The Spanish Society of Pulmonology and Thoracic Surgery created a registry for hospitalised patients with COVID-19 and the different types of respiratory support used (RECOVID). Objectives. To describe the profile of hospitalised patients with COVID-19, comorbidities, respiratory support treatments and setting. In addition, we aimed to identify varying profiles of patients according to outcomes and the complexity of respiratory support needed.

Multicentre, observational study in 49 Spanish hospitals. A protocol collected demographic data, comorbidities, respiratory support, treatment setting and 1-year follow-up. Patients were described using either frequency and percentages or median and interquartile range, as appropriate. A cluster analysis made it possible to identify different types of profile among the patients.

In total, 2148 of 2454 hospitalised patients (87.5%) received care in the conventional ward, whilst 126 in IRCU and 180 in ICU. In IRCU, 30% required high-flow nasal oxygen whilst 25%, non-invasive mechanical ventilation and 17%, mechanical ventilation. Four clusters of patients were identified. Two clusters were more likely to require IRCU/ICU admission, although primarily Cluster 2: Cluster (C) 1 consisted of patients without comorbidities and C2, those with comorbidities. Both presented higher inflammatory levels and lower lymphocyte count and SpO2/FiO2; however, C2 showed worse values. Two different clusters identified patients requiring less complex respiratory support. C3 presented higher comorbidities and elevated lymphocyte count, SpO2/FiO2 and low C-reactive protein (CRP). C4 included those without comorbidities except for arterial hypertension, lymphopenia and an intermediate CRP. In-hospital mortality and subsequent 1-year mortality were greater for C2 (28.6% and 7.1%) and C1 (11.1%, 8.3%) than for C4 (3.3%, 1.8%) and C3 (0%, 0%).

The cluster analysis identified four clinical phenotypes requiring distinct types of respiratory support, with great differences present per characteristics and outcomes.

Although, COVID-19 has resulted in over 7 million deaths globally, many questions still remain about the risk factors for disease severity and the effects of variants and vaccinations over the course of the pandemic. To address this gap, we conducted a retrospective analysis of electronic health records from COVID-19 patients over 2.5 years of the COVID-19 pandemic to identify associated clinical features.

We analyze a retrospective cohort of 21,312 acute-care patients over a 2.5 year period and define six clinical trajectory groups (TGs) associated with demographics, diagnoses, vitals, labs, imaging, consultations, and medications.

We show that the proportion of mild patients increased over time, particularly during Omicron waves. Additionally, while mild and fatal patients had differences in age, age did not distinguish patients with severe versus critical disease. Furthermore, we find that both male sex and Hispanic/Latino ethnicity are associated with more severe/critical TGs. More severe patients also have a higher rate of neuropsychiatric diagnoses and consultations, along with an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin is one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (e.g. Vitamin D, thiamine, B vitamins) or received vitamin supplementation.

Our findings expand on clinical risk factors in COVID-19, and highlight the interaction between severity, nutritional status, and neuropsychiatric complications in acute care patients to enable identification of patients at risk for severe disease.

Cohort studies are a robust analytical observational study design that explore the difference in outcomes between two cohorts, differentiated by their exposure status. Despite being observational in nature, they are often included in systematic reviews of effectiveness, particularly when randomized controlled trials are limited or not feasible. Like all studies included in a systematic review, cohort studies must undergo a critical appraisal process to assess the extent to which a study has considered potential bias in its design, conduct, or analysis. Critical appraisal tools facilitate this evaluation. This paper introduces the revised critical appraisal tool for cohort studies, completed by the JBI Effectiveness Methodology Group, who are currently revising the suite of JBI critical appraisal tools for quantitative study designs. The revised tool responds to updates in methodological guidance from the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Working Group and reporting guidance from PRISMA 2020, providing a robust framework for evaluating risk of bias in a cohort study. Transparent and rigorous assessment using this tool will assist reviewers in understanding the validity and relevance of the results and conclusions drawn from a systematic review that includes cohort studies. This may contribute to better evidence-based decision-making in health care. This paper discusses the key changes made to the tool, outlines justifications for these changes, and provides practical guidance on how this tool should be interpreted and applied by systematic reviewers.

To investigate the outcomes of geriatric COVID-19 patients in a German academic setting during the pandemic. This study included 468 consecutive geriatric patients (≥ 70 years) who tested positive for SARS-CoV-2 and were treated at the University of Duisburg-Essen from 2/2020 to 3/2021. 74 patients were transferred to a geriatric hospital and a 12-month follow-up (prospective study) was performed in 51 patients. Clinical assessments evaluated depression (GDS), apathy (AES), cognitive status (MMST), mobility (TUG), health status (EQ-5D-5 L), and daily living activities (Barthel Index). Demographic and clinical data were also analyzed. Results showed that the mortality in this vulnerable group was 52% (n = 209). Long-term survival was higher in patients who received comprehensive geriatric treatment (74.3% vs. 51.8%). The duration of inpatient stay at the primary hospital was 13.3 ± 3.6 days, with 28.8% (n = 135) requiring intensive care. At the 12-month mark more patients with geriatric treatment lived in nursing homes. Barthel-Index/Timed-Up-and-Go-Test/MMST/AES/GDS, and EQ-5D-5 L indicated worse outcomes in the group who received geriatric treatment. Specialized geriatric care may improve survival in geriatric COVID-19 patients despite decreased long-term outcomes. Further research, including international studies like NAPKON, are encouraged to confirm these findings and explore potential interventions for improved outcomes in this vulnerable population.

Over the past 120 years, mortality associated with myasthenia gravis (MG) has steadily decreased while the incidence of MG has increased. While mortality due to MG has been ≤5 % for at least the past 25 years, the prevalence of MG has increased. This increase in prevalence of MG may be due, in part, to improvements in diagnostics but also to an aging global population and immunosenescence as the largest increases in MG prevalence have been in patients ≥65 years old. In fact, a "very late-onset" subtype of MG has been proposed for patients diagnosed at or after age 65 years. These patients are predominantly anti-AChR antibody positive and thymoma negative. Preferred therapeutic options differ based on age at MG onset. Immunosenescence may play a role not only in MG etiology but also in the increased susceptibility of MG patients to infection. Immunosuppressive effects of MG therapies can also increase vulnerability to infection. Despite the improvements in MG treatment, mortality in MG patients remains higher than in the non-MG population. This is partly due to increased vulnerability to infection but also due to infection acting as a precipitating factor for MG exacerbation or crisis. The increased infection risk inherent with MG and the increased risk resulting from some therapies calls for increased diligence in monitoring and treating infections in MG patients.

Genetic polymorphisms in genes that enable the production of an effective host immune response, such as single nucleotide polymorphisms (SNPS) in the IL-6, INF-alpha, IFN-gamma, IL-10, TGF-beta genes can cause unfavorable clinical conditions or susceptibility to pathologies. The objective of this work is to evaluate the epidemiological and genetic profile of professionals from health institutions during the first pandemic wave. A case-control study was performed with convenience sampling from health institutions (HI) workers from Belém-PA, Northern Brazil (N = 213), divided into symptomatology groups (Asymptomatic-AS, n = 91; and Symptomatic-SI, n = 122); and severity groups classified by chest computerized tomography-CCT data (symptomatic with pulmonary involvement-SCP, n = 37; symptomatic without pulmonary involvement-SSP, n = 8). Genotyping was performed by sanger sequencing for SNP TNF-α -308 G/A (rs1800629), IFN-γ +874 T/A (rs2430561), TGF-β codon 10 (rs1982073), codon 25 (rs1800471), IL-6 - 174 G/C (rs180079), IL-10 - 1082 A/T (rs1800896), -819 C/T (rs1800871), and -592 A/C (rs1800872), and statistical analysis through the Epilfo program. Significant association was observed between the presence of comorbidities and poor prognosis of COVID-19 (especially between COVID-19 and overweight and obesity). Only the TNF-α 308 G/A snp was significantly associated with the symptoms and severity of COVID-19. These findings about this TNF-α SNP passed in the multiple testing correction at a false discovery rate (FDR)< 0.05. These data can help medicine and the scientific community understand the influence of genetics and epidemiological parameters in combating COVID-19.

CD209L and its homologous protein CD209 act as alternative entry receptors for the SARS-CoV-2 virus and are highly expressed in the virally targeted tissues. We tested for the presence and clinical features of autoantibodies targeting these receptors and compared these with autoantibodies known to be associated with COVID-19.

Using banked samples (n = 118) from Johns Hopkins patients hospitalised with COVID-19, we defined autoantibodies against CD209 and CD209L by enzyme-linked immunosorbent assay (ELISA). Clinical associations of these antibodies were compared with those of patients with anti-interferon (IFN) and anti-angiotensin-converting enzyme-2 (ACE2) autoantibodies.

Amongst patients hospitalised with COVID-19, 19.5% (23/118) had IgM autoantibodies against CD209L and were more likely to have coronary artery disease (44% vs 19%, P = 0.03). Antibodies against CD209 were present in 5.9% (7/118); interestingly, all 7 were male (P = 0.02). In our study, the presence of either antibody was positively associated with disease severity [OR 95% confidence interval (95% CI): 1.80 (0.69-5.03)], but the association did not reach statistical significance. In contrast, 10/118 (8.5%) had IgG autoantibodies against IFNα, and 21 (17.8%) had IgM antibodies against ACE2. These patients had significantly worse prognosis (intubation or death) and prolonged hospital stays. However, when adjusting for patient characteristics on admission, only the presence of anti-ACE2 IgM remained significant [pooled common OR (95% CI), 4.14 (1.37, 12.54)].

We describe IgM autoantibodies against CD209 and CD209L amongst patients hospitalised with COVID-19. These were not associated with disease severity. Conversely, patients with either anti-ACE2 IgM or anti-IFNα IgG antibodies had worse outcomes. Due to the small size of the study cohort, conclusions drawn should be considered cautiously.

Remdesivir is FDA-approved for the treatment of hospitalized patients with severe COVID-19. Many patients improve clinically to allow for hospital dismissal before completing the 5-day course. In a prior work, patients who continued remdesivir in an outpatient setting experienced better 28-day clinical outcomes. Here, we assessed patients' perspectives and the economic impact of this outpatient practice.

Hospitalized patients who received remdesivir for COVID-19 at Mayo Clinic, Rochester, from 11/6/2020 to 11/5/2021 and were dismissed to continue remdesivir in the outpatient setting were surveyed. The cost of care was compared between those who remained hospitalized versus those who were dismissed.

93 (19.8 %) among 470 eligible patients responded to the electronic survey. Responders were older than non-responders. The majority (70.5 %) had symptoms resolved by the time of the survey. Ten (11.4 %) patients had persistent symptoms attributed to long COVID-19. The majority were satisfied with the quality of care (82.3 %) and overall experience (76.0 %) in the infusion clinic. After adjusting for gender, comorbidity score, and WHO severity scale, the predicted costs for the groups were $16,544 (inpatient) and $9,097 (outpatient) per patient (difference of $7,447; p < .01). An estimate of 1,077 hospital bed-days were made available to other patients as a result of this transition to outpatient.

An outpatient remdesivir program that allowed for early dismissal was perceived favorably by patients. The program resulted in significant cost and resource savings, the latter in terms of the availability of hospital beds for other patients needing critical services.

To this date, COVID-19 remains an unresolved pandemic, and the impairment of redox homeostasis dictates the severity of clinical outcomes. Here we examined initial UCLA cohort of 440 COVID-19 patients hospitalized between March 1st and April 1st, 2020, representing the first wave of the pandemic. The mean age was 58.88 ± 21.12, among which males were significantly more than females (55.5 % vs. 44.5 %), most distinctively in age group of 50-69. The age groups of 50-69 (33.6 %) and ≥70 (34.8 %) dominated. The racial composition was in general agreement with Census data with slight under-representation of Hispanics and Asians, and over-representation of Caucasians. Smoking was a significant factor (28.8 % vs. 11.0 % in LA population), likewise for obesity (BMI ≥30) (37.4 % vs. 27.7 % in LA population). Patients suffering from obesity or BMI<18.5 checked into ICU at a significantly higher rate. A 74.5 % of the patients had comorbidities including diabetes, chronic kidney disease, chronic pulmonary disease, congestive heart failure and peripheral vascular disease. The levels of d-dimer were drastically upregulated (1159.5 ng/mL), indicating hypercoagulative state. Upregulated LDH (328 IU/L) indicated significant tissue damages. A distorted redox hemeostasis is a common trait associated with these risk factors and clinical markers. A quarter of the patients received antivirals, among which Remdesivir most prescribed (23.6 %). Majority received antithrombotics (75 %), and antibiotics. Upon admission, 67 patients were intubated or received CPR; 177 patients eventually received intensive care (40.2 %). While 290 were discharged alive, 10 remained hospitalized, 73 were transferred, and 36 died with 3 palliatively discharged. In summary, our data fully characterized a Californian cohort of COVID-19 at the breaking phase of the pandemic, indicating that population demographics, biophysical characters, comorbidities and molecular pathological parameters have significant impacts on the evolvement of a pandemic. These provide critical insights into effective management of COVID-19, and future break from another pathogen.

The COVID-19 pandemic has flooded open databases with population-level data. However, individual-level structured data, such as the course of disease and contact tracing information, is almost non-existent in open databases. Publish a structured and cleaned COVID-19 dataset with the course of disease and contact tracing information for easy benchmarking of COVID-19 models. We gathered data from Taiwanese open databases and daily news reports. The outcome is a structured quantitative dataset encompassing the course of the disease of Taiwanese individuals, alongside their contact tracing information. Our dataset comprises 579 confirmed cases covering the period from January 21, to November 9, 2020, when the original SARS-CoV-2 virus was most prevalent in Taiwan. The data include features such as travel history, age, gender, symptoms, contact types between cases, date of symptoms onset, confirmed, critically ill, recovered, and dead. We also include the daily summary data at population-level from January 21, 2020, to May 23, 2022. Our data can help enhance epidemiological modelling.

COVID-19 disease affected the cognitive level of institutionalized patients in nursing homes, especially in the older subjects regardless of gender. This study aims to assess cognitive impairment using the Mini-Mental State Examination (MMSE) before and after COVID-19 infection, and to determine whether these changes varied based on gender.

A pre- and post-COVID-19 study was conducted, involving 68 geriatric patients (34 men and 34 women) from two nursing homes. Cognitive impairment was assessed using the MMSE.

COVID-19 infection had a notable impact on the cognitive health of older adults residing in nursing homes, primarily attributed to the social isolation they experienced. This effect was more pronounced in older individuals. A comparison of the MMSE results by gender before and after contracting COVID-19 revealed significant differences in attention and calculation, with women obtaining the worst score before the virus. However, following their recovery from the virus, men demonstrated significantly lower scores in time and space orientation and evocation.

COVID-19 has led to a decline in cognitive functioning, significantly worsening the mental state of older individuals, even after recovery from the virus. Consequently, it is crucial to implement proactive measures to prevent isolation and safeguard the cognitive well-being of this vulnerable population.

Exacerbated inflammation and coagulation are a hallmark of COVID-19 severity. Extracellular vesicles (EVs) are intercellular transmitters involved in inflammatory conditions, which are capable of triggering prothrombotic mechanisms. Since the release of EVs is potentially associated with COVID-19-induced coagulopathy, the aim of this study was to evaluate changes in inflammation- and hypercoagulability-related EVs during the first month after symptom onset and to determine whether they are associated with disease severity. Blood samples of patients with mild or severe forms of the disease were collected on three occasions: in the second, third and fourth weeks after symptom onset for the quantification by flow cytometry of CD41A (platelet glycoprotein IIb/IIIa), CD162 (PSGL-1), CD31 (PECAM-1) and CD142 cells (tissue factor). Analysis of variance (ANOVA) with repeated measures, Kruskal-Wallis and correlation tests were used. Eighty-five patients were enrolled, 71% of whom had mild disease. Seventeen uninfected individuals served as controls. Compared to controls, both mild and severe COVID-19 were associated with higher EV-CD31+, EV-CD41+ and EV-CD142+ levels. All EV levels were higher in severe than in mild COVID-19 only after the third week from symptom onset, as opposed to C-reactive protein and D-dimer levels, which were higher in severe than in mild COVID-19 earlier during disease progression. EV levels were also associated with C-reactive protein and D-dimer levels only after the third week of symptoms. In conclusion, EVs expressing CD41A, CD31, TF, and CD162 appear as late markers of COVID-19 severity. This finding may contribute to the understanding of the pathogenesis of acute and possibly long COVID-19.

Louisiana experienced high morbidity and mortality from COVID-19. To assess possible explanatory factors, we conducted a cohort study (ClinSeqSer) of patients hospitalized with COVID-19 in New Orleans during August 2020-September 2021. Following enrollment, we reviewed medical charts, and performed SARS-CoV-2 RT-PCR testing on nasal and saliva specimens. We used multivariable logistic regression to assess associations between patient characteristics and severe illness, defined as ≥ 6 L/min oxygen or intubation. Among 456 patients, median age was 56 years, 277 (60.5%) were Black non-Hispanic, 436 (95.2%) had underlying health conditions, and 358 were unvaccinated (92.0% of 389 verified). Overall, 187 patients (40.1%) had severe illness; 60 (13.1%) died during admission. In multivariable models, severe illness was associated with age ≥ 65 years (OR 2.08, 95% CI 1.22-3.56), hospitalization > 5 days after illness onset (OR 1.49, 95% CI 1.01-2.21), and SARS CoV-2 cycle threshold (Ct) result of < 32 in saliva (OR 4.79, 95% CI 1.22-18.77). Among patients who were predominantly Black non-Hispanic, unvaccinated and with underlying health conditions, approximately 1 in 3 patients had severe COVID-19. Older age and delayed time to admission might have contributed to high case-severity. An association between case-severity and low Ct value in saliva warrants further investigation.

We completed a retrospective review of data collected by the JH-CROWN consortium based on ICD10 codes for a hospitalized cohort. The severity and prevalence of COVID-19 and development of PASC within heritable connective tissue diseases were unknown; however, clinical observation suggested a thorough examination was necessary. We compared rates of disease severity, death, and PASC in connective tissue diseases versus the entire cohort as well as in diabetes and hypertension to determine if connective tissue disease was a risk factor. Of the 15,676 patients in the database, 63 (0.40%) had a connective tissue disease, which is elevated relative to the distribution in the population, suggesting a higher risk of severe disease. Within these 63 patients, 9.52% developed PASC compared to 2.54% in the entire cohort (p < 0.005). Elucidation of populations at high risk for severe disease and development of PASC is integral to improving treatment approaches. Further, no other study to date has examined the risk in those with connective tissue diseases and these data support a need for enhanced awareness among physicians, patients, and the community.

Timely hospital presentation and treatment are critical for recovery from coronavirus disease (COVID-19). However, the relationship between symptom onset-to-door time and key clinical outcomes, such as inpatient mortality, has been poorly understood due to the difficulty of retrospectively measuring symptom onset in observational data. This study examines the association between patient-reported symptom onset-to-door time (ODT) and mortality among patients hospitalized and treated for COVID-19 disease.

We conducted a retrospective cohort study of emergency department (ED) encounters of patients with COVID-19 disease who were hospitalized and received remdesivir and/or dexamethasone between March 1, 2020, and March 1, 2022. The exposure was patient-reported ODT in days. The outcome of interest was inpatient mortality, including referral to hospice care. We used multivariable logistic regression to examine the association between ODT and mortality while adjusting for patient characteristics, hospital sites, and seasonality. We tested whether severe illness on hospital presentation modified the association between ODT and mortality. Severe illness was defined by Emergency Severity Index triage level 1 or 2 and hypoxia (SpO2 < 94%).

Of the 3451 ED hospitalizations included, 439 (12.7%) resulted in mortality, and 1693 (49.1%) involved patients with severe illness on hospital presentation. Greater ODT was significantly associated with lower odds of inpatient mortality (adjusted odds ratio (AOR) = 0.96, 95% CI = 0.93-1.00, P = 0.023). There was a statistically significant interaction between ODT and severe illness at hospital arrival on mortality, suggesting the negative association between ODT and mortality specifically pertained to patients who were not severely ill upon ED presentation (AOR = 0.93, 95% CI = 0.87-1.00, P = 0.035). The adjusted probability of mortality was significantly lower for non-severely ill, hospitalized patients who presented on days 8-14 (5.2%-3.3%) versus days 0-3 (9.4%-7.5%) after symptom onset.

More days between symptom onset and hospital arrival were associated with lower mortality among hospitalized patients treated for COVID-19 disease, particularly if they did not have severe illness at ED presentation. However, onset-to-door time was not associated with mortality among hospitalized patients with severe illness at ED presentation. Collectively, these results suggest that non-severely ill COVID-19 patients who require hospitalization are less likely to decompensate with each passing day without severe illness. These findings may continue to guide clinical care delivery for hospitalized COVID-19 patients.

There is an argument whether the delayed intubation aggravate the respiratory failure in Acute respiratory distress syndrome (ARDS) patients with coronavirus disease 2019 (COVID-19). We aimed to investigate the effect of high-flow nasal cannula (HFNC) failure before mechanical ventilation on clinical outcomes in mechanically ventilated patients with COVID-19.

This retrospective cohort study included mechanically ventilated patients who were diagnosed with COVID-19 and admitted to the intensive care unit (ICU) between February 2020 and December 2021 at Asan Medical Center. The patients were divided into HFNC failure (HFNC-F) and mechanical ventilation (MV) groups according to the use of HFNC before MV. The primary outcome of this study was to compare the worst values of ventilator parameters from day 1 to day 3 after mechanical ventilation between the two groups.

Overall, 158 mechanically ventilated patients with COVID-19 were included in this study: 107 patients (67.7%) in the HFNC-F group and 51 (32.3%) in the MV group. The two groups had similar profiles of ventilator parameter from day 1 to day 3 after mechanical ventilation, except of dynamic compliance on day 3 (28.38 mL/cmH2O in MV vs. 30.67 mL/H2O in HFNC-F, p = 0.032). In addition, the HFNC-F group (5.6%) had a lower rate of ECMO at 28 days than the MV group (17.6%), even after adjustment (adjusted hazard ratio, 0.30; 95% confidence interval, 0.11-0.83; p = 0.045).

Among mechanically ventilated COVID-19 patients, HFNC failure before mechanical ventilation was not associated with deterioration of respiratory failure.

Several scores aimed at predicting COVID-19 progression have been proposed. As the variables vaccination and early SARS-CoV-2 treatment were systematically excluded from the prognostic scores, the present study's objective was to develop a new model adapted to the current epidemiological scenario.

We included all patients evaluated by the Infectious Disease Unit in Sassari, with SARS-CoV-2 infection and without signs of respiratory failure at the first evaluation (P/F > 300). Disease progression was defined by the prescription of supplemental oxygen. In addition, variables related to demographics, vaccines, comorbidities, symptoms, CT scans, blood tests, and therapies were collected. Multivariate logistic regression modelling was performed to determine factors associated with progression; any variable with significant univariate test or clinical relevance was selected as a candidate for multivariate analysis. Hosmer-Lemeshow (HL) goodness of fit statistic was calculated. Odds ratio values were used to derive an integer score for developing an easy-to-use progression risk score. The discrimination performance of the risk index was determined using the AUC, and the best cut-off point, according to the Youden index, sensitivity, specificity, predictive value, and likelihood ratio, was chosen.

1145 patients [median (IQR) age 74 (62-83) years; 53.5% males] were enrolled; 336 (29.3%) had disease progression. Patients with a clinical progression were older and showed more comorbidities; furthermore, they were less vaccinated and exposed to preventive therapy. In the multivariate logistic regression analysis, age ≥ 60 years, COPD, dementia, haematological tumours, heart failure, exposure to no or one vaccine dose, fever, dyspnoea, GGO, consolidation, ferritin, De Ritis ≥ 1.2, LDH, and no exposure to early anti-SARS-CoV-2 treatment were associated with disease progression. The final risk score ranged from 0 to 45. The ROC curve analysis showed an AUC of 0.92 (95% CI 0.90-0.93) with a 93.7% specificity and 72.9% sensitivity. Low risk was defined when the cut-off value was less than 23. Three risk levels were identified: low (0-23 points), medium (24-35), and high (≥ 36).

The proportion of patients with progression increases with high scores: the assessment of the risk could be helpful for clinicians to plan appropriate therapeutic strategies.

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors-age and the patient's state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20.

Since 2003, we have seen the emergence of novel viruses, such as SARS-CoV-1, MERS, ZIKA, swine flu virus H1N1, Marburg, Monkeypox, Ebola, and SARS-CoV-2, but none of them gained pandemic proportions similar to SARS-CoV-2. This could be attributed to unique viral traits, allowing its rapid global dissemination following its emergence in October 2019 in Wuhan, China, which appears to be primarily driven by the emergence of highly transmissible and virulent variants that also associate, in some cases, with severe disease and considerable mortality caused by fatal pneumonia, acute respiratory distress syndrome (ARDS) in infected individuals. Mechanistically, several factors are involved in viral pathogenesis, and epigenetic alterations take the front seat in host-virus interactions. The molecular basis of all viral infections, including SARS-CoV-2, tightly hinges on the transitory silencing of the host gene machinery via epigenetic modulation. SARS-CoV-2 also hijacks and subdues the host gene machinery, leading to epigenetic modulation of the critical host elements responsible for antiviral immunity. Epigenomics is a powerful, unexplored avenue that can provide a profound understanding of virus-host interactions and lead to the development of epigenome-based therapies and vaccines to counter viruses. This review discusses current developments in SARS-CoV-2 variation and its role in epigenetic modulation in infected hosts. This review provides an overview, especially in the context of emerging viral strains, their recombinants, and their possible roles in the epigenetic exploitation of host defense and viral pathogenesis. It provides insights into host-virus interactions at the molecular, genomic, and immunological levels and sheds light on the future of epigenomics-based therapies for SARS-CoV-2 infection.

COVID-19 has resulted in over 645 million hospitalization and 7 million deaths globally. However, many questions still remain about clinical complications in COVID-19 and if these complications changed with different circulating SARS-CoV-2 strains. We analyzed a 2.5-year retrospective cohort of 47,063 encounters for 21,312 acute care patients at five Central Texas hospitals and define distinct trajectory groups (TGs) with latent class mixed modeling, based on the World Health Organization COVID-19 Ordinal Scale. Using this TG framework, we evaluated the association of demographics, diagnoses, vitals, labs, imaging, consultations, and medications with COVID-19 severity and broad clinical outcomes. Patients within 6 distinct TGs differed in manifestations of multi-organ disease and multiple clinical factors. The proportion of mild patients increased over time, particularly during Omicron waves. Age separated mild and fatal patients, though did not distinguish patients with severe versus critical disease. Male and Hispanic/Latino demographics were associated with more severe/critical TGs. More severe patients had a higher rate of neuropsychiatric diagnoses, consultations, and brain imaging, which did not change significantly in severe patients across SARS-CoV-2 variant waves. More severely affected patients also demonstrated an immunological signature of high neutrophils and immature granulocytes, and low lymphocytes and monocytes. Interestingly, low albumin was one of the best lab predictors of COVID-19 severity in association with higher malnutrition in severe/critical patients, raising concern of nutritional insufficiency influencing COVID-19 outcomes. Despite this, only a small fraction of severe/critical patients had nutritional labs checked (pre-albumin, thiamine, Vitamin D, B vitamins) or received targeted interventions to address nutritional deficiencies such as vitamin replacement. Our findings underscore the significant link between COVID-19 severity, neuropsychiatric complications, and nutritional insufficiency as key risk factors of COVID-19 outcomes and raise the question of the need for more widespread early assessment of patients' neurological, psychiatric, and nutritional status in acute care settings to help identify those at risk of severe disease outcomes.

We investigated different computed tomography (CT) features between Omicron-variant and original-strain SARS-CoV‑2 pneumonia to facilitate the clinical management.

Medical records were retrospectively reviewed to select patients with original-strain SARS-CoV‑2 pneumonia from February 22 to April 22, 2020, or Omicron-variant SARS-CoV‑2 pneumonia from March 26 to May 31, 2022. Data on the demographics, comorbidities, symptoms, clinical types, and CT features were compared between the two groups.

There were 62 and 78 patients with original-strain or Omicron-variant SARS-CoV‑2 pneumonia, respectively. There were no differences between the two groups in terms of age, sex, clinical types, symptoms, and comorbidities. The main CT features differed between the two groups (p = 0.003). There were 37 (59.7%) and 20 (25.6%) patients with ground-glass opacities (GGO) in the original-strain and Omicron-variant pneumonia, respectively. A consolidation pattern was more frequently observed in the Omicron-variant than original-strain pneumonia (62.8% vs. 24.2%). There was no difference in crazy-paving pattern between the original-strain and Omicron-variant pneumonia (16.1% vs. 11.6%). Pleural effusion was observed more often in Omicron-variant pneumonia, while subpleural lesions were more common in the original-strain pneumonia. The CT score in the Omicron-variant group was higher than that in the original-strain group for critical-type (17.00, 16.00-18.00 vs. 16.00, 14.00-17.00, p = 0.031) and for severe-type (13.00, 12.00-14.00 vs 12.00, 10.75-13.00, p = 0.027) pneumonia.

The main CT finding of the Omicron-variant SARS-CoV‑2 pneumonia included consolidations and pleural effusion. By contrast, CT findings of original-strain SARS-CoV‑2 pneumonia showed frequent GGO and subpleural lesions, but without pleural effusion. The CT scores were also higher in the critical and severe types of Omicron-variant than original-strain pneumonia.

The elderly admitted to nursing homes have especially suffered the havoc of the COVID-19 pandemic since most of them are not prepared to face such health problems.

An innovative coordinated on-site medicalization program (MP) in response to a sizeable COVID-19 outbreak in three consecutive waves was deployed, sharing coordination and resources among primary care, the referral hospital, and the eleven residences. The objectives were providing the best possible medical care to residents in their environment, avoiding dehumanization and loneliness of hospital admission, and reducing the saturation of hospitals and the risk of spreading the infection. The main outcomes were a composite endpoint of survival or optimal palliative care (SOPC), survival, and referral to the hospital.

587 of 1199 (49%) residents were infected, of whom 123 (21%) died. Patients diagnosed before the start of the MP presented SOPC, survival, and referrals to the hospital of 83%, 74%, and 22.4%, opposite to 96%, 84%, and 10.6% of patients diagnosed while the MP was set up. The SOPC was independently associated with an MP (OR 3.4 [1.6-7.2]).

During the COVID-19 outbreak, a coordinated MP successfully obtained a better rate of SOPC while simultaneously reducing the need for hospital admissions, combining optimal medical management with a more compassionate and humanistic approach in older people.

Pandemic of COVID-19 hit China at the end of 2022. According to China Center for Disease Control and Prevention, Omicron BA.5.2 and BF.7 were the main circulating variants. Chinese people had a high COVID-19 vaccination rate, and the most widely used vaccines were CoronaVac (Sinovac) and BBIBP-CorV (Sinopharm). An online questionnaire was distributed to survey the vaccination history and infection information of China mainland residents during this pandemic. A total of 4250 subjects were included for propensity score matching, 566 unvaccinated subjects and 1072 vaccinated subjects were finally included to analyze the effects of the two vaccines on BA.5.2 and BF.7. The SARS-CoV-2 infection rate was 84.5% in the vaccinated group and 82.3% in the unvaccinated group (p = 0.255). Vaccinated subjects had significantly higher rates of COVID-19-related symptoms, including fever, cough, nasal obstruction, runny nose, and sore throat. However, vaccinated people had lower risk of pneumonia (odds ratio [OR]: 0.467, 95% confidence interval [CI]: 0.286-0.762) and hospitalization (OR: 0.290, 95% CI: 0.097-0.870) due to COVID-19. In general, the current study did not observe the protective effect of CoronaVac and BBIBP CorV against BA.5.2 and BF.7 infection. However, these vaccines can still reduce the risk of adverse outcomes such as pneumonia and hospitalization.

COVID-19 is responsible for high mortality, but robust machine learning-based predictors of mortality are lacking. To generate a model for predicting mortality in patients hospitalized with COVID-19 using Gradient Boosting Decision Trees (GBDT). The Spanish SEMI-COVID-19 registry includes 24,514 pseudo-anonymized cases of patients hospitalized with COVID-19 from 1 February 2020 to 5 December 2021. This registry was used as a GBDT machine learning model, employing the CatBoost and BorutaShap classifier to select the most relevant indicators and generate a mortality prediction model by risk level, ranging from 0 to 1. The model was validated by separating patients according to admission date, using the period 1 February to 31 December 2020 (first and second waves, pre-vaccination period) for training, and 1 January to 30 November 2021 (vaccination period) for the test group. An ensemble of ten models with different random seeds was constructed, separating 80% of the patients for training and 20% from the end of the training period for cross-validation. The area under the receiver operating characteristics curve (AUC) was used as a performance metric. Clinical and laboratory data from 23,983 patients were analyzed. CatBoost mortality prediction models achieved an AUC performance of 84.76 (standard deviation 0.45) for patients in the test group (potentially vaccinated patients not included in model training) using 16 features. The performance of the 16-parameter GBDT model for predicting COVID-19 hospital mortality, although requiring a relatively large number of predictors, shows a high predictive capacity.

Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19.

Prospective cohort study.

A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers.

Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization.

MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days.

Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index.

The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively.

No control group of hospitalized patients without COVID-19.

We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes.

Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes.

Mortality from coronavirus disease 2019 (COVID-19) pandemic has left footprints across all ages and socio-economic strata. The deaths because of COVID-19 are usually multi-factorial. The study aimed to assess the health system factors related to COVID-19-related deaths.

A hospital-based retrospective study was conducted at a tertiary care hospital of eastern India. A total of 272 COVID-19 deaths that occurred between April and November 2020 were investigated. Data were extracted from Medical Record Department, and telephonic interviews were conducted to assess the different delays related to death. Data were analysed using Statistical Package for Social Sciences. Travel time, travel distance, delay in testing, and delay in receiving quality care were presented as median with inter-quartile range.

Complete information could be collected from 243 COVID deaths of the 272 deaths (89.3%). The duration of hospital stay was 1-7 days for 42% of the deceased. The median travel time was 120 min, and the median distance travelled was 60 km. The median time to receive first attention of health care workers was 10 minutes. There was hardly any delay in reporting of test results, whereas the median time from symptoms to test and the median time from symptoms to admission were 4 days each.

Health system factors related to death of COVID-19 need to be addressed to avoid the avoidable deaths during the pandemic situation. The resilience of the health system can be helpful in reducing death toll in a low-resource country like India.

Background and aims Being metabolically unhealthy (MU) is defined as having either hypertension, hyperlipidemia, type 2 diabetes mellitus/pre-diabetes, or fatty liver disease. We aimed to determine if MU was associated with severe COVID-19 pneumonia (severe disease). Methods We performed a single-center retrospective study between March 2020 and August 2021 for patients with overweight or obesity hospitalized with COVID-19 pneumonia. Logistic regression analysis was utilized to derive a risk score for severe disease. The accuracy of the model was assessed using the area under the receiver operating characteristic curve (AUROCC) and bootstrap resampling. Results A total of 334 of 450 patients hospitalized with COVID-19 pneumonia (74.2%) were MU. Patients who were MU had higher in-hospital mortality (10.5% vs. 2.6%) and longer length of hospitalization (median 6 vs. 4 days). MU was not associated with severe disease, p=0.311. On multivariable analysis, older age, male sex, and Asian race were associated with severe disease. Not being vaccinated was associated with doubled odds of severe disease. The AUROCC of the final model was 0.66 (95% CI: 0.60 to 0.71). The risk score at the lowest quintile had a 33.1% to 65.5% predicted risk and a 58.7% observed risk of severe disease, whereas, at the highest quintile, there was an 85.7% to 97.7% predicted risk and an 89.7% observed risk of severe disease. Conclusion Being MU was not a predictor of severe disease, even though mortality was higher despite having higher rates of vaccination. This risk score may help to predict severe disease in hospitalized patients with obesity or overweight. External validation is recommended.

Prior observation has shown differences in COVID-19 hospitalization risk between SARS-CoV-2 variants, but limited information describes hospitalization outcomes.

Inpatients with COVID-19 at 5 hospitals in the eastern United States were included if they had hypoxia, tachypnea, tachycardia, or fever, and SARS-CoV-2 variant data, determined from whole-genome sequencing or local surveillance inference. Analyses were stratified by history of SARS-CoV-2 vaccination or infection. The average effect of SARS-CoV-2 variant on 28-day risk of severe disease, defined by advanced respiratory support needs, or death was evaluated using models weighted on propensity scores derived from baseline clinical features.

Severe disease or death within 28 days occurred for 977 (29%) of 3369 unvaccinated patients and 269 (22%) of 1230 patients with history of vaccination or prior SARS-CoV-2 infection. Among unvaccinated patients, the relative risk of severe disease or death for Delta variant compared with ancestral lineages was 1.30 (95% confidence interval [CI]: 1.11-1.49). Compared with Delta, the risk for Omicron patients was .72 (95% CI: .59-.88) and compared with ancestral lineages was .94 (.78-1.1). Among Omicron and Delta infections, patients with history of vaccination or prior SARS-CoV-2 infection had half the risk of severe disease or death (adjusted hazard ratio: .40; 95% CI: .30-.54), but no significant outcome difference by variant.

Although risk of severe disease or death for unvaccinated inpatients with Omicron was lower than with Delta, it was similar to ancestral lineages. Severe outcomes were less common in vaccinated inpatients, with no difference between Delta and Omicron infections.

Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort.

To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients.

A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021.

Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation.

The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated.

We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604).

Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.