mortality during the index hospitalization and secondary outcomes were demographics, medical histories and triage vital signs, and laboratory tests. Definition of DKA: Beta-Hydroxybutyrate (BHBA) (>0.4 mmol/L) and bicarbonate (<15 mmol/L) or pH (<7.3).

RESULTS

Demographics and past medical histories were similar during the pre-pandemic (n = 6938) vs. pandemic (n = 7962) periods. DKA prevalence was greater during pandemic (3.14%, 2.66-3.68) vs. pre-pandemic period (0.72%, 0.54-0.95) (p > 0.001). DKA/COVID-19+ mortality rates were greater (46.3% (38.4-54.3) vs. pre-pandemic period (18%, 8.6-31.4) (p < 0.001). Surviving vs. non-surviving DKA/COVID-19+ patients had more severe DKA with lower bicarbonates by 2.7 mmol/L (1.0-4.5) (p < 0.001) and higher both Anion Gaps by 3.0 mmol/L (0.2-6.3) and BHBA by 2.1 mmol/L (1.2-3.1) (p < 0.001).

CONCLUSIONS

COVID-19 increased the prevalence of DKA with higher mortality rates secondary to COVID-19 severity, not DKA. We suggest DKA screening all COVID-19+ patients and prioritizing ICU DKA/COVID-19+ with low oxygen saturation, blood pressures, or renal insufficiency.

COVID-19 and myocarditis: a systematic review and overview of current challenges

Myocardial inflammation in COVID-19 has been documented. Its pathogenesis is not fully elucidated, but the two main theories foresee a direct role of ACE2 receptor and a hyperimmune response, which may also lead to isolated presentation of COVID-19-mediated myocarditis. The frequency and prognostic impact of COVID-19-mediated myocarditis is unknown. This review aims to summarise current evidence on this topic. We performed a systematic review of MEDLINE and Cochrane Library (1/12/19-30/09/20). We also searched clinicaltrials.gov for unpublished studies testing therapies with potential implication for COVID-19-mediated cardiovascular complication. Eligible studies had laboratory confirmed COVID-19 and a clinical and/or histological diagnosis of myocarditis by ESC or WHO/ISFC criteria. Reports of 38 cases were included (26 male patients, 24 aged < 50 years). The first histologically proven case was a virus-negative lymphocytic myocarditis; however, biopsy evidence of myocarditis secondary to SARS-CoV-2 cardiotropism has been recently demonstrated. Histological data was found in 12 cases (8 EMB and 4 autopsies) and CMR was the main imaging modality to confirm a diagnosis of myocarditis (25 patients). There was a substantial variability in biventricular systolic function during the acute episode and in therapeutic regimen used. Five patients died in hospital. Cause-effect relationship between SARS-CoV-2 infection and myocarditis is difficult to demonstrate. However, current evidence demonstrates myocardial inflammation with or without direct cardiomyocyte damage, suggesting different pathophysiology mechanisms responsible of COVID-mediated myocarditis. Established clinical approaches should be pursued until future evidence support different actions. Large multicentre registries are advisable to elucidate further.

Clinical, demographic, and laboratory characteristics of COVID -19 infection and risk of in-hospital mortality. A single center 4

Background:

Frequent waves of corona virus disease (COVID-19) and lack of specific drugs against that, warrant studies to reduce the morbidity and mortality of this pandemic disease. In this study, we investigated the association between influenza vaccination and the severity and outcome of COVID-19 disease in Iranian patients living in the North.

Methods:

This retrospective case-control study was performed on186 patients with COVID-19 infection between March and April, 2020. Patients with positive PCR were divided into two groups of case and control; Patients with moderate to severe and normal to mild lung involvement, respectively. The lung opacities in all of the 5 lobes were evaluated on chest CT images using a CT severity scoring system. The history of influenza vaccination during the fall of 2019-2020 was determined by a phone call. Statistical analysis was done using the chi-square test, student’s t-test, and logistic regression. The significance level was p<0.05.

Results:

The mean age of patients was 54.67±15.05years. Most patients had pulmonary manifestations including ground-glass opacity (57%), consolidation (80%) and pleural effusion (3.2%). Adjusting for age, gender, and history of underlying disease, vaccination is an effective factor in the severity of pulmonary involvement (AOR=0.39; 95%CI: (0.21, 0.73); P=0.003). Furthermore, the chance of ICU admission decreased via influenza vaccination (OR=0.21, P=0.001).

Conclusion:

The results showed that the severity of COVID-19 pulmonary involvement and outcome as ICU admission, and severe symptoms in patients with history of influenza vaccination were significantly lower than those without history of vaccination. This strategy can be used to prevent and reduce the complications of COVID-19.

Data-driven case fatality rate estimation for the primary lineage of SARS-CoV-2 in Poland

After more than one and a half year since the COVID-19 pandemics outbreak the scientific world is constantly trying to understand its dynamics. In this paper of the case fatality rates (CFR) for COVID-19 we study the historic data regarding mortality in Poland during the first six months of pandemic, when no SARS-CoV-2 variants of concern were present among infected. To this end, we apply competing risk models to perform both uni- and multivariate analyses on specific subpopulations selected by different factors including the key indicators: age, sex, hospitalization. The study explores the case fatality rate to find out its decreasing trend in time. Furthermore, we describe the differences in mortality among hospitalized and other cases indicating a sudden increase of mortality among hospitalized cases at the end of the 2020 spring season. Exploratory and multivariate analysis revealed the real impact of each variable and besides the expected factors indicating increased mortality (age, comorbidities) we track more non-obvious indicators. Recent medical care as well as the identification of the source contact, independently of the comorbidities, significantly impact an individual mortality risk. As a result, the study provides a twofold insight into the COVID-19 mortality in Poland. On one hand we explore mortality in different groups with respect to different variables, on the other we indicate novel factors that may be crucial in reducing mortality. The later can be coped, e.g. by more efficient contact tracing and proper organization and management of the health care system to accompany those who need medical care independently of comorbidities or COVID-19 infection.

Clinical characteristics and mortality predictors of patients with cancer hospitalized by COVID-19 in a pediatric third-level referral center

BACKGROUND

More than 135 million COVID-19 cases (coronavirus disease 2019) have been reported worldwide until today, with over 2.9 million deaths. Several studies have demonstrated that disease severity is lower in the pediatric population than in adults; however, differences are described in patients with chronic diseases, including oncological patients. Current world literature suggests patients with comorbidities, including cancer, have an increased risk of unfortunate outcomes. Therefore, our objective was to describe the clinical characteristics and epidemiological factors associated with mortality in a cohort of pediatric cancer patients hospitalized for COVID-19.

METHODS

This is a retrospective, descriptive study of the cases of patients with cancer hospitalized for COVID-19. A total of 40 pediatrics were included in the analysis. Data from pediatric patients with COVID-19 included clinical and epidemiological records, laboratory, imaging studies, COVID-19 diagnostic methods, and medical treatment.

RESULTS

Of the 40 pediatric patients admitted with cancer with a confirmed diagnosis of COVID-19, 42.5% were solid tumors, 40% leukemias, and 17.5% lymphomas. The clinical parameters associated with mortality were stage IV tumor (p = 0.029) and intubation (p < 0.001). The biochemical factors associated with lower survival were thrombocytopenia under 25,000 cells/mm³ (p < 0.001), D-dimer over 1 μg/ml (p = 0.003), clinical malnutrition (p = 0.023), and disseminated intravascular coagulation (p = 0.03).

CONCLUSION

Our findings showed that the fever was the most frequent symptom, and the clinical parameters associated with mortality were stage IV tumor, intubation, saturation percentage, RDW, platelets, creatinine, ALT, D-dimer, ferritin, and FiO2 percentage. The thrombocytopenia, D-dimer, nutritional status, and disseminated intravascular coagulation were significantly associated with lower survival.

Procalcitonin and High APACHE (Acute Physiological and Chronic Health Evaluation) Level Are Associated with the Course of Acute Kidney Injury in Patients with SARS-CoV-2

BACKGROUND

Acute kidney injury (AKI) is associated with poor outcomes in patients infected with SARS-CoV-2. Sepsis, direct injury to kidney cells by the virus, and severe systemic inflammation are mechanisms implicated in its development. We investigated the association between inflammatory markers (C-reactive protein, procalcitonin, D-dimer, lactate dehydrogenase, and ferritin) in patients infected with SARS-CoV-2 and the development of AKI.

METHODS

A prospective cohort study performed at the Civil Hospital (Dr. Juan I. Menchaca) Guadalajara, Mexico, included patients aged >18 years with a diagnosis of SARS-CoV-2 pneumonia confirmed by RT-PCR and who did or did not present with AKI (KDIGO) while hospitalized. Biomarkers of inflammation were recorded, and kidney function was estimated using the CKD-EPI formula.

RESULTS

291 patients were included (68% males; average age, 57 years). The incidence of AKI was 40.5% (118 patients); 21% developed stage 1 AKI, 6% developed stage 2 AKI, and 14% developed stage 3 AKI. The development of AKI was associated with higher phosphate (p = 0.002) (RR 1.39, CI 95% 1.13-1.72), high procalcitonin levels at hospital admission (p = 0.005) (RR 2.09, CI 95% 1.26-3.50), and high APACHE scores (p = 0.011) (RR 2.0, CI 95% 1.17-3.40). The survival analysis free of AKI according to procalcitonin levels and APACHE scores demonstrated a lower survival in patients with procalcitonin >0.5 ng/ml (p = 0.001) and APACHE >15 points (p = 0.004).

CONCLUSIONS

Phosphate, high procalcitonin levels, and APACHE levels >15 were predictors of AKI development in patients hospitalized with COVID-19.

Perspective of HLA-G Induced Immunosuppression in SARS-CoV-2 Infection

COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has threatened public health worldwide. Host antiviral immune responses are essential for viral clearance and disease control, however, remarkably decreased immune cell numbers and exhaustion of host cellular immune responses are commonly observed in patients with COVID-19. This is of concern as it is closely associated with disease severity and poor outcomes. Human leukocyte antigen-G (HLA-G) is a ligand for multiple immune inhibitory receptors, whose expression can be upregulated by viral infections. HLA-G/receptor signalling, such as engagement with immunoglobulin-like transcript 2 (ILT-2) or ILT-4, not only inhibit T and natural killer (NK) cell immune responses, dendritic cell (DC) maturation, and B cell antibody production. It also induces regulatory cells such as myeloid-derived suppressive cells (MDSCs), or M2 type macrophages. Moreover, HLA-G interaction with CD8 and killer inhibitory receptor (KIR) 2DL4 can provoke T cell apoptosis and NK cell senescence. In this context, HLA-G can induce profound immune suppression, which favours the escape of SARS-CoV-2 from immune attack. Although detailed knowledge on the clinical relevance of HLA-G in SARS-CoV-2 infection is limited, we herein review the immunopathological aspects of HLA-G/receptor signalling in SARS-CoV-2 infection, which could provide a better understanding of COVID-19 disease progression and identify potential immunointerventions to counteract SARS-CoV-2 infection.

No association of Gaucher Disease with COVID-19-related outcomes: a nationwide cohort study

BACKGROUND

It is well documented that patients with chronic metabolic diseases such as diabetes and obesity are adversely affected by the Covid 19 pandemic. However, when the subject is rare metabolic diseases, there is not enough data in the literature.

AIM

To investigate course of COVID-19 among patients with Gaucher disease (GD), the most common lysosomal storage disease.

METHODS

Based on the National Health System data, a retrospective cohort of patients with confirmed (PCR positive) COVID-19 infection (n = 149 618) was investigated. The adverse outcomes between patients with GD (n = 39) and those without GD (n = 149 579) were compared in crude and propensity score matched (PSM) groups. The outcomes were hospitalization, the composite of intensive care unit (ICU) admission and/or mechanical ventilation and mortality.

RESULTS

The patients with GD were significantly older and had a higher frequency of hypertension, T2DM, dyslipidemia, asthma or COPD, chronic kidney disease, coronary artery disease, heart failure, and cancer. Although hospitalization rates in Gaucher patients were found to be higher in crude analyzes, the PSM models (model 1, age- and gender-matched; model 2, matched for age, gender, hypertension, T2DM, and cancer) revealed no difference for the outcomes between patients with GD and the general population. According to multivariate regression analyses, having a diagnosis of GD was not a significant predictor for hospitalization (p = 0.241), ICU admission/mechanical ventilation (p = 0.403) or mortality (p = 0.231).

CONCLUSION

According to our national data, SARS-CoV-2 infection in patients with GD does not have a more severe course than the normal population. This article is protected by copyright. All rights reserved.

Mortality predictors of hospitalized patients with COVID-19: Retrospective cohort study from Nur-Sultan, Kazakhstan

BACKGROUND

First reported case of Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) in Kazakhstan was identified in March 2020. Many specialized tertiary hospitals in Kazakhstan including National Research Cardiac Surgery Center (NRCSC) were re-organized to accept coronavirus disease 2019 (COVID-19) infected patients during summer months of 2020. Although many studies from worldwide reported their experience in treating patients with COVID-19, there are limited data available from the Central Asia countries. The aim of this study is to identify predictors of mortality associated with COVID-19 in NRCSC tertiary hospital in Nur-Sultan, Kazakhstan.

METHODS

This is a retrospective cohort study of patients admitted to the NRCSC between June 1st-August 31st 2020 with COVID-19. Demographic, clinical and laboratory data were collected from electronic records. In-hospital mortality was assessed as an outcome. Patients were followed-up until in-hospital death or discharge from the hospital. Descriptive statistics and factors associated with mortality were assessed using univariate and multivariate logistic regression models.

RESULTS

Two hundred thirty-nine admissions were recorded during the follow-up period. Mean age was 57 years and 61% were males. Median duration of stay at the hospital was 8 days and 34 (14%) patients died during the hospitalization. Non-survivors were more likely to be admitted later from the disease onset, with higher fever, lower oxygen saturation and increased respiratory rate compared to survivors. Leukocytosis, lymphopenia, anemia, elevated liver and kidney function tests, hypoproteinemia, elevated inflammatory markers (C-reactive protein (CRP), ferritin, and lactate dehydrogenase (LDH)) and coagulation tests (fibrinogen, D-dimer, international normalized ratio (INR), and activated partial thromboplastin time (aPTT)) at admission were associated with mortality. Age (OR 1.2, CI:1.01-1.43), respiratory rate (OR 1.38, CI: 1.07-1.77), and CRP (OR 1.39, CI: 1.04-1.87) were determined to be independent predictors of mortality.

CONCLUSION

This study describes 14% mortality rate from COVID-19 in the tertiary hospital. Many abnormal clinical and laboratory variables at admission were associated with poor outcome. Age, respiratory rate and CRP were found to be independent predictors of mortality. Our finding would help healthcare providers to predict the risk factors associated with high risk of mortality. Further investigations involving large cohorts should be provided to support our findings.

Predictors of mortality in COVID-19 patients at Kinshasa Medical Center and a survival analysis: a retrospective cohort study

Background

Despite it being a global pandemic, there is little research examining the clinical features of severe COVID-19 in sub-Saharan Africa. This study aims to identify predictors of mortality in COVID-19 patients at Kinshasa Medical Center (KMC).

Methods

In this retrospective, observational, cohort study carried out at the Kinshasa Medical Center (KMC) between March 10, 2020 and July 10, 2020, we included all adult inpatients (≥ 18 years old) with a positive COVID-19 PCR result. The end point of the study was survival. The study population was dichotomized into survivors and non-survivors group. Kaplan–Meier plot was used for survival analyses. The Log-Rank test was employed to compare the survival curves. Predictors of mortality were identified by Cox regression models. The significance level of p value was set at 0.05.

Results

432 patients with confirmed COVID-19 were identified and only 106 (24.5%) patients with moderate, severe or critical illness (mean age 55.6 ± 13.2 years old, 80.2% were male) were included in this study, of whom 34 (32%) died during their hospitalisation. The main complications of the patients included ARDS in 59/66 (89.4%) patients, coagulopathy in 35/93 (37.6%) patients, acute cardiac injury in 24/98 (24.5%) patients, AKI in 15/74 (20.3%) patients and secondary infection in 12/81 (14.8%) patients. The independent predictors of mortality were found to be age [aHR 1.38; 95% CI 1.10–1.82], AKI stage 3 [aHR 2.51; 95% CI 1.33–6.80], proteinuria [aHR 2.60; 95% CI 1.40–6.42], respiratory rate [aHR 1.42; 95% CI 1.09–1.92] and procalcitonin [aHR 1.08; 95% CI 1.03–1.14]. The median survival time of the entire group was 12 days. The cumulative survival rate of COVID-19 patients was 86.9%, 65.0% and 19.9% respectively at 5, 10 and 20 days. Levels of creatinine (p = 0.012), were clearly elevated in non-survivors compared with survivors throughout the clinical course and increased deterioration.

Conclusion

Mortality rate of COVID-19 patients is high, particularly in intubated patients and is associated with age, respiratory rate, proteinuria, procalcitonin and acute kidney injury.

Modelling RT-qPCR cycle-threshold using digital PCR data for implementing SARS-CoV-2 viral load studies

OBJECTIVES

To exploit the features of digital PCR for implementing SARS-CoV-2 observational studies by reliably including the viral load factor expressed as copies/μL.

METHODS

A small cohort of 51 Covid-19 positive samples was assessed by both RT-qPCR and digital PCR assays. A linear regression model was built using a training subset, and its accuracy was assessed in the remaining evaluation subset. The model was then used to convert the stored cycle threshold values of a large dataset of 6208 diagnostic samples into copies/μL of SARS-CoV-2. The calculated viral load was used for a single cohort retrospective study. Finally, the cohort was randomly divided into a training set (n = 3095) and an evaluation set (n = 3113) to establish a logistic regression model for predicting case-fatality and to assess its accuracy.

RESULTS

The model for converting the Ct values into copies/μL was suitably accurate. The calculated viral load over time in the cohort of Covid-19 positive samples showed very low viral loads during the summer inter-epidemic waves in Italy. The calculated viral load along with gender and age allowed building a predictive model of case-fatality probability which showed high specificity (99.0%) and low sensitivity (21.7%) at the optimal threshold which varied by modifying the threshold (i.e. 75% sensitivity and 83.7% specificity). Alternative models including categorised cVL or raw cycle thresholds obtained by the same diagnostic method also gave the same performance.

CONCLUSION

The modelling of the cycle threshold values using digital PCR had the potential of fostering studies addressing issues regarding Sars-CoV-2; furthermore, it may allow setting up predictive tools capable of early identifying those patients at high risk of case-fatality already at diagnosis, irrespective of the diagnostic RT-qPCR platform in use. Depending upon the epidemiological situation, public health authority policies/aims, the resources available and the thresholds used, adequate sensitivity could be achieved with acceptable low specificity.

The effect of casirivimab with imdevimab on disease progression in nonsevere COVID‐19 patients in a single hospital in Japan

Background

Recent randomized trials have revealed that neutralizing monoclonal antibodies can reduce disease progression in mild–moderate COVID‐19 patients. However, no studies have investigated the effect of neutralizing monoclonal antibodies on clinical outcomes in Japan.

Methods

A single‐center retrospective and prospective cohort study was conducted. All consecutive febrile nonsevere COVID‐19 patients with at least one risk factor were included. The primary outcome was progression to severe COVID‐19. Severe COVID‐19 cases were defined as patients requiring oxygen therapy and dexamethasone. The differences in the primary outcomes between the patients who were treated with casirivimab with imdevimab (treatment group) and those who were not (control group) were compared using the chi‐squared test.

Results

A total of 128 patients were included. Of those, the mean age was 53.6 years old (SD 9.9), and 52 (40.6%) were women. Fifty‐three patients were treated with casirivimab with imdevimab, and 75 patients were given the standard treatment only. The primary outcome occurred in eight (15.1%) of the 53 patients in the treatment group and 33 (44.0%) of the 75 patients in the control group (odd ratio [OR] 0.23, 95% CI 0.09 to 0.55). The multivariate analysis revealed that the use of casirivimab with imdevimab (OR 0.21, 95% CI 0.08 to 0.54) was the only independent risk factor associated with progression to severe COVID‐19. No patients died during hospitalization in either group.

Conclusion

Similar to other countries, casirivimab with imdevimab significantly reduced disease progression in early nonsevere COVID‐19 patients with fever and risk factors in Japan.

A close-up view of dynamic biomarkers in the setting of COVID-19: Striking focus on cardiovascular system

Based on the recent reports, cardiovascular events encompass a large portion of the mortality caused by the COVID‐19 pandemic, which drawn cardiologists into the management of the admitted ill patients. Given that common laboratory values may provide key insights into the illness caused by the life‐threatening SARS‐CoV‐2 virus, it would be more helpful for screening, clinical management and on‐time therapeutic strategies. Commensurate with these issues, this review article aimed to discuss the dynamic changes of the common laboratory parameters during COVID‐19 and their association with cardiovascular diseases. Besides, the values that changed in the early stage of the disease were considered and monitored during the recovery process. The time required for returning biomarkers to basal levels was also discussed. Finally, of particular interest, we tended to abridge the latest updates regarding the cardiovascular biomarkers as prognostic and diagnostic criteria to determine the severity of COVID‐19.