Fatality rate and predictors of mortality in an Italian cohort of hospitalized COVID-19 patients

A Complex Interplay: Navigating the Crossroads of Tobacco Use, Cardiovascular Disease, and the COVID-19 Pandemic: A WHF Policy Brief

The Coronavirus Disease 2019, commonly referred to as COVID-19, is responsible for one of the deadliest pandemics in human history. The direct, indirect and lasting repercussions of the COVID-19 pandemic on individuals and public health, as well as health systems can still be observed, even today. In the midst of the initial chaos, the role of tobacco as a prognostic factor for unfavourable COVID-19 outcomes was largely neglected. As of 2023, numerous studies have confirmed that use of tobacco, a leading risk factor for cardiovascular and other diseases, is strongly associated with increased risks of severe COVID-19 complications (e.g., hospitalisation, ICU admission, need for mechanical ventilation, long COVID, etc.) and deaths from COVID-19. In addition, evidence suggests that COVID-19 directly affects multiple organs beyond the respiratory system, disproportionately impacting individuals with comorbidities. Notably, people living with cardiovascular disease are more prone to experiencing worse outcomes, as COVID-19 often inherently manifests as thrombotic cardiovascular complications. As such, the triad of tobacco, COVID-19 and cardiovascular disease constitutes a dangerous cocktail. The lockdowns and social distancing measures imposed by governments have also had adverse effects on our lifestyles (e.g., shifts in diets, physical activity, tobacco consumption patterns, etc.) and mental well-being, all of which affect cardiovascular health. In particular, vulnerable populations are especially susceptible to tobacco use, cardiovascular disease and the psychological fallout from the pandemic. Therefore, national pandemic responses need to consider health equity as well as the social determinants of health. The pandemic has also had catastrophic impacts on many health systems, bringing some to the brink of collapse. As a result, many health services, such as services for cardiovascular disease or tobacco cessation, were severely disrupted due to fears of transmission and redirection of resources for COVID-19 care. Unfortunately, the return to pre-pandemic levels of cardiovascular disease care activity has stagnated. Nevertheless, digital solutions, such as telemedicine and apps, have flourished, and may help reduce the gaps. Advancing tobacco control was especially challenging due to interference from the tobacco industry. The industry exploited lingering uncertainties to propagate misleading information on tobacco and COVID-19 in order to promote its products. Regrettably, the links between tobacco use and risk of SARS-CoV-2 infection remain inconclusive. However, a robust body of evidence has, since then, demonstrated that tobacco use is associated with more severe COVID-19 illness and complications. Additionally, the tobacco industry also repeatedly attempted to forge partnerships with governments under the guise of corporate social responsibility. The implementation of the WHO Framework Convention on Tobacco Control could address many of the aforementioned challenges and alleviate the burden of tobacco, COVID-19, and cardiovascular disease. In particular, the implementation of Article 5.3 could protect public health policies from the vested interests of the industry. The world can learn from the COVID-19 pandemic to better prepare for future health emergencies of international concern. In light of the impact of tobacco on the COVID-19 pandemic, it is imperative that tobacco control remains a central component in pandemic preparedness and response plans.

Evaluation of the relationship of treatment and vaccination with prognosis in patients with a diagnosis of COVID-19

PURPOSE

Coronavirus disease 2019 (COVID-19) has affected millions of people worldwide and caused mortality. Many factors have been reported to affect the prognosis of COVID-19. In this study, we aimed to investigate the effects of drug therapy and vaccination on prognosis in patients hospitalized with a COVID-19 diagnosis.

METHODS

In this single-center, cross-sectional study, data were retrospectively collected from patients receiving inpatient treatment at a university hospital with a diagnosis of COVID-19 between January 1, 2020, and April 30, 2022. The patients' demographic and clinical characteristics were recorded. The Chi-square, Cox and logistic regression was performed, P < 0.05 was considered statistically significant.

RESULTS

Total 1723 patients (50.1% were men, mean age: 60.6 ± 16.90) who had not been vaccinated rate was 27.0% (> 3 doses: 45.7%). Mortality rate was 17.0%. Increasing age, male, a high Charlson Comorbidity Index (CCI), and no vaccination significantly increased mortality (P < 0.05). The mortality rate was significantly lower in the chloroquine treatment group than in the other treatment groups. Increasing age, male, and a high CCI were determined to be factors that significantly increased the length of hospital stay (LOHS). LOHS found to be significantly lower in the favipiravir or chloroquine groups compared to the remaining treatment groups (P < 0.001). Both mortality and the LOHS significantly differed according to AST, d-dimer, ferritin, and GFR.

CONCLUSION

This study primarily investigated the effect of treatment and vaccination on the prognosis of COVID-19. This was determined to be prepared for another potential pandemic that may arise due to COVID-19.

Laboratory biomarkers associated with COVID-19 mortality among inpatients in a Peruvian referral hospital

Aim

To evaluate the biochemical and hematological markers associated with the risk of death due to COVID-19 in a clinical cohort with a severe clinical profile.

Methods

A retrospective study was conducted among 215 anonymized inpatient records from the Hospital Nacional Almanzor Aguinaga Asenjo, Peru, between April and June 2020. The association between biomarkers and death due to COVID-19 was assessed using Cox regression, with a multivariable modeling of 1) biochemical and 2) hematological markers. Kaplan-Meier analyses and time-dependent receiver operating characteristic curves were calculated for each associated biomarker (p < 0.05).

Results

Data analysis of 215 inpatient records revealed an overall mortality rate of 51.30% (95% CI 44.70-58.50), a mean age of 63.90 ± 14.10 years, and a median oxygen saturation of 88% (interquartile range 82-92%). The best-fitted biochemical model included higher levels of C-reactive protein (CRP), D-dimer, fibrinogen, urea, and lactate dehydrogenase. Similarly, the best-fitted hematological model included higher absolute neutrophil and prothrombin time, and lower absolute platelet counts. The best area under the curve values in both models were found to be CRP and D-dimer values (>0.74) and the absolute neutrophil count (0.63).

Conclusions

Some specific biochemical markers outperformed hematological markers. Evaluated hematological counts analyzed in multivariable models proved to be better markers and could be useful to discriminate COVID-19 patients at high risk of death.

Patterns of case fatality and hospitalization duration among nearly 1 million hospitalized COVID-19 patients covered by Iran Health Insurance Organization (IHIO) over two years of pandemic: An analysis of associated factors

BACKGROUND

Different populations and areas of the world experienced diverse COVID-19 hospitalization and mortality rates. Claims data is a systematically recorded source of hospitalized patients' information that could be used to evaluate the disease management course and outcomes. We aimed to investigate the hospitalization and mortality patterns and associated factors in a huge sample of hospitalized patients.

METHODS

In this retrospective registry-based study, we utilized claim data from the Iran Health Insurance Organization (IHIO) consisting of approximately one million hospitalized patients across various hospitals in Iran over a 26-month period. All records in the hospitalization dataset with ICD-10 codes U07.1/U07.2 for clinically/laboratory confirmed COVID-19 were included. In this study, a case referred to one instance of a patient being hospitalized. If a patient experienced multiple hospitalizations within 30 days, those were aggregated into a single case. However, if hospitalizations had longer intervals, they were considered independent cases. The primary outcomes of study were general and intensive care unit (ICU) hospitalization periods and case fatality rate (CFR) at the hospital. Besides, various demographic and hospitalization-associated factors were analyzed to derive the associations with study outcomes using accelerated failure time (AFT) and logistic regression models.

RESULTS

A total number of 1 113 678 admissions with COVID-19 diagnosis were recorded by IHIO during the study period, defined as 917 198 cases, including 51.9% females and 48.1% males. The 61-70 age group had the highest number of cases for both sexes. Among defined cases, CFR was 10.36% (95% CI: 10.29-10.42). The >80 age group had the highest CFR (26.01% [95% CI: 25.75-26.27]). The median of overall hospitalization and ICU days were 4 (IQR: 3-7) and 5 (IQR: 2-8), respectively. Male patients had a significantly higher risk for mortality both generally (odds ratio (OR) = 1.36 [1.34-1.37]) and among ICU admitted patients (1.12 [1.09-1.12]). Among various insurance funds, Foreign Citizens had the highest risk of death both generally (adjusted OR = 2.06 [1.91-2.22]) and in ICU (aOR = 1.71 [1.51-1.92]). Increasing age groups was a risk of longer hospitalization, and the >80 age group had the highest risk for overall hospitalization period (median ratio = 1.52 [1.51-1.54]) and at ICU (median ratio = 1.17 [1.16-1.18]). Considering Tehran as the reference province, Sistan and Balcuchestan (aOR = 1.4 [1.32-1.48]), Alborz (aOR = 1.28 [1.22-1.35]), and Khorasan Razavi (aOR = 1.24 [1.20-1.28]) were the provinces with the highest risk of mortality in hospitalized patients.

CONCLUSION

Hospitalization data unveiled mortality and duration associations with variables, highlighting provincial outcome disparities in Iran. Using enhanced registry systems in conjunction with other studies, empowers policymakers with evidence for optimizing resource allocation and fortifying healthcare system resilience against future health challenges.

A Retrospective Study on the Outcome of Coronavirus Disease 2019 (COVID-19) Patients Admitted to a District General Hospital and Predictors of High Mortality

BACKGROUND

The clinical features and severity of coronavirus disease 2019 (COVID-19) vary between patients and countries. Patients with certain conditions are predisposed to poor outcomes compared with those without medical conditions, such as diabetes, dementia, and hypertension (HTN).

METHODS

The aim of this retrospective study was to assess factors associated with higher mortality in patients with COVID-19 infections and to identify the reason for hospital admission in these patients. The study was performed on patients admitted between 1 and 31 March 2020. Data collection was done retrospectively from electronic medical records.

RESULTS

There were 269 patient admissions during this period, of which 147 were included in this audit. The mean age of COVID-19-positive patients was 62.8 years and 65.9 years for COVID-19-negative patients during this period. Forty-seven patients requiring hospital admission were COVID-19 positive and 93 were COVID-19 negative. There were no COVID-19 swabs in the seven patients included in the audit. Approximately 50% of the COVID-19-positive patients presented with fever and shortness of breath (sob), followed by dyspnea and cough (seven patients). The most common comorbidity was HTN, followed by type 2 diabetes mellitus (T2DM) and ischemic heart disease (IHD). The survival rate was 72.3% in COVID-19-positive patients and 80% in COVID-19-negative patients. The average length of stay was 14.4 days for COVID-19-positive survivors compared to 7.8 days for COVID-19-negative survivors. Most patients who tested positive for COVID-19 infection received oseltamivir vaccination and antibiotics. The presence of HTN, diabetes mellitus (DM), age, and organ failure was associated with a high mortality risk.

CONCLUSION

Our study supports the findings of previous studies that diabetes, HTN, coronary artery disease, old age, and organ failure were associated with high mortality in patients admitted to hospitals with COVID-19 infections.

Mortality rate and predictors of COVID-19 inpatients in Ethiopia: a systematic review and meta-analysis

Introduction

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an extremely rare virus that devastates the economy and claims human lives. Despite countries' urgent and tenacious public health responses to the COVID-19 pandemic, the disease is killing a large number of people. The results of prior studies have not been used by policymakers and programmers due to the presence of conflicting results. As a result, this study was conducted to fill the knowledge gap and develop a research agenda.

Objective

This study aimed to assess the mortality rate and predictors of COVID-19 hospitalized patients in Ethiopia.

Methods

Electronic databases were searched to find articles that were conducted using a retrospective cohort study design and published in English up to 2022. The data were extracted using a Microsoft Excel spreadsheet and exported to StataTM version 17.0 for further analysis. The presence of heterogeneity was assessed and presented using a forest plot. The subgroup analysis, meta-regression, and publication bias were computed to identify the source of heterogeneity. The pool COVID-19 mortality rate and its predictors were calculated and identified using the random effects meta-analysis model, respectively. The significant predictors identified were reported using a relative risk ratio and 95% confidence interval (CI).

Results

Seven studies with 31,498 participants were included. The pooled mortality rate of COVID-19 was 9.13 (95% CI: 5.38, 12.88) per 1,000 person-days of mortality-free observation. Those study participants who had chronic kidney disease had 2.29 (95% CI: 1.14, 4.60) times higher chance of experiencing mortality than their corresponding counterparts, diabetics had 2.14 (95% CI: 1.22, 3.76), HIV patients had 2.98 (95% CI: 1.26, 7.03), hypertensive patients had 1.63 (95% CI: 1.43, 1.85), and smoker had 2.35 (95% CI: 1.48, 3.73).

Conclusion

COVID-19 mortality rate was high to tackle the epidemic of the disease in Ethiopia. COVID-19 patients with chronic renal disease, diabetes, hypertension, smoking, and HIV were the significant predictors of mortality among COVID-19 patients in Ethiopia. COVID-19 patients with chronic diseases and comorbidities need special attention, close follow-up, and care from all stakeholders.

Polymerase chain reaction-positivity and predictors for SARS-CoV-2 infection among diagnosed cases' in North West Ethiopia

Background

The lack of sophisticated diagnosing equipment for polymerase chain reaction (PCR) during the incidence of variant types of COVID-19 underestimates the morbidity and mortality patterns of this pandemic. Thus, this study aimed to estimate seropositive and confirmatory predictors for COVID-19 suspected and tested cases through polymerase chain reaction (RT-PCR) in two diagnosing.

Methods

A facility-based descriptive cross-sectional study was employed among COVID-19 suspected cases from January 2, 2022, to June 9, 2022. The data were collected both using a structured interviewees and nasopharyngeal (NP) swabs. The nasal swab (NS) was analyzed in the laboratory for RNA detection of the virus using PCR. The collected data were entered into Epi Data version 4.2 and then exported to STATA (SE) version R-14 software for further analysis. multivariable logistic regression was used to assess the associated risk.

Results

A total of 285 suspected cases have participated in this study. The overall mean (±SD) age of the participants was 37.5 (±18.5) years. The majority, 174 (61.1%) of the tested groups were symptomatic when diagnosed. The positivity of RT-PCR for suspected and COVID-19 diagnosed cases were confirmed in 62/285 (21.75%). In multivariable analysis, they were aged 26-50 years (adjusted odds ratio [AOR] = 4.2, 95% confidence interval [CI] = 1.5-10.75), had comorbidity (AOR = 5.8; 95% CI = 2.1-12.2), and cigarette smokers (AOR = 13.5; 95% CI = 5.3-36.6) were significantly associated with developing COVID-19 infection.

Conclusion

More than two in every nine suspected cases were positive RT-PCR tests, and the infectivity of COVID-19 was significantly associated with age 25-50 years, comorbidities, and cigarette smoking. The deployment of high-quality diagnostic kits like RT-PCR is crucial for the early detection and risk stratification of suspected cases.

Biochemical Analysis of Ferritin and D-dimer in COVID-19 Survivors and Non-survivors

Background COVID-19 is a major cause of illness and mortality. The management of COVID-19-related illnesses might change if variables connected to their severity and the requirement for ICU admission could be found. The severity of COVID-19 might be efficiently predicted with several laboratory measures, such as ferritin levels and D-dimer analysis. Objectives This study aimed to evaluate the association between serum D-dimer and ferritin levels and their effects on mortality in patients with COVID-19. Methods This retrospective observational study included all patients with positive real-time polymerase chain reaction (RT-PCR) results for COVID-19 who were hospitalized in the Ministry of Health South Al-Qunfudah General Hospital between March and September 30, 2020. Their laboratory parameters, serum D-dimer, and ferritin levels were evaluated. IBM SPSS Statistics for Windows, Version 26.0 (released 2019; IBM Corp., Armonk, New York, United States) was used to analyze the data. Results A total of 318 COVID-19 patients were analyzed; 56.9% (n=181) were male and 43.1% (n=137) were female. Of these, 78.6% (n=250) survived, including 58% of men and 42% of women. The mean D-dimer was 2.1 mcg/mL (SD=3.16) and the mean ferritin was 698.59 ng/mL (SD=603.11). Non-recovered patients were substantially older (66.16 years old) and had higher D-dimer (5.46) mcg/mL and ferritin levels (992.96) ng/mL. Intubation length and gender did not affect survival. Of the non-survivors, 95.6% (n=239) were admitted to the ICU, and 50% (n=34) required mechanical ventilation. Conclusions COVID-19 infection mortality dramatically increased with older age and increased mean ferritin and plasma D-dimer values, which were significantly higher in COVID-19 non-survivors than in survivors. Therefore, assessing and monitoring these laboratory markers in the early stages of the disease may have a significant impact on preventing disease progression and death.

Analysis of Predictors and Outcomes of COVID-19 Patients Requiring ICU Admission from COVID-19 Registry, India

Background

Patients admitted to intensive care units (ICUs) with severe coronavirus disease (COVID-19) are associated with high mortality. The present retrospective, multicenter study describes the predictors and outcomes of COVID-19 patients requiring ICU admission from COVID-19 Registry of Indian Council of Medical Research (ICMR), India.

Materials and methods

Prospectively collected data from participating institutions were entered into the electronic National Clinical Registry of COVID-19. We enrolled patients aged >18 years with COVID-19 pneumonia requiring ICU admission between March 2020 and August 2021. Exclusion criteria were negative in RT-PCR report, death within 24 hours of ICU admission, or incomplete data. Their demographic and laboratory variables, ICU severity indices, treatment strategies, and outcomes were analyzed.

Results

A total of 5,865 patients were enrolled. Overall mortality was 43.2%. Non-survivors were older (58.2 ± 15.4 vs 53.6 ± 14.7 years; p = 0.001), had multiple comorbidities (33.2% vs 29.5%, p = 0.001), had higher median D-dimer (1.56 vs 1.37, p = 0.015), higher CT severity index (16.8 ± 5.2 vs 13.5 ± 5.47, p = 0.001) and longer median hospital stay (10 vs 8 days, p = 0.001) and ICU stay (5 vs 4 days, p = 0.001), compared with survivors.On multivariate analysis, high CRP (HR 1.008, 95% CI: 1.006-1.010, p = 0.001) and high D-dimer (HR 1.089, 95% CI: 1.065-1.113, p < 0.001) were associated with invasive mechanical ventilation while older age (HR 1.19, CI: 1.001-1.038, p = 0.039) and high D-dimer (HR-1.121, CI: 1.072-1.172, p = 0.001) were independently associated with mortality and while the use of prophylactic low molecular weight heparin (LMWH) (HR 0.647, CI: 0.527-0.794, p = 0.001) lowered mortality.

Conclusion

Among 5,865 COVID-19 patients admitted to ICU, mortality was 43.5%. High CRP and D-dimers were independently associated with the need for invasive mechanical ventilation while older age and high D-dimer were associated with higher mortality. The use of prophylactic LMWH independently reduced mortality.

How to cite this article

Kajal K, Singla K, Puri GD, Bhalla A, Mukherjee A, Kumar G, et al. Analysis of Predictors and Outcomes of COVID-19 Patients Requiring ICU Admission from COVID-19 Registry, India. Indian J Crit Care Med 2023;27(8):552-562.

Machine learning-based mortality prediction models for smoker COVID-19 patients

BACKGROUND

The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population.

METHODS

A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of smoking. Multiple machine learning models were developed using 10-fold cross-validation. The target variable was in-hospital mortality and input features included patient demographics, levels of care, vital signs, medications, and comorbidities. Two sets of models were developed for at-admission and post-admission predictions. Subsequently, the top five prediction models were selected from at-admission models and post-admission models and their probabilities were calibrated.

RESULTS

The in-hospital mortality rate for smoker COVID-19 patients was 20.1%. For "at admission" models, the best-calibrated model was XGBoost which yielded an accuracy of 87.5% and F₁ score of 86.2%. For the "post-admission" models, XGBoost also outperformed the rest with an accuracy of 90.5% and F₁ score of 89.9%. Active smoking was among the most important features in patients' mortality prediction.

CONCLUSION

Our machine learning-based mortality prediction models have the potential to be adapted for improving the management of smoker COVID-19 patients and predicting patients' chance of survival.

Baseline Plasma Osteopontin Protein Elevation Predicts Adverse Outcomes in Hospitalized COVID-19 Patients

More than three years have passed since the first case, and COVID-19 is still a health concern, with several open issues such as the lack of reliable predictors of a patient’s outcome. Osteopontin (OPN) is involved in inflammatory response to infection and in thrombosis driven by chronic inflammation, thus being a potential biomarker for COVID-19. The aim of the study was to evaluate OPN for predicting negative (death or need of ICU admission) or positive (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. We enrolled 133 hospitalized, moderate-to-severe COVID-19 patients in a prospective observational study between January and May 2021. Circulating OPN levels were measured by ELISA at admission and at day 7. The results showed a significant correlation between higher plasma concentrations of OPN at hospital admission and a worsening clinical condition. At multivariate analysis, after correction for demographic (age and gender) and variables of disease severity (NEWS2 and PiO2/FiO2), OPN measured at baseline predicted an adverse prognosis with an odds ratio of 1.01 (C.I. 1.0–1.01). At ROC curve analysis, baseline OPN levels higher than 437 ng/mL predicted a severe disease evolution with 53% sensitivity and 83% specificity (area under the curve 0.649, p = 0.011, likelihood ratio of 1.76, (95% confidence interval (CI): 1.35–2.28)). Our data show that OPN levels determined at the admission to hospital wards might represent a promising biomarker for early stratification of patients’ COVID-19 severity. Taken together, these results highlight the involvement of OPN in COVID-19 evolution, especially in dysregulated immune response conditions, and the possible use of OPN measurements as a prognostic tool in COVID-19.

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital

PURPOSE

Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction.

METHODS

COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models.

RESULTS

Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873).

CONCLUSION

Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

[Influence of arterial hypertension on the clinical profile and prognosis of patients hospitalized for COVID-19 in the city of Bukavu, in the Democratic Republic of Congo : Prospective cohort study]

PROBLEM

There are no studies on the association between high blood pressure and COVID-19 in South Kivu.

OBJECTIVE

to determine the influence of arterial hypertension on the clinical characteristics and prognosis of COVID-19 patients hospitalized in the city of Bukavu.

METHODOLOGY

Between June 2020 and June 2022, an open cohort of hypertensive and non-hypertensive COVID-19 patients admitted to two clinics in the city of Bukavu was formed. The primary endpoint was the occurrence of death. Thus, a prospective modeling of mortality by the Kaplan-Meier estimator was carried out.

RESULTS

Among the 178 admissions for COVID-19, 68 (38.2%) patients were hypertensive. Compared to non-hypertensives, hypertensive patients were significantly older [61.0 (56.0-71.0) vs. 48.0 (32.7-64.0); p < 0,0001). During the observation period of 1059 patient-days, the incidence of death (3.2/100 patient-days) was non-significantly higher in hypertensive patients (4.3/100 patient-days) (p = 0.06). On the other hand, the independent predictors of death were Sepsis [adjusted HR = 3.7 (1.5-8.7)], CRP > 100 mg/L [adjusted HR = 3.0 (1.2-7, 0)] and SaO₂ < 90 % [adjusted HR = 3.9 (1.3-11.8)].

CONCLUSION

This study shows that hypertension was very common in patients admitted for COVID-19 in the city of Bukavu but did not influence the vital prognosis of the latter, thus confirming the finds of most authors who have addressed the question.

Predictors of mortality in patients with COVID-19 infection in different health-care settings: A retrospective analysis from a CORACLE study group

BACKGROUND

Despite the large number of hospitalized patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, few data are available about risk factors and mortality in subjects with nosocomially acquired respiratory infection of Coronavirus Disease 2019 (COVID-19).

METHODS

We retrospectively evaluated in a multicentric study -during the pre-vaccination era-all patients admitted with confirmed diagnosis of nosocomial COVID-19 (NC). Patients were classified according to provenance: hospital-acquired NC or long-term care (LTC) facilities.

RESULTS

Among overall 1047 patients evaluated with COVID-19, 137 had a confirmed diagnosis of NC (13%). 78 (56.9%) patients had hospital-acquired NC and 59 (43%) had LTC NC. Overall mortality was 35.8%, in hospital-acquired NC 24.4%, in LTC NC 50.8% (p < 0.001) (Log Rank test: p = 0.001). Timing of diagnosis was significantly different between hospital acquired and LTC NC (3.5 vs 10 days, p < 0.001). In multivariate analysis age, intensive-care unit admission, LTC provenance and sepsis were significant predictors of mortality in patients with NC infection.

CONCLUSION

Patients with NC are at higher risk of mortality (especially for LTC NC) and required preventive strategies, early diagnosis, and treatment to avoid COVID-19 cluster.

Assessment of Clinical Indicators Registered on Admission to the Hospital Related to Mortality Risk in Cancer Patients with COVID-19

BACKGROUND

Oncology patients are a particularly vulnerable group to the severe course of COVID-19 due to, e.g., the suppression of the immune system. The study aimed to find links between parameters registered on admission to the hospital and the risk of later death in cancer patients with COVID-19.

METHODS

The study included patients with a reported history of malignant tumor (n = 151) and a control group with no history of cancer (n = 151) hospitalized due to COVID-19 between March 2020 and August 2021. The variables registered on admission were divided into categories for which we calculated the multivariate Cox proportional hazards models.

RESULTS

Multivariate Cox proportional hazards models were successfully obtained for the following categories: Patient data, Comorbidities, Signs recorded on admission, Medications used before hospitalization and Laboratory results recorded on admission. With the models developed for oncology patients, we identified the following variables that registered on patients' admission were linked to significantly increased risk of death. They are: male sex, presence of metastases in neoplastic disease, impaired consciousness (somnolence or confusion), wheezes/rhonchi, the levels of white blood cells and neutrophils.

CONCLUSION

Early identification of the indicators of a poorer prognosis may serve clinicians in better tailoring surveillance or treatment among cancer patients with COVID-19.

Prognostic factors for mortality, intensive care unit and hospital admission due to SARS-CoV-2: a systematic review and meta-analysis of cohort studies in Europe

BACKGROUND

As mortality from coronavirus disease 2019 (COVID-19) is strongly age-dependent, we aimed to identify population subgroups at an elevated risk for adverse outcomes from COVID-19 using age-/gender-adjusted data from European cohort studies with the aim to identify populations that could potentially benefit from booster vaccinations.

METHODS

We performed a systematic literature review and meta-analysis to investigate the role of underlying medical conditions as prognostic factors for adverse outcomes due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including death, hospitalisation, intensive care unit (ICU) admission and mechanical ventilation within three separate settings (community, hospital and ICU). Cohort studies that reported at least age and gender-adjusted data from Europe were identified through a search of peer-reviewed articles published until 11 June 2021 in Ovid Medline and Embase. Results are presented as odds ratios with 95% confidence intervals and absolute risk differences in deaths per 1000 COVID-19 patients.

FINDINGS

We included 88 cohort studies with age-/gender-adjusted data from 6 653 207 SARS-CoV-2 patients from Europe. Hospital-based mortality was associated with high and moderate certainty evidence for solid organ tumours, diabetes mellitus, renal disease, arrhythmia, ischemic heart disease, liver disease and obesity, while a higher risk, albeit with low certainty, was noted for chronic obstructive pulmonary disease and heart failure. Community-based mortality was associated with a history of heart failure, stroke, diabetes and end-stage renal disease. Evidence of high/moderate certainty revealed a strong association between hospitalisation for COVID-19 and solid organ transplant recipients, sleep apnoea, diabetes, stroke and liver disease.

INTERPRETATION

The results confirmed the strong association between specific prognostic factors and mortality and hospital admission. Prioritisation of booster vaccinations and the implementation of nonpharmaceutical protective measures for these populations may contribute to a reduction in COVID-19 mortality, ICU and hospital admissions.

Severe COVID-19 May Impact Hepatic Fibrosis /Hepatic Stellate Cells Activation as Indicated by a Pathway and Population Genetic Study

Coronavirus disease 19 (COVID-19) has affected over 112 million people and killed more than 2.5 million worldwide. When the pandemic was declared, Spain and Italy accounted for 29% of the total COVID-19 related deaths in Europe, while most infected patients did not present severe illness. We hypothesised that shared genomic characteristics, distinct from the rest of Europe, could be a contributor factor to a poor prognosis in these two populations. To identify pathways related to COVID-19 severity, we shortlisted 437 candidate genes associated with host viral intake and immune evasion from SARS-like viruses. From these, 21 were associated specifically with clinically aggressive COVID-19. To determine the potential mechanism of viral infections, we performed signalling pathway analysis with either the full list (n = 437) or the subset group (n = 21) of genes. Four pathways were significantly associated with the full gene list (Caveolar-mediated Endocytosis and the MSP-RON Signalling) or with the aggressive gene list (Hepatic Fibrosis/Hepatic Stellate Cell (HSC) Activation and the Communication between Innate and Adaptive Immune Cells). Single nucleotide polymorphisms (SNPs) from the ±1 Mb window of all genes related to these four pathways were retrieved from the dbSNP database. We then performed Principal Component analysis for these SNPs in individuals from the 1000 Genomes of European ancestry. Only the Hepatic Fibrosis/HSC Activation pathway showed population-specific segregation. The Spanish and Italian populations clustered together and away from the rest of the European ancestries, with the first segregating further from the rest. Additional in silico analysis identified potential genetic markers and clinically actionable therapeutic targets in this pathway, that may explain the severe disease.

Determinants of long COVID among adults hospitalized for SARS-CoV-2 infection: A prospective cohort study

Rationale

Factors associated with long-term sequelae emerging after the acute phase of COVID-19 (so called "long COVID") are unclear. Here, we aimed to identify risk factors for the development of COVID-19 sequelae in a prospective cohort of subjects hospitalized for SARS-CoV-2 infection and followed up one year after discharge.

Methods

A total of 324 subjects underwent a comprehensive and multidisciplinary evaluation one year after hospital discharge for COVID-19. A subgroup of 247/324 who consented to donate a blood sample were tested for a panel of circulating cytokines.

Results

In 122 patients (37.8%) there was evidence of at least one persisting physical symptom. After correcting for comorbidities and COVID-19 severity, the risk of developing long COVID was lower in the 109 subjects admitted to the hospital in the third wave of the pandemic than in the 215 admitted during the first wave, (OR 0.69, 95%CI 0.51-0.93, p=0.01). Univariable analysis revealed female sex, diffusing capacity of the lungs for carbon monoxide (DLCO) value, body mass index, anxiety and depressive symptoms to be positively associated with COVID-19 sequelae at 1 year. Following logistic regression analysis, DLCO was the only independent predictor of residual symptoms (OR 0.98 CI 95% (0.96-0.99), p=0.01). In the subgroup of subjects with normal DLCO (> 80%), for whom residual lung damage was an unlikely explanation for long COVID, the presence of anxiety and depressive symptoms was significantly associated to persistent symptoms, together with increased levels of a set of pro-inflammatory cytokines: interferon-gamma, tumor necrosis factor-alpha, interleukin (IL)-2, IL-12, IL-1β, IL-17. In logistic regression analysis, depressive symptoms (p=0.02, OR 4.57 [1.21-17.21]) and IL-12 levels (p=0.03, OR 1.06 [1.00-1.11]) 1-year after hospital discharge were independently associated with persistence of symptoms.

Conclusions

Long COVID appears mainly related to respiratory sequelae, prevalently observed during the first pandemic wave. Among patients with little or no residual lung damage, a cytokine pattern consistent with systemic inflammation is in place.

COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies

Introduction

An excess in the daily fluctuation of COVID-19 in hospital admissions could cause uncertainty and delays in the implementation of care interventions. This study aims to characterize a possible source of extravariability in the number of hospitalizations for COVID-19 by considering age at admission as a potential explanatory factor. Age at hospitalization provides a clear idea of the epidemiological impact of the disease, as the elderly population is more at risk of severe COVID-19 outcomes. Administrative data for the Veneto region, Northern Italy from February 1, 2020, to November 20, 2021, were considered.

Methods

An inferential approach based on quasi-likelihood estimates through the generalized estimation equation (GEE) Poisson link function was used to quantify the overdispersion. The daily variation in the number of hospitalizations in the Veneto region that lagged at 3, 7, 10, and 15 days was associated with the number of news items retrieved from Global Database of Events, Language, and Tone (GDELT) regarding containment interventions to determine whether the magnitude of the past variation in daily hospitalizations could impact the number of preventive policies.

Results

This study demonstrated a significant increase in the pattern of hospitalizations for COVID-19 in Veneto beginning in December 2020. Age at admission affected the excess variability in the number of admissions. This effect increased as age increased. Specifically, the dispersion was significantly lower in people under 30 years of age. From an epidemiological point of view, controlling the overdispersion of hospitalizations and the variables characterizing this phenomenon is crucial. In this context, the policies should prevent the spread of the virus in particular in the elderly, as the uncontrolled diffusion in this age group would result in an extra variability in daily hospitalizations.

Discussion

This study demonstrated that the overdispersion, together with the increase in hospitalizations, results in a lagged inflation of the containment policies. However, all these interventions represent strategies designed to contain a mechanism that has already been triggered. Further efforts should be directed toward preventive policies aimed at protecting the most fragile subjects, such as the elderly. Therefore, it is essential to implement containment strategies before the occurrence of potentially out-of-control situations, resulting in congestion in hospitals and health services.

Long-Term Survival among Patients Hospitalized for COVID-19 during the First Three Epidemic Waves: An Observational Study in a Northern Italy Hospital

The mortality rate of hospitalized COVID-19 patients differed strongly between the first three pandemic waves. Nevertheless, their long-term survival has been poorly assessed. The aim of this study was to compare the clinical characteristics and mortality rates of 825 patients with coronavirus disease 2019 (COVID-19) infection who were hospitalized at the Alessandria hub hospital, in Northern Italy, during the first fifty days of the first three pandemic waves. Each subject was followed in terms of vital status for six months from the date of hospital admission or until deceased. Patients admitted during the three waves differed in age (p = 0.03), disease severity (p < 0.0001), Charlson comorbidity index (p = 0.0002), oxygen therapy (p = 0.002), and invasive mechanical ventilation (p < 0.0001). By the end of follow-up, 309 deaths (38.7%) were observed, of which 186 occurred during hub hospitalization (22.5%). Deaths were distributed differently among the waves (p < 0.0001), resulting in being higher amongst those subjects admitted during the first wave. The COVID-19 infection was reported as the main cause of death and patients with a higher mortality risk were those aged ≥65 years [adjusted HR = 3.40 (95% CI 2.20-5.24)], with a higher disease severity [adjusted HR = 1.87 (95%CI 1.43-2.45)], and those requiring oxygen therapy [adjusted HR = 2.30 (95%CI 1.61-3.30)]. In conclusion, COVID-19 patients admitted to our hub hospital during the second and the third waves had a lower risk of long-term mortality than those admitted during the first. Older age, more severe disease, and the need for oxygen therapy were among the strongest risk factors for poor prognosis.

COVID-19 Mortality in Public Hospitals in a Brazilian State: An Analysis of the Three Waves of the Pandemic

OBJECTIVE

To analyze COVID-19 deaths in public hospitals in a Brazilian state, stratified by the three waves of the pandemic, and to test their association with socio-clinical variables.

METHODS

Observational analytical study, where 5436 deaths by COVID-19 occurred in hospitals of the public network of Espírito Santo, between 1 April 2020, and 31 August 2021, stratified by the three waves of the pandemic, were analyzed. For the bivariate analyses, the Pearson's chi-square, Fisher's Exact or Friedman's tests were performed depending on the Gaussian or non-Gaussian distribution of the data. For the relationship between time from diagnosis to death in each wave, quantile regression was used, and multinomial regression for multiple analyses.

RESULTS

The mean time between diagnosis and death was 18.5 days in the first wave, 20.5 days in the second wave, and 21.4 days in the third wave. In the first wave, deaths in public hospitals were associated with the following variables: immunodeficiency, obesity, neoplasia, and origin. In the second wave, deaths were associated with education, O₂ saturation < 95%, chronic neurological disease, and origin. In the third wave, deaths were associated with race/color, education, difficulty breathing, nasal or conjunctival congestion, irritability or confusion, adynamia or weakness, chronic cardiovascular disease, neoplasms, and diabetes mellitus. Origin was associated with the outcome in the three waves of the pandemic, in the same way that education was in the second and third waves (p < 0.05).

CONCLUSION

The time interval between diagnosis and death can be impacted by several factors, such as: plasticity of the health system, improved clinical management of patients, and the start of vaccination at the end of January 2021, which covered the age group with the higher incidence of deaths. The deaths occurring in public hospitals were associated with socio-clinical characteristics.

Predictors of mortality among inpatients with COVID-19 infection in a tertiary referral center in the Philippines

•

Mortality data were comparable to those of early reports relating to the wild-type SARS-CoV-2.

•

Clinical and laboratory monitoring is critical during the 2nd to 3rd week of illness.

•

Common and inexpensive laboratory tests may aid in the monitoring of patients.

•

Clinical pathways can be adapted to local data, especially in resource-poor settings.

Objectives

The aim of this study was to determine the predictors of mortality and describe laboratory trends among adults with confirmed COVID-19.

Methods

The medical records of adult patients admitted to a referral hospital with COVID-19 were retrospectively reviewed. Demographic and clinical characteristics, and laboratory parameters, were compared between survivors and non-survivors. Predictors of mortality were determined by multivariate analysis. Mean laboratory values were plotted across illness duration.

Results

Of 1215 patients, 203 (16.7%) had mild, 488 (40.2%) moderate, 183 (15.1%) severe, and 341 (28.1%) critical COVID-19 on admission. In-hospital mortality was 18.2% (0% mild, 6.1% moderate, 15.8% severe, 47.5% critical). Predictors of mortality were age ≥ 60 years, COPD, qSOFA score ≥ 2, WBC > 10 × 10⁹/L, absolute lymphocyte count < 1000, neutrophil ≥ 70%, PaO₂/FiO₂ ratio ≤ 200, eGFR < 90 mL/min/1.73 m², LDH > 600 U/L, and CRP > 12 mg/L. Non-survivors exhibited an increase in LDH and decreases in PaO₂/FiO₂ ratio and eGFR during the 2nd–3rd week of illness.

Conclusion

The overall mortality rate was high. Predictors of mortality were similar to those of other reports globally. Marked inflammation and worsening pulmonary and renal function were evident among non-survivors by the 2nd–3rd week of illness.

Modulation of the Association Between Age and Death by Risk Factor Burden in Critically Ill Patients With COVID-19

Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death.

DESIGN

Multicenter cohort study.

SETTING

ICUs at 68 hospitals across the United States.

PATIENTS

A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity <0.001) after adjustment for covariates that included demographics, preexisting comorbidities, acute physiologic ICU factors, number of ICU beds, and treatments for COVID-19. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and 28-day mortality (p for interaction <0.001), but this effect modification was modest as age still had an exponential relationship with death in subgroups stratified by the number of risk factors.

CONCLUSIONS

In a large population of critically ill patients with COVID-19, age had an independent exponential association with death. The number of preexisting comorbidities and acute physiologic ICU factors modified the association between age and death, but age still had an exponential association with death in subgroups according to the number of risk factors present. Additional studies are needed to identify the mechanisms underpinning why older age confers an increased risk of death in critically ill patients with COVID-19.

Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score

Background

The hospital management of patients diagnosed with COVID-19 can be hampered by heterogeneous characteristics at entry into the emergency department. We aimed to identify demographic, clinical and laboratory parameters associated with higher risks of hospitalisation, oxygen support, admission to intensive care and death, to build a risk score for clinical decision making at presentation to the emergency department.

Methods

We carried out a retrospective study using linked administrative data and laboratory parameters available in the initial phase of the pandemic at the emergency department of the regional reference hospital of Pescara, Abruzzo, Italy, March–June 2020. Logistic regression and Cox modelling were used to identify independent predictors for risk stratification. Validation was carried out collecting data from an extended timeframe covering other variants of concern, including Alpha (December 2020–January 2021) and Delta/Omicron (January–March 2022).

Results

Several clinical and laboratory parameters were significantly associated to the outcomes of interest, independently from age and gender. The strongest predictors were: for hospitalisation, monocyte distribution width ≥ 22 (4.09; 2.21–7.72) and diabetes (OR = 3.04; 1.09–9.84); for oxygen support: saturation < 95% (OR = 11.01; 3.75–41.14), lactate dehydrogenase≥237 U/L (OR = 5.93; 2.40–15.39) and lymphocytes< 1.2 × 10³/μL (OR = 4.49; 1.84–11.53); for intensive care, end stage renal disease (OR = 59.42; 2.43–2230.60), lactate dehydrogenase≥334 U/L (OR = 5.59; 2.46–13.84), D-dimer≥2.37 mg/L (OR = 5.18; 1.14–26.36), monocyte distribution width ≥ 25 (OR = 3.32; 1.39–8.50); for death, procalcitonin≥0.2 ng/mL (HR = 2.86; 1.95–4.19) and saturation < 96% (HR = 2.74; 1.76–4.28). Risk scores derived from predictive models using optimal thresholds achieved values of the area under the curve between 81 and 91%. Validation of the scoring algorithm for the evolving virus achieved accuracy between 65 and 84%.

Conclusions

A set of parameters that are normally available at emergency departments of any hospital can be used to stratify patients with COVID-19 at risk of severe conditions. The method shall be calibrated to support timely clinical decision during the first hours of admission with different variants of concern.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08421-4.

Ongoing Mycophenolate Treatment Impairs Anti-SARS-CoV-2 Vaccination Response in Patients Affected by Chronic Inflammatory Autoimmune Diseases or Liver Transplantation Recipients: Results of the RIVALSA Prospective Cohort

Vaccines are the most effective means to prevent the potentially deadly effects of SARS-CoV-2 infection, but not all vaccinated individuals gain the same degree of protection. Patients undergoing chronic immunosuppressive therapy due to autoimmune diseases or liver transplants, for example, may show impaired anti-SARS-CoV-2 antibody response after vaccination. We performed a prospective observational study with parallel arms, aiming to (a) evaluate seroconversion after anti-SARS-CoV-2 mRNA vaccine administration in different subgroups of patients receiving immunosuppressive treatment for rheumatological or autoimmune diseases or to prevent organ rejection after liver transplantation and (b) identify negative predictors of IgG anti-SARS-CoV-2 development. Out of 437 eligible patients, 183 individuals were enrolled at the Rheumatology and Hepatology Tertiary Units of “Maggiore della Carità” University Hospital in Novara: of those, 52 were healthy subjects, while among the remaining 131 patients, 30 had a diagnosis of spondyloarthritis, 25 had autoimmune hepatitis, 10 were liver transplantation recipients, 23 suffered from connective tissue diseases (including 10 cases that overlapped with other diseases), 40 were treated for rheumatoid arthritis, and 5 had vasculitis. Moreover, all patients were receiving chronic immunosuppressive therapy. The immunogenicity of mRNA COVID-19 vaccines was evaluated by measuring IgG anti-SARS-CoV-2 antibody titers before vaccination and after 10, 30, and 90 days since the first dose administration. Of the selected cohort of patients, 24.0% did not develop any detectable anti-SARS-CoV-2 IgG after a complete mRNA-based two doses primary vaccination cycle. At univariate analysis, independent predictors of an absent antibody response to vaccine were a history of liver transplantation (OR 11.5, 95% CI 2.5−53.7, p = 0.0018), the presence of a comorbid active neoplasia (OR 26.4, 95% CI 2.8−252.4, p = 0.0045), and an ongoing immunosuppressive treatment with mycophenolate (MMF) (OR 14.0, 95% CI 3.6−54.9, p = 0.0002) or with calcineurin inhibitors (OR 17.5, 95% CI 3.1−99.0, p = 0.0012). At multivariate analysis, only treatment with MMF (OR 24.8, 95% CI 5.9−103.2, p < 0.0001) and active neoplasia (OR 33.2, 95% CI 5.4−204.1, p = 0.0002) were independent predictors of seroconversion failure. These findings suggest that MMF dose reduction or suspension may be required to optimize vaccine response in these patients.

The relationship of laboratory parameters and mortality of patients followed in intensive care units with COVID-19

Aim: We aimed to evaluate the parameters associated with mortality in COVID-19 patients followed up in the intensive care unit. Material and Method: Three hundred twenty-one patients followed up with the diagnosis of COVID-19 were included in the study. Demographic characteristics, laboratory and clinical parameters were compared in patients with and without mortality. Results: A higher intubation rate (98.6% vs. 10.9%) and longer hospitalization (10.0 vs. 8.0 days) were detected in the non-survivor group (p

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Objective: To analyze the survival of patients hospitalized with COVID-19 and its associated factors. Methods: Retrospective study of survival analysis in individuals notified and hospitalized with COVID-19 in the state of Espírito Santo, Brazil. As data source, the reports of hospitalized patients in the period from 1 March 2020, to 31 July 2021 were used. The Cox regression analysis plus the proportional risk assessment (assumption) were used to compare hospitalization time until the occurrence of the event (death from COVID-19) associated with possible risk factors. Results: The sample comprised 9806 notifications of cases, with the occurrence of 1885 deaths from the disease (19.22%). The mean age of the group was 58 years (SD ± 18.3) and the mean hospital length of stay was 10.5 days (SD ± 11.8). The factors that presented a higher risk of death from COVID-19, associated with a lower survival rate, were non-work-related infection (HR = 4.33; p < 0.001), age group 60−79 years (HR: 1.62; p < 0.001) and 80 years or older (HR = 2.56; p < 0.001), presence of chronic cardiovascular disease (HR = 1.18; p = 0.028), chronic kidney disease (HR = 1.5; p = 0.004), smoking (HR = 1.41; p < 0.001), obesity (HR = 2.28; p < 0.001), neoplasms (HR = 1.81; p < 0.001) and chronic neurological disease (HR = 1.68; p < 0.001). Conclusion: It was concluded that non-work-related infection, age group above or equal to 60 years, presence of chronic cardiovascular disease, chronic kidney disease, chronic neurological disease, smoking, obesity and neoplasms were associated with a higher risk of death, and, therefore, a lower survival in Brazilian patients hospitalized with COVID-19. The identification of priority groups is crucial for Health Surveillance and can guide prevention, control, monitoring, and intervention strategies against the new coronavirus.

Smoking is associated with increased risk of cardiovascular events, disease severity, and mortality among patients hospitalized for SARS-CoV-2 infections

The clinical sequalae of SARS-CoV-2 infection are in part dependent upon age and pre-existing health conditions. Although the use of tobacco products decreases cardiorespiratory fitness while increasing susceptibility to microbial infections, limited information is available on how smoking affects COVID-19 severity. Therefore, we examined whether smokers hospitalized for COVID-19 are at a greater risk for developing severe complications than non-smokers. Data were from all hospitalized adults with SARS-CoV-2 infection from the American Heart Association's Get-With-The-Guidelines COVID-19 Registry, from January 2020 to March 2021, which is a hospital-based voluntary national registry initiated in 2019 with 122 participating hospitals across the United States. Patients who reported smoking at the time of admission were classified as smokers. Severe outcome was defined as either death or the use of mechanical ventilation. Of the 31,545 patients in the cohort, 6,717 patients were 1:2 propensity matched (for age, sex, race, medical history, medications, and time-frame of hospital admission) and classified as current smokers or non-smokers according to admission data. In multivariable analyses, after adjusting for sociodemographic characteristics, medical history, medication use, and the time of hospital admission, patients self-identified as current smokers had higher adjusted odds of death (adjusted odds ratio [aOR], 1.41; 95% CI, 1.21-1.64), the use of mechanical ventilation (aOR 1.15; 95% CI 1.01-1.32), and increased risk of major adverse cardiovascular events (aOR, 1.27; 95% CI 1.05-1.52). Independent of sociodemographic characteristics and medical history, smoking was associated with a higher risk of severe COVID-19, including death.

National geographical pattern of COVID-19 hospitalization, case fatalities, and associated factors in patients covered by Iran Health Insurance Organization

Background

Understanding the Spatio-temporal distribution and interpersonal comparisons are important tools in etiological studies. This study was conducted to investigate the temporal and geographical distribution of COVID-19 hospitalized patients in the Iran Health Insurance Organization (IHIO) insured population (the second largest social health insurance organization) and the factors affecting their case fatality rate (CFR).

Methods

In this descriptive-analytical cross-sectional study, the demographic and clinical data of all insured of the IHIO who were hospitalized with COVID-19 in hospitals across the country until March 2021 was extracted from the comprehensive system of handling the inpatient documents of this organization. The Excel 2019 and GeoDA software were used for descriptive reporting and geographical distribution of variables. A multiple logistic regression model was used to estimate the Odds Ratio (OR) of death in patients with COVID-19 using STATA 14 software.

Results

During the first 14 months of the COVID-19 outbreak in Iran, 0.72% of the IHIO insured (303,887 individuals) were hospitalized with COVID-19. Hospitalization per 100,000 people varied from 192.51 in East Azerbaijan to 1,277.49 in Yazd province. The overall CFR in hospitalized patients was 14%. Tehran and Kohgiluyeh & BoyerAhmad provinces had the highest and lowest CFR with 19.39% and 5.19%, respectively. The highest odds of death were in those over 80 years old people (OR = 9.65), ICU-admitted (OR = 7.49), Hospitalized in governmental hospitals (OR = 2.08), Being a foreign national (OR = 1.45), hospitalized in November (OR = 1.47) and Residence in provinces such as Sistan & Baluchestan (OR = 1.47) and Razavi Khorasan (OR = 1.66) respectively. Furthermore, the odds of death were lower in females (OR = 0.81) than in males.

Conclusions

A sound understanding of the primary causes of COVID-19 death and severity in different groups can be the basis for developing programs focused on more vulnerable groups in order to manage the crisis more effectively and benefit from resources more efficiently.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13649-0.

Association of Obesity With COVID-19 Severity and Mortality: An Updated Systemic Review, Meta-Analysis, and Meta-Regression

Background

Obesity affects the course of critical illnesses. We aimed to estimate the association of obesity with the severity and mortality in coronavirus disease 2019 (COVID-19) patients.

Data Sources

A systematic search was conducted from the inception of the COVID-19 pandemic through to 13 October 2021, on databases including Medline (PubMed), Embase, Science Web, and Cochrane Central Controlled Trials Registry. Preprint servers such as BioRxiv, MedRxiv, ChemRxiv, and SSRN were also scanned.

Study Selection and Data Extraction

Full-length articles focusing on the association of obesity and outcome in COVID-19 patients were included. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used for study selection and data extraction. Our Population of interest were COVID-19 positive patients, obesity is our Intervention/Exposure point, Comparators are Non-obese vs obese patients The chief outcome of the study was the severity of the confirmed COVID-19 positive hospitalized patients in terms of admission to the intensive care unit (ICU) or the requirement of invasive mechanical ventilation/intubation with obesity. All-cause mortality in COVID-19 positive hospitalized patients with obesity was the secondary outcome of the study.

Results

In total, 3,140,413 patients from 167 studies were included in the study. Obesity was associated with an increased risk of severe disease (RR=1.52, 95% CI 1.41-1.63, p<0.001, I2 = 97%). Similarly, high mortality was observed in obese patients (RR=1.09, 95% CI 1.02-1.16, p=0.006, I2 = 97%). In multivariate meta-regression on severity, the covariate of the female gender, pulmonary disease, diabetes, older age, cardiovascular diseases, and hypertension was found to be significant and explained R2 = 40% of the between-study heterogeneity for severity. The aforementioned covariates were found to be significant for mortality as well, and these covariates collectively explained R2 = 50% of the between-study variability for mortality.

Conclusions

Our findings suggest that obesity is significantly associated with increased severity and higher mortality among COVID-19 patients. Therefore, the inclusion of obesity or its surrogate body mass index in prognostic scores and improvement of guidelines for patient care management is recommended.

Predictors and outcome of hyponatremia in patients with COVID 19: a single-center experience

Introduction/Aim: Hyponatremia (serum sodium 0.001) were the independent predictors of hyponatremia at the time of admission. The median length of hospital stay (LOS) was longer in patients with hyponatremia than patients with normonatremia (10 days vs. 8 days, p < 0.001). In multivariate analysis, hyponatremia was significantly associated with ICU admission or the need for mechanical ventilation (adjusted OR, 1.72; 95% confidence interval [95% CI], 1.03 to 2.85; p = 0.036). The severity of pneumonia, hemoglobin and lactate dehydrogenase levels, neutrophil-to-lymphocyte ratio (NLR), and body temperature were also associated with ICU admission or the need for mechanical ventilation. The oxygen saturation, male sex, serum albumin, NLR, and the ICU admission but not the hyponatremia on admission were significantly related to mortality. Conclusion: Hyponatremia on admission, even when mild, predicts a worse outcome in COVID-19 patients, and it should be considered in risk stratification.

Plasma Levels and Renal Handling of Amino Acids Contribute to Determination of Risk of Mortality or Feed of Ventilation in Patients with COVID-19

COVID-19 infection may lead to serious complications, e.g., need for mechanical ventilation or death in some cases. A retrospective analysis of patients referred to our COVID Emergency Department, indiscriminately, was performed. A routine lab analysis measured amino acids in plasma and urine of patients. Data of surviving and deceased patients and those requiring or not requiring mechanical ventilation were compared, and logistic regression analyses have been performed. Deceased patients were older, had higher blood glucose, potassium, AST, LDH, troponin, d-dimer, hsCRP, procalcitonin, interleukin-6 levels (p < 0.05 for all). They had lower plasma serine, glycine, threonine, tryptophan levels (p < 0.01), higher tyrosine and phenylalanine levels (p < 0.05), and higher fractional excretion of arginine, methionine, and proline (p < 0.05) than survivors. In a regression model, age, severity score of COVID-pneumonia, plasma levels of threonine and phenylalanine were predictors of in-hospital mortality. There was a difference in ventilated vs. non-ventilated patients in CT-scores, glucose, and renal function (p < 0.001). Using logistic regression, CT-score, troponin, plasma level, and fractional excretion of glycine were predictors of ventilation. Plasma levels and renal excretion of certain amino acids are associated with the outcome of COVID-19 infection beside other parameters such as the CT-score or age.

A SARS-CoV-2 Spike Receptor Binding Motif Peptide Induces Anti-Spike Antibodies in Mice andIs Recognized by COVID-19 Patients

The currently devastating pandemic of severe acute respiratory syndrome known as coronavirus disease 2019 or COVID-19 is caused by the coronavirus SARS-CoV-2. Both the virus and the disease have been extensively studied worldwide. A trimeric spike (S) protein expressed on the virus outer bilayer leaflet has been identified as a ligand that allows the virus to penetrate human host cells and cause infection. Its receptor-binding domain (RBD) interacts with the angiotensin-converting enzyme 2 (ACE2), the host-cell viral receptor, and is, therefore, the subject of intense research for the development of virus control means, particularly vaccines. In this work, we search for smaller fragments of the S protein able to elicit virus-neutralizing antibodies, suitable for production by peptide synthesis technology. Based on the analysis of available data, we selected a 72 aa long receptor binding motif (RBM436-507) of RBD. We used ELISA to study the antibody response to each of the three antigens (S protein, its RBD domain and the RBM436-507 synthetic peptide) in humans exposed to the infection and in immunized mice. The seroreactivity analysis showed that anti-RBM antibodies are produced in COVID-19 patients and immunized mice and may exert neutralizing function, although with a frequency lower than anti-S and -RBD. These results provide a basis for further studies towards the development of vaccines or treatments focused on specific regions of the S virus protein, which can benefit from the absence of folding problems, conformational constraints and other advantages of the peptide synthesis production.

Baseline Plasma Gas6 Protein Elevation Predicts Adverse Outcomes in Hospitalized COVID-19 Patients

Reliable biomarkers allowing early patients’ stratification for the risk of adverse outcomes in COVID-19 are lacking. Gas6, together with its tyrosine kinase receptors named TAM, is involved in the regulation of immune homeostasis, fibrosis, and thrombosis. Our aim was to evaluate whether Gas6, sAxl, and sMerTK could represent early predictors of disease evolution either towards a negative (death or need of ICU admission) or a positive (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. To this purpose, between January and May 2021 (corresponding to third pandemic wave in Italy), 139 consecutive SARS-CoV-2 positive patients were enrolled in a prospective observational study. Plasma levels of these molecules were measured by ELISA at the time of hospitalization and after 7 and 14 days. We observed that higher plasma Gas6 concentrations at hospital admission were associated with a worsening in clinical conditions while lower sMerTK concentrations at baseline and after 7 days of hospitalization were associated with a more favorable outcome. At multivariate analysis, after correction for demographic and COVID-19 severity variables (NEWS2 and PiO2/FiO2), only Gas6 measured at baseline predicted an adverse prognosis with an odds ratio of 1.03 (C.I. 1.01-10.5). At ROC curve analysis, baseline Gas6 levels higher than 58.0 ng/ml predicted a severe disease evolution with 53.3% sensitivity and 77.6% specificity (area under the curve 0.653, $p=0.01$ , likelihood ratio of 2.38, IQR: 1.46-3.87). Taken together, these results support the hypothesis that a dysregulation in the Gas6/TAM axis could play a relevant role in modulating the course of COVID-19 and suggest that plasma Gas6 may represent a promising prognostic laboratory parameter for this condition.

Within and Between Subject Spectral Fingerprints of EEG-Microstate Parameters

Early reports have claimed that EEG microstate features (e.g. their mean duration or percent of time covered) are largely independent from EEG spectra. This has meanwhile been questioned for conceptual and empirical reasons, but so far, EEG spectral power map correlates of microstate features have not been reported. We present the results of such analyses, conducted both within and between subjects, and report patterns of systematic changes in local EEG spectral amplitude associated with the mean duration, frequency of occurrence and relative contribution of particular microstate classes. The combination of EEG microstate analysis with spectral analysis may therefore be helpful to come to a deeper understanding of local patterns of activation and inhibition associated with particular microstate classes.

The study of serum hsCRP, ferritin, IL-6 and plasma D-dimer in COVID-19: a retrospective study

OBJECTIVES

The cut off values for serum high sensitivity C-reactive protein (hsCRP), ferritin, interleukin 6 (IL-6) and plasma D-dimer could be of profound help in detecting COVID-19 patients at risk of adverse outcomes. Therefore, the aim of the present study is to determine the cut off values of the serum hsCRP, ferritin, IL-6 and plasma D-dimer in predicting mortality in COVID-19 patients.

METHODS

Four hundred RT-PCR confirmed cases of COVID-19 were sub divided into two groups based on their outcome during hospitalisation. Group I consisted of survivors and Group II consisted of non-survivors. The survivors were further divided into three sub-groups: mild, moderate and severe based on the severity of infection. The laboratory data of serum hsCRP, ferritin, IL-6 and plasma D-dimer for all these patients was retrieved from the Medical Record Section of the Hospital.

RESULTS

Mean serum hsCRP, ferritin, IL-6 and plasma D-dimer levels were significantly higher in non-survivors as compared to survivors of COVID-19. The levels of these biomarkers correlated with the severity of COVID-19 illness. ROC curve analysis revealed that plasma D-dimer is having a better predictive value as compared to other parameters in predicting mortality in COVID-19.

CONCLUSIONS

The serum hsCRP, ferritin, IL-6 and plasma D-dimer levels could be used in risk stratification of COVID-19 patients. The optimum cut off given by the current study could be considered in predicting adverse outcome in these patients. Amongst the many studied biomarkers, plasma D-dimer might be the best early biomarker to predict mortality in COVID-19 patients.

High mortality among hospitalized adult patients with COVID-19 pneumonia in Peru: A single centre retrospective cohort study

Background

Peru is the country with the world’s highest COVID-19 death rate per capita. Characteristics associated with increased mortality among adult patients with COVID-19 pneumonia in this setting are not well described.

Methods

Retrospective, single-center cohort study including 1537 adult patients hospitalized with a diagnosis of SARS-CoV-2 pneumonia between May 2020 and August 2020 at a national hospital in Lima, Peru. The primary outcome measure was in-hospital mortality.

Results

In-hospital mortality was 49.71%. The mean age was 60 ± 14.25 years, and 68.38% were males. We found an association between mortality and inflammatory markers, mainly leukocytes, D-dimer, lactate dehydrogenase, C-reactive protein and ferritin. A multivariate model adjusted for age, hypertension, diabetes mellitus, and corticosteroid use demonstrated that in-hospital mortality was associated with greater age (RR: 2.01, 95%CI: 1.59–2.52) and a higher level of oxygen requirement (RR: 2.77, 95%CI: 2.13–3.62). Conclusions: In-hospital mortality among COVID-19 patients in Peru is high and is associated with greater age and higher oxygen requirements.

The detrimental impact of elevated Ferritin to Iron ratio on in-hospital prognosis of patients with COVID-19

Background

Acute viral infections, including coronavirus disease 2019 (COVID-19), are characterized by the dysregulation of iron metabolism, resulting in high serum ferritin and low iron levels.

Research design and methods

This study aimed to evaluate the prospective impact of iron metabolism dysregulation, as expressed by serum Ferritin-to-Iron Ratio (FIR), on the in-hospital prognosis of patients with COVID-19. Serum levels of ferritin and iron, as well as other iron metabolism markers and recognized prognostic indicators of COVID-19 severity, were measured in 362 patients consecutively hospitalized for COVID-19. The prospective relationship between FIR and the risk of the composite outcome of intensive care unit (ICU) admission/in-hospital death was analyzed.

Results

In the population examined (mean age 74 ± 15 years, males 55%), the rates of radiographic signs of pneumonia, respiratory distress, and the need for noninvasive ventilation were higher in patients with high FIR (≥29.2, the 75^th percentile) than in those with low FIR (<29.2, the 75^th percentile) (p < 0.05 for all comparisons). High FIR was associated with a 1.7-fold (HR 1.709, 95% CI 1.017–2.871, p = 0.043) higher risk of ICU admission/in-hospital death.

Conclusions

Increasing FIR values significantly and independently predicts worse in-hospital prognosis in hospitalized patients with COVID-19.

Prognostic Markers in Hospitalized COVID-19 Patients: The Role of IP-10 and C-Reactive Protein

Background

SARS-CoV-2 is responsible for COVID-19, a clinically heterogeneous disease, ranging from being completely asymptomatic to life-threating manifestations. An unmet clinical need is the identification at disease onset or during its course of reliable biomarkers allowing patients' stratification according to disease severity. In this observational prospective cohort study, patients' immunologic and laboratory signatures were analyzed to identify independent predictors of unfavorable (either death or intensive care unit admission need) or favorable (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome.

Methods

Between January and May 2021 (third wave of the pandemic), we enrolled 139 consecutive SARS-CoV-2 positive patients hospitalized in Northern Italy to study their immunological and laboratory signatures. Multiplex cytokine, chemokine, and growth factor analysis, along with routine laboratory tests, were performed at baseline and after 7 days of hospital stay.

Results

According to their baseline characteristics, the majority of our patients experienced a moderate to severe illness. At multivariate analysis, the only independent predictors of disease evolution were the serum concentrations of IP-10 (at baseline) and of C-reactive protein (CRP) after 7 days of hospitalization. Receiver-operating characteristic (ROC) curve analysis confirmed that baseline IP − 10 > 4271 pg/mL and CRP > 2.3 mg/dL at 7 days predict a worsening in clinical conditions (87% sensitivity, 66% specificity, area under the curve (AUC) 0.772, p < 0.001 and 83% sensitivity, 73% specificity, AUC 0.826, p < 0.001, respectively).

Conclusions

According to our results, baseline IP-10 and CRP after 7 days of hospitalization could be useful in driving clinical decisions tailored to the expected disease trajectory in hospitalized COVID-19 patients.

The Prognostic Role of Metabolic and Endocrine Parameters for the Clinical Severity of COVID-19

Objective

An outbreak of coronavirus disease-19 (COVID-19) began in December 2019 and spread globally, overwhelming the entire world. COVID-19 is a public health emergency of international concern. Due to its high morbidity and mortality rate, recognition of its risk and prognostic factors is important. We aimed to understand the relationship between metabolic and endocrine parameters and the prognosis of COVID-19.

Methods and Materials

This was a cross-sectional clinical study. A total of 70 patients with severe COVID-19 were enrolled. Laboratory results at the first admission time (including complete blood count, C-reactive protein, lactate dehydrogenase, blood glucose, calcium, phosphate, albumin, creatinine, magnesium, lipid profiles, liver enzymes, thyroid hormones, cortisol, and vitamin D) and outcome data were recorded. We divided patients into (1) intensive care unit- (ICU-) admitted and non-ICU-admitted and (2) survivors and nonsurvivors for estimation of severity and prognosis. We determined the risk factors associated with critical illness and poor prognosis.

Results

Patients with higher white blood cell (WBC) count and phosphate levels had significantly higher ICU admission rates. According to univariate analysis, serum levels of T3, phosphate, and WBC as well as the duration of hospitalization were associated with mortality. Multivariate analysis revealed that only WBC and duration of hospitalization were independent predictors for mortality rate in COVID-19 patients.

Conclusion

Our findings suggest that longer duration of hospitalization and higher WBC count are associated with poor outcomes in patients with COVID-19.

The Impact of Tobacco Use on COVID-19 Outcomes: A Systematic Review

Introduction

Tobacco use increases risks for numerous diseases, including respiratory illnesses. We examined the literature to determine whether a history of tobacco use increases risks for adverse outcomes among COVID-19 patients.

Methods

We conducted a systematic search of PubMed, LitCovid, Scopus, and Europe PMC (for preprints) using COVID-19 and tobacco-related terms. We included studies of human subjects with lab-confirmed COVID-19 infections that examined tobacco use history as an exposure and used multivariable analyses. The data was collected between March 31^st, 2020, and February 20^th, 2021. Outcomes included mortality, hospitalization, ICU admission, mechanical ventilation, and illness severity.

Results

Among the 39 studies (33 peer-reviewed, 6 preprints) included, the most common outcome assessed was mortality (n = 32). The majority of these studies (17/32) found that tobacco use increased risk, one found decreased risk, and 14 found no association. Tobacco use was associated with increased risk of hospitalization in 7 of 10 studies, ICU admission in 6 of 9 studies, mechanical ventilation in 2 of 6 studies, and illness severity in 3 of 9 studies. One study found that tobacco use history increased risk of pulmonary embolism in COVID-19 patients. Tobacco use was found to compound risks associated with diabetes (n = 1), cancer (n = 2), and chronic liver disease (n = 1).

Conclusion

There is strong evidence that tobacco use increases risks of mortality and disease severity/progression among COVID-19 patients. Public health efforts during the pandemic should encourage tobacco users to quit use and seek care early and promote vaccination and other preventive behaviors among those with a history of tobacco use.

Pneumopathie à SARS-CoV-2 et sujets âgés ou très âgés : quel devenir ? Expérience d’un service de pneumologie

Introduction

Les sujets âgés sont à haut risque de formes sévères et de décès par pneumonie à SARS-CoV-2 [1]. L’intérêt d’admettre les patients très âgés en réanimation est déjà discuté habituellement encore plus en période pandémique. L’objectif est d’étudier le devenir des patients de plus de 80 ans pris en charge dans le service de pneumologie du CHU de Toulouse.

Méthodes

Étude sur des données cliniques de 67 patients de 80 ans et plus hospitalisés entre janvier 2020 et avril 2021 pour pneumopathie hypoxémiante à SARS-CoV-2 en pneumologie.

Résultats

Sur 67 patients inclus (38 H), l’âge moyen était de 86 + 4,3 ans. Ils présentaient des antécédents cardiovasculaires (88,8 %), métaboliques (46,6 %), et des pathologies respiratoires (26,8 %). Une oxygénothérapie à haut débit (OHD) a été mise en place chez 40 patients (59,7 %), dont 29 (72,5 %) hospitalisés en unité traditionnelle et 11 (27,5 %) en unité de soins intensifs respiratoires (USIR). Parmi eux, un patient a été secondairement orienté en réanimation. L’OHD a été réalisée en alternance avec de la pression positive continue (CPAP) chez 18 patients (45 %). Parmi les traitements médicamenteux, 32 patients (47,8 %) ont reçu une corticothérapie à dose adaptée. Tous les patients ont été anticoagulés. Trente-sept patients (57,2 %) ont été récusés de toute prise en charge invasive. Vingt et un patients (31,3 %) sont décédés ; dans le sous-groupe des patients hospitalisés en USIR, 3 (27,3 %) sont décédés. Vingt-quatre patients (35,8 %) ont pu rentrer à domicile, 21 patients (31,3 %) ont été transférés vers des secteurs de soins de suite et réadaptation (SSR). Parmi les patients ayant bénéficié d’OHD, 19 (47,5 %) sont décédés, 12 (30 %) ont été transférés en soins de suite et réadaptation (SSR), et 9 (22,5 %) sont rentrés à domicile.

Conclusion

Le taux de mortalité observé dans notre population est concordant avec ce qui a été observé dans une cohorte de patients hospitalisés sans distinction d’âge (29,7 %, [2]). Les services de pneumologie, où les techniques d’oxygénation non invasives sont maîtrisées, semblent donc particulièrement adaptés à la prise en charge des sujets âgés, notamment en période pandémique où les places en réanimation manquent.

Mortality predictors in a cohort of patients with COVID-19 admitted to a large tertiary hospital in the city of São Paulo, Brazil: a retrospective study

BACKGROUND

There is discrepant information across countries regarding the natural history of patients admitted to hospitals with coronavirus disease (COVID-19), in addition to a lack of data on the scenario in Brazil.

OBJECTIVE

To determine the mortality predictors in COVID-19 patients admitted to a tertiary hospital in São Paulo, Brazil.

DESIGN AND SETTING

A retrospective analysis of medical records of COVID-19 patients admitted to the Hospital Central da Irmandade da Santa Casa de Misericórdia of São Paulo.

METHODS

Overall, 316 patients with laboratory-confirmed COVID-19 between March 1, 2020, and July 31, 2020, were included. The analysis included the baseline characteristics, clinical progression, and outcomes.

RESULTS

The mortality rate of the sample was 51.27%. Age ≥ 60 years was determined as a risk factor after multivariate logistic regression analysis. Patients with an oxygen (O2) saturation ≤ 94% upon admission accounted for 87% of the deaths (P < 0.001). Vasoactive drugs were used in 92% (P < 0.001) of patients who progressed to death, and mechanical ventilation was employed in 88% (P < 0.001) of such patients. However, patients who received corticosteroids concomitantly with mechanical ventilation had a better prognosis than those who did not. The progressive degree of pulmonary involvement observed on chest computed tomography was correlated with a worse prognosis. The presence of thrombocytopenia has been considered as a risk factor for mortality.

CONCLUSION

The main predictors of in-hospital mortality after logistic regression analysis were age, O2 saturation ≤ 94% upon admission, use of vasoactive drugs, and presence of thrombocytopenia.

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.

Deep dissection of the antiviral immune profile of patients with COVID-19

In light of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variants potentially undermining humoral immunity, it is important to understand the fine specificity of the antiviral antibodies. We screened 20 COVID-19 patients for antibodies against 9 different SARS-CoV-2 proteins observing responses against the spike (S) proteins, the receptor-binding domain (RBD), and the nucleocapsid (N) protein which were of the IgG1 and IgG3 subtypes. Importantly, mutations which typically occur in the B.1.351 "South African" variant, significantly reduced the binding of anti-RBD antibodies. Nine of 20 patients were critically ill and were considered high-risk (HR). These patients showed significantly higher levels of transforming growth factor beta (TGF-β) and myeloid-derived suppressor cells (MDSC), and lower levels of CD4+ T cells expressing LAG-3 compared to standard-risk (SR) patients. HR patients evidenced significantly higher anti-S1/RBD IgG antibody levels and an increased neutralizing activity. Importantly, a large proportion of S protein-specific antibodies were glycosylation-dependent and we identified a number of immunodominant linear epitopes within the S1 and N proteins. Findings derived from this study will not only help us to identify the most relevant component of the anti-SARS-CoV-2 humoral immune response but will also enable us to design more meaningful immunomonitoring methods for anti-COVID-19 vaccines.

Plasma S-Adenosylmethionine Is Associated with Lung Injury in COVID-19

Objective

S-Adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH) are indicators of global transmethylation and may play an important role as markers of severity of COVID-19.

Methods

The levels of plasma SAM and SAH were determined in patients admitted with COVID-19 (n = 56, mean age = 61). Lung injury was identified by computed tomography (CT) in accordance with the CT0-4 classification.

Results

SAM was found to be a potential marker of lung damage risk in COVID-19 patients (SAM > 80 nM; CT3,4 vs. CT 0-2: relative ratio (RR) was 3.0; p = 0.0029). SAM/SAH > 6.0 was also found to be a marker of lung injury (CT2-4 vs. CT0,1: RR = 3.47, p = 0.0004). There was a negative association between SAM and glutathione level (ρ = −0.343, p = 0.011). Interleukin-6 (IL-6) levels were associated with SAM (ρ = 0.44, p = 0.01) and SAH (ρ = 0.534, p = 0.001) levels.

Conclusions

A high SAM level and high methylation index are associated with the risk of lung injury in patients with COVID-19. The association of SAM with IL-6 and glutathione indicates an important role of transmethylation in the development of cytokine imbalance and oxidative stress in patients with COVID-19.

Early Prediction of Disease Progression in Patients with Severe COVID-19 Using C-Reactive Protein to Albumin Ratio

Background

Early identification of patients with severe coronavirus disease (COVID-19) at an increased risk of progression may promote more individualized treatment schemes and optimize the use of medical resources. This study is aimed at investigating the utility of the C-reactive protein to albumin (CRP/Alb) ratio for early risk stratification of patients.

Methods

We retrospectively reviewed 557 patients with COVID-19 with confirmed outcomes (discharged or deceased) admitted to the West Court of Union Hospital, Wuhan, China, between January 29, 2020 and April 8, 2020. Patients with severe COVID-19 (n = 465) were divided into stable (n = 409) and progressive (n = 56) groups according to whether they progressed to critical illness or death during hospitalization. To predict disease progression, the CRP/Alb ratio was evaluated on admission.

Results

The levels of new biomarkers, including neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, CRP/Alb ratio, and systemic immune-inflammation index, were higher in patients with progressive disease than in those with stable disease. Correlation analysis showed that the CRP/Alb ratio had the strongest positive correlation with the sequential organ failure assessment score and length of hospital stay in survivors. Multivariate logistic regression analysis showed that percutaneous oxygen saturation (SpO2), D-dimer levels, and the CRP/Alb ratio were risk factors for disease progression. To predict clinical progression, the areas under the receiver operating characteristic curves of Alb, CRP, CRP/Alb ratio, SpO2, and D-dimer were 0.769, 0.838, 0.866, 0.107, and 0.748, respectively. Moreover, patients with a high CRP/Alb ratio (≥1.843) had a markedly higher rate of clinical deterioration (log − rank p < 0.001). A higher CRP/Alb ratio (≥1.843) was also closely associated with higher rates of hospital mortality, ICU admission, invasive mechanical ventilation, and a longer hospital stay.

Conclusion

The CRP/Alb ratio can predict the risk of progression to critical disease or death early, providing a promising prognostic biomarker for risk stratification and clinical management of patients with severe COVID-19.

Long-lasting consequences of Coronavirus disease 19 pneumonia: a systematic review

INTRODUCTION

Coronavirus Disease 19 (Covid-19) is an infectious disease caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have plenty of data about the clinical features of the disease's acute phase, while little is known about the long-term consequences on survivors.

EVIDENCE ACQUISITION

We aimed to review systematically emerging evidence about clinical and functional consequences of Covid-19 pneumonia months after hospital discharge.

EVIDENCE SYNTHESIS

Current evidence supports the idea that a high proportion of Covid-19 survivors complain of symptoms months after the acute illness phase, being fatigue and reduced tolerance to physical effort the most frequently reported symptom. The strongest association for these symptoms is with the female gender, while disease severity seems less relevant. Respiratory symptoms are associated with a decline in respiratory function and, conversely, seem to be more frequent in those who experienced a more severe acute pneumonia. Current evidence highlighted a persistent motor impairment which is, again, more prevalent among those survivors who experienced a more severe acute phase of the disease. Additionally, the persistence of symptoms is a primary determinant of mental health outcome, with anxiety, depression, sleep disturbances, and post-traumatic stress symptoms being commonly reported in Covid-19 survivors.

CONCLUSIONS

Current literature highlights the importance of a multidisciplinary approach to Coronavirus Disease 19 since the sequelae appear to involve different organs and systems. Given the pandemic outbreak's size, this is a critical public health issue: a better insight on this topic should inform clinical decisions about the modalities of follow-up for Covid-19 survivors.

Long-term sequelae are highly prevalent one year after hospitalization for severe COVID-19

Many coronavirus disease 2019 (Covid-19) survivors show symptoms months after acute illness. The aim of this work is to describe the clinical evolution of Covid-19, one year after discharge. We performed a prospective cohort study on 238 patients previously hospitalized for Covid-19 pneumonia in 2020 who already underwent clinical follow-up 4 months post-Covid-19. 200 consented to participate to a 12-months clinical assessment, including: pulmonary function tests with diffusing lung capacity for carbon monoxide (DLCO); post-traumatic stress (PTS) symptoms evaluation by the Impact of Event Scale (IES); motor function evaluation (by Short Physical Performance Battery and 2 min walking test); chest Computed Tomography (CT). After 366 [363–369] days, 79 patients (39.5%) reported at least one symptom. A DLCO < 80% was observed in 96 patients (49.0%). Severe DLCO impairment (< 60%) was reported in 20 patients (10.2%), related to extent of CT scan abnormalities. Some degree of motor impairment was observed in 25.8% of subjects. 37/200 patients (18.5%) showed moderate-to-severe PTS symptoms. In the time elapsed from 4 to 12 months after hospital discharge, motor function improves, while respiratory function does not, being accompanied by evidence of lung structural damage. Symptoms remain highly prevalent one year after acute illness.