COVID-19 is no longer a global health emergency, but it remains challenging to predict its prognosis.

To develop and validate an instrument to predict COVID-19 progression for critically ill hospitalized patients in a Brazilian population.

Observational study with retrospective follow-up. Participants were consecutively enrolled for treatment in non-critical units between January 1, 2021, to February 28, 2022. They were included if they were adults, with a positive RT-PCR result, history of exposure, or clinical or radiological image findings compatible with COVID-19. The outcome was characterized as either transfer to critical care or death. Predictors such as demographic, clinical, comorbidities, laboratory, and imaging data were collected at hospitalization. A logistic model with lasso or elastic net regularization, a random forest classification model, and a random forest regression model were developed and validated to estimate the risk of disease progression.

Out of 301 individuals, the outcome was 41.8 %. The majority of the patients in the study lacked a COVID-19 vaccination. Diabetes mellitus and systemic arterial hypertension were the most common comorbidities. After model development and cross-validation, the Random Forest regression was considered the best approach, and the following eight predictors were retained: D-dimer, Urea, Charlson comorbidity index, pulse oximetry, respiratory frequency, Lactic Dehydrogenase, RDW, and Radiologic RALE score. The model's bias-corrected intercept and slope were - 0.0004 and 1.079 respectively, the average prediction error was 0.028. The ROC AUC curve was 0.795, and the variance explained was 0.289.

The prognostic model was considered good enough to be recommended for clinical use in patients during hospitalization (https://pedrobrasil.shinyapps.io/INDWELL/). The clinical benefit and the performance in different scenarios are yet to be known.

The COVID-19 pandemic disrupted healthcare systems and possibly impacted the management of heart failure (HF). This study examined the impact of the pandemic on HF hospitalization activities, outcomes, and costs in Victoria, Australia.

Data on HF hospitalizations were acquired from the Victorian Admitted Episodes Dataset. All consecutive patients hospitalized for HF in both public and private hospitals in Victoria between February 2019 and March 2021 were extracted using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification. Data were analysed using descriptive analysis and interrupted time series analysis. A total of 85 564 completed admissions were identified, of which 45 080 were hospitalized in the pre-COVID-19 period and 40 484 were hospitalized in the COVID-19 impacted period. A higher average cost per completed admission in the COVID-19 impacted period was observed, while average length of stay (LOS) was not different between the two periods. It was revealed that monthly total LOS and hospitalization activity cost across all HF admissions dropped at the beginning of the pandemic and continued to decrease until the end of the observation period. However, these changes were not statistically significant.

The impacts of COVID-19 on HF hospitalization activities and associated outcomes at the beginning of the pandemic appeared relatively small and were not sustained. Further studies using other data (i.e. linkage data) are required to understand if, or how, the pandemic impacted on HF management in Australia, especially in the long COVID-19 era.

As new SARS-CoV-2 variants emerge and as treatment of COVID-19 ARDS remains exclusively supportive, there is an unmet need to better characterize its different phenotypes to tailor personalized treatments. Clinical, biological, spirometric and CT data hardly allow deciphering of Heavy (H), Intermediate (I) and Light (L) phenotypes of COVID-19 ARDS and the implementation of tailored specific strategies (prone positioning, PEEP settings, recruitment maneuvers). We hypothesized that the ratio of two pivotal COVID-19 biomarkers (interleukin 6 [IL-6] and Krebs von den Lungen 6 [KL-6], related to inflammation and pneumocyte repair, respectively) would provide a biologic insight into the disease timeline allowing 1) to differentiate H, I and L phenotypes, 2) to predict outcome and 3) to reflect some of CT findings.

This was a retrospective analysis of prospectively acquired data (COVID HUS cohort). Inclusion concerned any patient with severe COVID-19 pneumonia admitted to two intensive care units between March 1st and May 1st, 2020, in a high-density cluster of the first epidemic wave (Strasbourg University Hospital, France). Demographic, clinical, biological (standard, IL-6 [new generation ELISA], KL-6 [CLEIA technique]), spirometric (driving pressure, respiratory system compliance) and CT data were collected longitudinally. CT analysis included semi-automatic and automatic lung measurements and allowed segmentation of lung volumes into 4 (poorly aerated, non-aerated, overinflated and normally aerated) and 3 (ground-glass, restricted normally aerated, and overinflated) zones, respectively. The primary outcome was to challenge the IL-6/KL-6 ratio capacity to decipher the three COVID-19 ARDS phenotypes (H, I and L) defined on clinical, spirometric and radiologic grounds. Secondary outcomes were the analysis of the prognostic value of the IL-6/KL-6 ratio and its correlates with CT-acquired data. Multivariate analysis was based on principal component analysis. One hundred and forty-eight ventilated COVID-19 ICU patients from the COVID HUS cohort were assessed for eligibility and 77 were included in the full analysis. Most were male, all were under invasive mechanical ventilation and vasopressor therapy and displayed high severity scores (SAPSII: 48 [42-56]; SOFA: 8 [7-10]). The L, I and H COVID ARDS phenotypes were identified in 11, 15 and 48 patients, respectively. In three patients, the phenotype could not be defined precisely. Thirty patients (39%) died in the ICU and the number of ventilator-free days was 2 [0-2] days. The IL-6/KL-6 ratio was not significantly different between the L, I and H phenotypes and evolved according to similar patterns over time. Surviving and deceased patients displayed an inverse kinetic of KL-6. IL-6 and the IL-6/KL-6 ratio were linearly associated with ground-glass volume on semi-automatic and automatic CT lung measurements.

In our population of severe ventilated COVID ARDS patients, the IL-6/KL-6 ratio was not clue to differentiate the H, I and L phenotypes and tailor a personalized ventilatory approach. There was an interesting correlation between IL-6/KL-6 ratio and ground-glass volume as determined by automated lung CT analysis. Such correlation deserves more in-depth pathophysiological study, at best gathered from a prospective cohort with a larger sample size and histological analysis.

COVID HUS Trial registration number: NCT04405726.

The global coronavirus disease 2019 (COVID-19) pandemic has placed patients with end-stage kidney disease (ESKD) at heightened risk owing to their vulnerability to infections. Our study focused on patients with ESKD, examining COVID-19 incidence, hospitalization, and mortality in relation to their renal replacement therapy (RRT) type and identifying factors influencing COVID-19 hospitalization.

We conducted a retrospective cohort study using health insurance claims data from the Health Insurance Review and Assessment Service for patients with ESKD between July 2017 and June 2022. COVID-19 data for the general population were sourced from the Korea Disease Control and Prevention Agency.

Patients undergoing hemodialysis (HD) constituted 90.7% of the cohort, followed by kidney transplantation (KT) recipients and peritoneal dialysis (PD). After adjusting for every 10,000 individuals, KT recipients exhibited the highest COVID-19 incidence, followed by those undergoing HD and PD, whereas the general population showed a higher infection rate of 43.64. Patients undergoing HD had the highest hospitalization rates, followed by KT recipients and those undergoing PD. The mortality rate per 10,000 individuals was highest in HD, followed by PD, the general population, and KT. Multivariate analysis indicated that age, RRT duration, residence in a nursing hospital, and comorbidities were associated with COVID-19 hospitalization.

Among RRT modalities, KT recipients displayed the highest COVID-19 incidence, whereas those undergoing HD exhibited the highest hospitalization and mortality rates. This study contributes to our understanding of infectious diseases in patients on RRT and aids in preparedness for future infectious disease outbreaks.

To investigate the barriers experienced by intensive care nurses and registered nurses and to provide optimal nursing for adult patients with a temporary tracheostomy in intensive care and general wards.

Tracheostomy is widely used in intensive care units, around 20% of intensive care unit patients undergo tracheostomy insertions and expect high quality of care. Caring for patients with a tracheostomy is complex and challenging task. An investigation of barriers to care for adult patients with a temporary tracheostomy in a hospital setting is essential to ensure that these patients receive the highest quality of care and to identify areas for improvement.

This paper applied secondary analysis to data from two qualitative studies, including narrative interviews and maximum variation sampling.

Secondary analysis of primary qualitative datasets is appropriate when the analysis extends rather than exceeds the primary. The analysis was based on interview data collected from six intensive care nurses and six registered nurses from two university teaching hospitals in Norway. The interviews were audio-recorded and transcribed. The data was analysed using the qualitative analysis suggested by Graneheim and Lundman. This study adhered to the consolidated criteria for reporting in a qualitative research (COREQ) checklist.

Four main themes were identified as barriers to care for adult patients with a temporary tracheostomy in the hospital: encountering ambivalence, inadequate staffing levels, lack of patient continuity of care and lack of systematic follow-up.

Understanding barriers to care is crucial for hospitals and healthcare organisations to develop targeted interventions and educational programs to address these barriers and improve the care provided to adult patients with tracheostomies in hospital settings.

We assessed the impact of COVID-19, and the prevalence and factors associated with a history of COVID-19 infection, long COVID and incomplete COVID-19 vaccine uptake among people with HIV.

Positive Voices 2022 is a questionnaire study of people accessing HIV care in the United Kingdom (March 2022-April 2023). Logistic regression assessed factors associated with a history of COVID-19 (previous positive test), long COVID among those with a history of COVID-19 (ongoing symptoms, with COVID-19 onset >3 months previously) and incomplete COVID-19 vaccine uptake (less than three doses of vaccine), adjusted for: age; gender; ethnicity; and year of HIV diagnosis.

In all, 4188 participants were included. Commonly reported negative impacts of the pandemic were on social contact (44% of participants), mental health (30%), healthcare access (26%) and financial security (25%). Overall, 2068 of 4188 (49.4%) participants had a history of COVID-19. Of these, 10.8% met criteria for long COVID, associated with female gender, unemployment, financial hardship, earlier HIV diagnosis date, diabetes diagnosis, asthma/chronic obstructive pulmonary disease diagnosis, obesity and symptoms of depression and anxiety. Overall, 95.8% reported having at least one vaccine dose, but 649 (15.7%) participants had incomplete vaccine uptake, associated with younger age, female gender, Black African ethnicity, lower education, financial hardship, unemployment, multioccupancy household, more recent HIV diagnosis, detectable HIV viral load and symptoms of depression and anxiety.

About half of participants had a history of COVID-19, of whom 11% had persistent symptoms (long COVID). COVID-19 vaccine uptake was high, but incomplete uptake was apparent for 16% of participants and was more common among women, younger people, Black African individuals and those with socio-economic disadvantage.

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) binds to Angiotensin Converting Enzyme - 2 (ACE2) receptor targeting various organs including liver. Liver injury is a common feature of SARS-CoV-2 acute infection. A few studies have also described chronic hepatic alterations in patients with previous COVID-19. We hypothesize that steatosis seen in COVID-19 patients reflects their metabolic profile and is not caused by persistent inflammation sustained by SARS-CoV-2.

We conducted a prospective study to evaluate long-term changes in liver function and structure in patients hospitalized for COVID-19. Patients without a prior known hepatic disease with mild to moderate COVID-19 were enrolled during hospitalization and reevaluated during a follow-up visit at a medium 16 months. Complete blood panels with metabolic profile, BMI, alcohol consumption and physical activity were compared between baseline and follow-up. Specific ultrasound scans were obtained during hospital stay and at follow-up to quantify steatosis using Steatoscore2.0.

Among 55 eligible patients, 33 were included in the analysis and only 3 (9 %) had a new diagnosis of steatosis at follow-up. Steatoscore2.0 did not change significantly from baseline to follow-up (1.7 vs 1.73, p = 0.348), while changes occurred in body mass index and physical activity estimated by IPAQ questionnaire (26.3 vs 26.6 kg/m2, p = 0.005; 540 vs. 480, p = 0.015, respectively). There was a statistically significant increase in total cholesterol (144.5 vs 187.0 mg/dl, p = 0.003) and low-density lipoprotein-cholesterol (73.8 vs 113.9 mg/dl, p = 0.003). Inflammatory markers normalized at follow-up, including C-reactive protein (41.1 vs. 0.8 mg/L, p < 0.001), and ferritin (410.0 vs. 91.0 ng/dl, p < 0.001). Four patients had a 3-time rise in liver transaminase levels at baseline, and this was not confirmed at follow-up. Change in Steatoscore2.0 correlated significantly with Triglyceride-glucose index as a surrogate of insulin resistance.

In our study, long term functional and structural changes were not observed in patients with previous SARS-CoV-2 infection. There was a significant deterioration of metabolic profile post COVID-19.

A detailed analysis was carried out on the impact of cardiovascular disease on the risk of death of patients hospitalized at a temporary hospital in Gdańsk during the third and fifth waves of the COVID-19 pandemic (in 2021 and 2022, respectively). Background/Objectives: The documentation of 1244 patients was analyzed, of which 701 were hospitalized in 2021 (the Delta variant) and 543 in 2022 (the Omicron variant). The aim of this study was to assess the risk of death of patients with COVID-19 depending on the co-existence of cardiovascular diseases. Methods: A model of logistic regression was used to identify the impact of the patients' age, the coexistence of cardiovascular disease, and the length of hospitalization on the risk of death. Results: In 2021, patients were younger (median of 66 years) than in 2022 (median of 74 years), the length of hospitalization was shorter in 2022 (9 days) than in 2021 (11 days), and there was a higher proportion of patients with cardiovascular and respiratory diseases and a medical history of cancer in 2022. The odds of death were also observed to be higher in older patients with cardiovascular disease, particularly those under 73 years of age. In older patients (over 73 years), the odds were paradoxically reduced. Conclusions: The age of the patient, cardiovascular disease, and duration of hospitalization affect the risk of death. The Delta variant (2021) was more virulent than Omicron (2022). Cardiovascular disease significantly increases the risk of death in patients with COVID-19. The comprehensive diagnosis and treatment of patients with these conditions may reduce mortality. Further studies are needed on the long-term effects of COVID-19 on the cardiovascular system.

Quantitative studies have shown that people living with obesity experienced deteriorations in mental health and health-related behaviours during the first UK's Coronavirus Disease-2019 (COVID-19) lockdown. However, there is a lack of qualitative research exploring their lived experiences during this period.

Thematic analysis of large-scale free-text survey data was conducted to understand the challenges faced by adults with obesity during the first UK's COVID-19 lockdown.

Among 543 participants, 467 (86%) responded to the free text questions. The majority were female (87.8%), with a mean age of 51.6 (SD 9.9) years. Of these, 65.3% has a body mass index ≥40 kg/m2, and 57.7% were not enroled in weight management services. Five overarching themes and 10 sub-themes were identified with the five key themes being (1) increased fear and anxiety, (2) the impact of obesity being classified as 'high risk', (3) disruption in weight management services, (4) the impact on health-related behaviours, and (5) the adverse impact on mental health. Participants expressed fear of contracting COVID-19 and concerns about weight gain. UK Government messages linking obesity with severe COVID-19 complications exacerbated feelings of shame and stigma. The reduced provision of weight management services caused further health concerns, highlighting the need for digital health technologies for continued support. Participants reported changes in shopping, diet, physical activity, and sleep patterns, leading to deteriorated mental health.

People living with obesity experienced distinct challenges during the first COVID-19 lockdown, affecting their ability to practice and maintain health-related behaviours.

The objective of this study was to examine the impact of diaphragm thickness (DT) on the prognosis of elderly patients infected with COVID-19, particularly with regard to the necessity of intensive care unit (ICU) admission.

Between August 2020 and January 2021, 188 patients aged ≥ 65 years who were admitted to the internal medicine department of our hospital with a diagnosis of COVID-19 infection, were included in this study. The patients' DTs of the patients were measured by a radiologist using computed tomography (CT) scans from the right and left diaphragm dome level. DT was compared with the progression of respiratory distress and the necessity of intensive care. In statistical analysis, p < 0.05 was considered significant.

Right DT was higher in the group of patients with admission to the ICU (p = 0.11). According to multivariate logistic regression analysis, ferritin level (OR = 1; 95% CI = 1-1; p = 0.014), IL-6 level (OR = 1.004; 95% CI = 1-1.007; p = 0.045) and higher right DT (OR = 11.015; 95% CI = 3.739-32.447; p = 0.035) were found to be independent risk factors predicting the ICU admission in COVID-19 patients. There was no significant association with left DT. The predictive value of right DT for ICU requirement in COVID-19 patients was evaluated by ROC analysis. The ROC analysis showed a cut-off value > 1.8, AUC = 0.632, p = 0.009, 95% Cl (0.558-0.701). In correlation analysis, a positive correlation was found between right DT and ICU admission (r = 0.331, p < 0.001).

Our study is the first to evaluate dome-level DT with CT in elderly patients with COVID-19. In the elderly population, higher right DT levels have been observed to enhance the probability of ICU admission. This may be due to the fact that our sample group consists only of elderly people and the effects of COVID-19. We believe that further validation with more comprehensive studies is needed for DT assessment for clinical treatment decisions, particularly in COVID-19 patients. In addition, we think that the proposal for a standardized measurement site and method for DT measurement will be a guide for future studies.

Not applicable.

This review provides a broad overview of lessons learned in the five years since COVID-19 was identified. It is a bimodal disease, starting with an initially virus-driven phase, followed by resolution or ensuing inappropriate immune activation causing severe inflammation that is no longer strictly virus dependent. Humoral immunity is beneficial for preventing or attenuating the early stage, without benefit once the later stage begins. Neutralizing antibodies elicited by natural infection or vaccination are short-lived and highly vulnerable to viral sequence variation. By contrast, cellular immunity, particularly the CD8+ T cell arm, has a role in preventing or attenuating severe disease, is far less susceptible to viral variation, and is longer-lived than antibodies. Finally, an ill-defined phenomenon of prolonged symptoms after acute infection, termed "long COVID," is poorly understood but may involve various immunologic defects that are hyperactivating or immunosuppressive. Remaining issues include needing to better understand the immune dysregulation of severe disease to allow more tailored therapeutic interventions, developing antibody strategies that cope with the viral spike sequence variability, prolonging vaccine efficacy, and unraveling the mechanisms of long COVID to design therapeutic approaches.

The development of frailty at hospital discharge affects the clinical outcomes in severe coronavirus disease 2019 (COVID-19) survivors who had no frailty before hospitalization. We aimed to describe the prevalence of new frailty using the clinical frailty scale (CFS) and evaluate its associated factors in patients with severe COVID-19 without pre-existing frailty before hospitalization.

We performed a secondary analysis of clinical data from a nationwide retrospective cohort collected from 22 hospitals between January 1, 2020 and August 31, 2021. The patients were at least 19 years old and survived until discharge after admission to the intensive care unit (ICU) because of severe COVID-19. Development of new frailty was defined as a CFS score ≥5 at hospital discharge.

Among 669 severe COVID-19 survivors without pre-existing frailty admitted to the ICU, the mean age was 65.2±12.8 years, 62.5% were male, and 50.2% received mechanical ventilation (MV). The mean CFS score at admission was 2.4±0.9, and new frailty developed in 27.8% (186/483). In multivariate analysis, older age, cardiovascular disease, CFS score of 3-4 before hospitalization, increased C-reactive protein level, longer duration of corticosteroid treatment, and use of MV and extracorporeal membrane oxygenation were identified as factors associated with new-onset frailty.

Our study suggests that new frailty is not uncommon and is associated with diverse factors in survivors of severe COVID-19 without pre-existing frailty.

Previous research demonstrated Non-Hispanic Black populations experience higher COVID-19 mortality rates than Non-Hispanic White individuals. Additionally, cancer status is a known risk factor for COVID-19 death. While prior studies investigated comorbidities as exploratory variables in differences in COVID-19 hospitalization, none have explored their role in COVID-19-related deaths. This study aimed to evaluate whether Charlson Comorbidity Index (CCI) and subsequently, individual diseases are potential explanatory variables for this relationship. The analysis focused on Non-Hispanic Black and Non-Hispanic White cancer patients aged 20 or older, diagnosed between 2011 and 2019, who tested positive for COVID-19 from the start of pandemic through June 30, 2021 from Louisiana Tumor Registry. Two separate mediation analyses were conducted. First checked whether overall comorbidity, measured by CCI, could explain the difference in COVID-19 mortality. If so, further checked which individual comorbidities contributed to this difference. The hazard rate for Non-Hispanic Black cancer patients dying from COVID-19 was 6.46 times than that of Non-Hispanic White patients. The CCI accounted for 12.7% of the differences observed in COVID-19 mortality, with renal disease as the top contributor, explaining 4.9%. These findings could help develop interventions to reduce COVID-19 mortality and address the disproportionate impact, especially by managing chronic conditions like renal disease.

In 2021, vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were developed and the Omicron variant emerged. This study compared the characteristics, treatments, and mortality of patients with coronavirus disease 2019 (COVID-19) between 2022 and 2020-2021, using administrative claims data linked including vaccine records in a Japanese city. Patients who underwent COVID-19 antigen or polymerase chain reaction tests and were diagnosed with COVID-19 were identified. Patient characteristics, treatments, and mortality were compared between 2022 and 2020-2021 among those diagnosed with COVID-19. We identified 26,262 patients with COVID-19. The mortality in 2022 was lower than that in 2020-2021 (0.6% vs. 1.7%; P < 0.01). Patients in 2022 were significantly less likely to receive oxygen therapy, high-flow nasal oxygenation, mechanical ventilation, steroids, and tocilizumab than those in 2020-2021. Among the deceased, the proportion of those aged ≥65 years was significantly higher in 2022 than in 2020-2021 (98.4% vs. 88.6%). The logistic regression analysis indicated, older age, male sex, and ≥3 comorbidities were associated with higher mortality, whereas ≥3 vaccinations were associated with lower mortality. Patients with COVID-19 in 2022 were less likely to require respiratory care or succumb to the disease. Older patients were more likely to die in 2022 than in 2020-2021.

The rapid spread of COVID-19 have overwhelmed health systems, especially in the care of chronic disease such as tuberculosis and diabetes. The objective of the study was to analyze the magnitude and relevance of tuberculosis-diabetes and diabetes-COVID-19 comorbidities in spatial risk areas and their factors associated with unfavorable outcomes in the Brazilian population between 2020 and 2022. An ecological study was carried out in Brazilian municipalities. The population was composed by cases of tuberculosis-diabetes and diabetes-COVID-19 comorbidities, registered in the Influenza Epidemiological Surveillance Information System (SIVEP-GRIPE) and in DATASUS from 2020 to 2022. The Scan Statistics technique was used to identify spatial risk clusters. Binary logistic regression was then employed to understand the relationship between outcomes and comorbidities, considering clinical and sociodemographic variables. A total of 24,750 cases of tuberculosis-diabetes comorbidity were identified, which consisted of an incidence of 3.2 cases per 100,000 inhabitants. Risk clusters were identified in the Central-West and North regions. 303,210 cases of diabetes- COVID-19 comorbidity were identified, resulting in an incidence of 0.4 cases per 100,000 inhabitants. São Paulo-SP, Rio de Janeiro-RJ and Belo Horizonte-MG were the municipalities with the highest spatial risk of illness. The analysis of the spatial risk areas revealed distinct patterns in the geographic distribution of comorbidities. Based on the findings, it is concluded that comorbidities between tuberculosis and diabetes, as well as between COVID-19 and diabetes, represent significant challenges for public health in Brazil, deserving attention from health authorities and the scientific community.

This investigation sought to understand the mental health needs of Black/African Americans during the COVID-19 pandemic and explore the church leaders' perspectives and experiences in supporting the mental health of their communities during this time. This exploratory qualitative study uses semi-structured interviews with 21 church leaders from predominantly Black neighborhoods in Brooklyn and Harlem, New York City (NYC), which were most adversely impacted by COVID-19. Neighborhoods were selected based on COVID-19 infection and mortality data from the NYC Department of Health. Transcripts were coded and thematically analyzed using Dedoose software. Four overarching themes and one subtheme emerged: (1) The COVID-19 pandemic amplified underlying community quality of life challenges, creating a "downward cascade" of mental health, (1a) COVID-19 is a source of collective grief and trauma; (2) Faith-based leaders play a key role in providing mental health support to their community; (3) Faith-based leaders seek strategies to reduce mental health stigma, normalize conversations around mental health; and (4) Faith-based leaders often operate in isolation from citywide mental health services, disconnected from existing programs and resources. Leaders anticipate a higher demand for mental health services. On the road to emotional wellness post-pandemic, public awareness of symptoms of mental health issues must be emphasized and available resources utilized to reduce stigma and encourage help-seeking behavior. Addressing these implications requires comprehensive multi-sector community driven efforts that include collaboration with faith-based communities to support community resilience and improve mental healthcare access.

The COVID-19 pandemic disrupted normal mechanisms of health care delivery and facilitated the rapid and widespread implementation of telehealth technology. As a result, the effectiveness of virtual health care visits in diverse populations represents an important consideration. We used lung cancer screening as a prototype to determine whether subsequent adherence differs between virtual and in-person encounters in an urban, safety-net health care system.

We conducted a retrospective analysis of initial low-dose computed tomography (LDCT) ordered for lung cancer screening from March 2020 through February 2023 within Parkland Health, the integrated safety-net provider for Dallas County, TX. We collected data on patient characteristics, visit type, and LDCT completion from the electronic medical record. Associations among these variables were assessed using the chi-square test. We also performed interaction analyses according to visit type.

Initial LDCT orders were placed for a total of 1,887 patients, of whom 43% were female, 45% were Black, and 17% were Hispanic. Among these orders, 343 (18%) were placed during virtual health care visits. From March to August 2020, 79 of 163 (48%) LDCT orders were placed during virtual visits; after that time, 264 of 1,724 (15%) LDCT orders were placed during virtual visits. No patient characteristics were significantly associated with visit type (in-person v virtual) or LDCT completion. Rates of LDCT completion were 95% after in-person visits and 97% after virtual visits (P = .13).

In a safety-net lung cancer screening population, patients were as likely to complete postvisit initial LDCT when ordered in a virtual encounter as in an in-person encounter.

This study aimed to investigate the significant expression of Peptidyl prolyl cis-trans isomerase (PIN1) as a key regulator of COVID-19 cycle. A quantitative real-time polymerase chain reaction (qRT-PCR) measured the expression levels of PIN1 in the serum of mild and severe patients and evaluated its association with clinical parameters. ROC curve analysis was performed to evaluate the expression of PIN1 for the diagnosis of COVID-19 between mild and severe patients. Expression of the PIN1 gene in severe patients (0.89±0.43) was higher than in mild patients (-2.28±0.34), and this difference was statistically significant between the two groups regardless of other factors (P‑value<0.0001). ROC curve analysis showed that high PIN1 levels in the discrimination of severe from mild patients could be useful. PIN1 expression levels were significantly associated with shortness of breath and cough. PIN1 can be considered an effective factor in the intensification of the symptoms of COVID-19.

COVID-19 exhibits complex pathophysiological manifestations, characterized by significant clinical and biological heterogeneity. Identifying phenotypes may enhance our understanding of the disease's diverse trajectories, benefiting clinical practice and trials.

This study included adult patients with COVID-19 from Xinhua Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, between December 15, 2022, and February 15, 2023. The k-prototypes clustering method was employed using 50 clinical variables to identify phenotypes. Machine learning algorithms were then applied to select key classifier variables for phenotype recognition.

A total of 1376 patients met the inclusion criteria. K-prototypes clustering revealed two distinct subphenotypes: Hypo-inflammatory subphenotype (824 [59.9%]) and Hyper-inflammatory subphenotype (552 [40.1%]). Patients in Hypo-inflammatory subphenotype were younger, predominantly female, with low mortality and shorter hospital stays. In contrast, Hyper-inflammatory subphenotype patients were older, predominantly male, exhibiting a hyperinflammatory state with higher mortality and rates of organ dysfunction. The AdaBoost model performed best for subphenotype prediction (Accuracy: 0.975, Precision: 0.968, Recall: 0.976, F1: 0.972, AUROC: 0.975). "CRP", "IL-2R", "D-dimer", "ST2", "BUN", "NT-proBNP", "neutrophil percentage", and "lymphocyte count" were identified as the top-ranked variables in the AdaBoost model.

This analysis identified two phenotypes based on COVID-19 symptoms and comorbidities. These phenotypes can be accurately recognized using machine learning models, with the AdaBoost model being optimal for predicting in-hospital mortality. The variables "CRP", "IL-2R", "D-dimer", "ST2", "BUN", "NT-proBNP", "neutrophil percentage", and "lymphocyte count" play a significant role in the prediction of subphenotypes. Use the identified subphenotypes for risk stratification in clinical practice. Hyper-inflammatory subphenotypes can be closely monitored, and preventive measures such as early admission to the intensive care unit or prophylactic anticoagulation can be taken.

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Identifying individuals hospitalized for SARS-CoV-2 infection at increased risk of death is crucial for clinical decision making. Analyses must consider simultaneously the multitude of biomarkers across several domains and how these biomarker profiles change over time.

This electronic health records-based study included individuals hospitalized at a Massachusetts General Brigham hospital for at least 24 h within 5 days prior and 30 days after diagnosis of COVID-19. K-means clustering was used to identify profiles among 20 eligible biomarkers and proportional hazards models were used to model 30-day mortality at hospitalization and 7 days after hospitalization (i.e., landmark models).

Twelve thousand, nine hundred forty-two individuals were included, among whom 1,198 died within 30 days. Six states were identified, characterized by the following abnormalities: (1) normal/reference, (2) hematologic, (3) inflammatory and hematological, (4) metabolic, (5) kidney, hematologic, and metabolic, and (6) cardio-thrombotic, liver, and metabolic. Risk of death within 30 days was higher in States 3, 4, 5, and 6 (adjusted hazard ratios ranging from 3.6 to 7.8) compared to individuals in State 1 at hospitalization. Landmark model findings were similar.

Distinct sub-phenotypes based on biomarker profiles were identified among patients hospitalized with SARS-CoV-2 infection, and certain phenotypes are associated with greater risk of 30-day mortality.

NCT06170320 (retrospectively registered on December 21, 2023).

Background/Objectives: Reports documenting racial disparities in COVID-19 hospitalization rates from electronic medical record data have used different sample selection methods. Studies including all individuals with a positive COVID-19 test may be vulnerable to misclassification bias if hospitalization status is not captured for all individuals (i.e., if they went to a non-study hospital). A few studies have used only patients who tested positive in the ED and have found different results. In this study, we explore the implications of using different sets of inclusion criteria for analyses that compare COVID-19 hospital admissions by race and ethnicity. Methods: Two separate data sets were created by applying the two different COVID-19 testing inclusion criteria to medical records data from a single academic health system. We used logistic regression to compare the odds of COVID-19 hospitalization across race and ethnicity for each data set and compared our results with previous studies. Results: We found that using all positive COVID-19 tests as the study sample resulted in higher odds of hospitalization for Black and Hispanic patients relative to White patients. In contrast, using only patients who tested positive in the ED resulted in higher odds of hospitalization for White patients. These findings matched the findings of other studies. Conclusions: Patient inclusion criteria should be considered carefully when comparing results from studies of COVID-19 hospitalization.

Early remdesivir administration in high-risk patients with coronavirus disease 2019 (COVID-19) is known to be effective in preventing the progression to severe disease. However, the effect of early remdesivir administration on Omicron variants, which are known to have decreased severity, remains unclear.

This study aimed to analyze the effects of early remdesivir administration during the Omicron wave in hospitalized patients. Electronic medical records of hospitalized patients with confirmed COVID-19 between February 2022 and February 2023 were reviewed. We included patients aged ≥ 18 years who had symptom onset within 7 days and had at least one risk factor for disease progression at the time of diagnosis. We compared the clinical outcomes between the early remdesivir administration group and the group not administered early remdesivir. The primary outcome was all-cause mortality within 28 days and the secondary outcome was the need for oxygen supplementation within 28 days. Multivariable analysis was conducted to assess risk factors for all-cause mortality and the need for oxygen supplementation.

A total of 286 patients were enrolled, including 88 in the early remdesivir administration group and 198 in the control group. Clinical outcomes, including all-cause mortality (3.4% vs. 6.1%, P = 0.556) and need for oxygen supplementation (15.9% vs. 14.6%, P = 0.783) within 28 days, were not significantly different between the two groups. Age (HR, 1.061; 95% CI: 1.002, 1.124; P = 0.043), BMI (HR, 0.849; 95% CI: 0.725, 0.994; P = 0.041), and malignancy (HR, 4.619; 95% CI: 1.618, 13.189; P = 0.004) were identified as independent factors associated with all-cause mortality. Additionally, BMI (OR, 0.908; 95% CI, 0.824, 1.000; P = 0.049) and vaccination with more than three doses (OR, 0.412; 95% CI, 0.202, 0.839; P = 0.015) were independent factors associated with the need for oxygen supply. Early remdesivir administration was not significantly associated with all-cause mortality (HR, 0.393; 95% CI: 0.109, 1.417; P = 0.154) or the need for oxygen supplementation (OR, 0.823; 95% CI: 0.389, 1.740; P = 0.610).

In our study, early remdesivir administration was not associated with preventing progression to severe disease when used as previously indicated during the Omicron wave. Considering the decline in the severity of the Omicron variant and the increased vaccination rate reported in previous studies, further studies are needed to establish new indications for the use of early remdesivir in the Omicron variant.

The COVID-19 pandemic posed unprecedented challenges to healthcare systems worldwide, particularly in managing critically ill patients requiring mechanical ventilation early in the pandemic. Surging patient volumes strained hospital resources and complicated the implementation of standard-of-care intensive care unit (ICU) practices, including sedation management. The objective of this study was to evaluate the impact of an evidence-based ICU sedation bundle during the early COVID-19 pandemic. The bundle was designed by a multi-disciplinary collaborative to reinforce best clinical practices related to ICU sedation. The bundle was implemented prospectively with retrospective analysis of electronic medical record data. The setting was the ICUs of a single-center tertiary hospital. The patients were the ICU patients requiring mechanical ventilation for confirmed COVID-19 between March and June 2020. A learning health collaborative developed a sedation bundle encouraging goal-directed sedation and use of adjunctive strategies to avoid excessive sedative administration. Implementation strategies included structured in-service training, audit and feedback, and continuous improvement. Sedative utilization and clinical outcomes were compared between patients admitted before and after the sedation bundle implementation. Quasi-experimental interrupted time-series analyses of pre and post intervention sedative utilization, hospital length of stay, and number of days free of delirium, coma, or death in 21 days (as a quantitative measure of encephalopathy burden). The analysis used the time duration between start of the COVID-19 wave and ICU admission to identify a "breakpoint" indicating a change in observed trends. A total of 183 patients (age 59.0 ± 15.9 years) were included, with 83 (45%) admitted before the intervention began. Benzodiazepine utilization increased for patients admitted after the bundle implementation, while agents intended to reduce benzodiazepine use showed no greater utilization. No "breakpoint" was identified to suggest the bundle impacted any endpoint measure. However, increasing time between COVID-19 wave start and ICU admission was associated with fewer delirium, coma, and death-free days (β =  - 0.044 [95% CI - 0.085, - 0.003] days/wave day); more days of benzodiazepine infusion (β = 0.056 [95% CI 0.025, 0.088] days/wave day); and a higher maximum benzodiazepine infusion rate (β = 0.079 [95% CI 0.037, 0.120] mg/h/wave day). The evidence-based practice bundle did not significantly alter sedation utilization patterns during the first COVID-19 wave. Sedation practices deteriorated and encephalopathy burden increased over time, highlighting that strategies to reinforce clinical practices may be hindered under conditions of extreme healthcare system strain.

To provide globally representative data on hospitalization and death in recently SARS-CoV-2-positive ambulatory populations.

We enrolled SARS-CoV-2-positive ambulatory adults in the cohort studies, ICOS (47 sites, 5 continents), and PCOS (Liberia) and followed for 28-days. Kaplan-Meier estimates of percentage of those hospitalized or died were derived. Risk factors for hospitalization, death, and failure to recover were identified using Cox and logistic models respectively.

9817(ICOS) and 125(PCOS) participants, 46.7% male; median age 43 years; 24.5% with comorbidity(s); 0.8% pregnant; 9.3% SARS-CoV-2 vaccinated, were enrolled June-2020 and January-2022. By 28 days, 424(4.3%) participants were hospitalized or had died; most within 7 days of enrolment(3.4%). Hospitalization or death declined over calendar time i.e. 7.5%(2020); 4.1(first-half 2021) and 2.1%(second-half 2021), P < 0.0001. Older age, male sex, comorbidities, pregnancy, symptomatic disease were each independently associated with hospitalization or death; SARS-CoV-2 vaccination reduced this risk. At 28 days, 26.1% and 29.9% reported ongoing symptoms and failure to return to pre-morbid health respectively.

These global SARS-CoV-2 ambulatory cohort studies identified demographic/clinical risks for hospitalization or death. Vaccination does not fully explain hospitalization and death declines over time. Symptomatic recovery and return to premorbid health were incomplete at 28 days in ≈one third.

Data on treatment outcomes among minority populations treated with remdesivir are limited. We sought to evaluate outcomes among patients hospitalized with COVID-19 and treated with remdesivir among a predominantly Black and LatinX population.

This was a retrospective cohort study of adult patients hospitalized with COVID-19 and treated with remdesivir at an urban hospital in Newark, NJ, between May 1, 2020, and April 30, 2021, prior to widespread COVID-19 vaccination uptake. We describe 28-day mortality by demographic, socio-economic, and clinical factors, including clinical status by World Health Organization's (WHO) 8-point Ordinal Scale for Clinical Improvement.

A total of 206 patients met study inclusion criteria (52% were male, 41% non-Hispanic Black and 42% Hispanic). Overall mortality at 28 days was 11%. Eighty-one percent of patients with baseline WHO status of 4 or greater recovered by day 14. Mortality was higher among those who were older (p = 0.01), those with underlying diabetes mellitus (p = 0.047), those with more severe illness on admission by WHO Ordinal Scale (WHO status ≥ 4), and those on concomitant tociluzimab or convalescent plasma use.

We found that remdesivir was effective in treating most COVID-19 patients in our study. Traditional risk factors, such as advanced age and underlying co-morbidities, were associated with worse clinical outcomes and deaths.

A substantial number of individuals worldwide experience long COVID, or post-COVID condition. Other postviral and autoimmune conditions have a female predominance, but whether the same is true for long COVID, especially within different subgroups, is uncertain.

To evaluate sex differences in the risk of developing long COVID among adults with SARS-CoV-2 infection.

This cohort study used data from the National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER)-Adult cohort, which consists of individuals enrolled in and prospectively followed up at 83 sites in 33 US states plus Washington, DC, and Puerto Rico. Data were examined from all participants enrolled between October 29, 2021, and July 5, 2024, who had a qualifying study visit 6 months or more after their initial SARS-CoV-2 infection.

Self-reported sex (male, female) assigned at birth.

Development of long COVID, measured using a self-reported symptom-based questionnaire and scoring guideline at the first study visit that occurred at least 6 months after infection. Propensity score matching was used to estimate risk ratios (RRs) and risk differences (95% CIs). The full model included demographic and clinical characteristics and social determinants of health, and the reduced model included only age, race, and ethnicity.

Among 12 276 participants who had experienced SARS-CoV-2 infection (8969 [73%] female; mean [SD] age at infection, 46 [15] years), female sex was associated with higher risk of long COVID in the primary full (RR, 1.31; 95% CI, 1.06-1.62) and reduced (RR, 1.44; 95% CI, 1.17-1.77) models. This finding was observed across all age groups except 18 to 39 years (RR, 1.04; 95% CI, 0.72-1.49). Female sex was associated with significantly higher overall long COVID risk when the analysis was restricted to nonpregnant participants (RR, 1.50; 95%: CI, 1.27-1.77). Among participants aged 40 to 54 years, the risk ratio was 1.42 (95% CI, 0.99-2.03) in menopausal female participants and 1.45 (95% CI, 1.15-1.83) in nonmenopausal female participants compared with male participants.

In this prospective cohort study of the NIH RECOVER-Adult cohort, female sex was associated with an increased risk of long COVID compared with male sex, and this association was age, pregnancy, and menopausal status dependent. These findings highlight the need to identify biological mechanisms contributing to sex specificity to facilitate risk stratification, targeted drug development, and improved management of long COVID.

Long-term recovery following critical COVID-19 has not been sufficiently studied.

The primary objective was to describe changes in functional outcome and Health-Related Quality of Life (HRQoL) between 3 and 12 months in critically ill COVID-19 survivors. The secondary objective was to investigate factors associated with good functional outcome and HRQoL at 12 months.

Prospective multicentre cohort study including critically ill COVID-19 patients admitted to six intensive care units in Sweden between May 2020 and May 2021. Surviving patients were invited to face-to-face follow-ups at 3 and 12 months. Functional outcome was assessed using the Glasgow Outcome Scale Extended (GOSE), ranging from 1 to 8. Physical and mental HRQoL was assessed by the physical component summary (PCS) and mental component summary (MCS) scores of the Short form health survey version 2 (SF-36v2®). Multivariable logistic regression models were used to identify factors associated with good functional outcome (GOSE ≥7) and good physical and mental HRQoL (PCS and MCS ≥45) at 12 months.

The percentage of participants with a good functional outcome increased from 35% to 64% between 3 and 12 months (p < .001). Mean PCS improved from 40 to 44 between 3 and 12 months (p < .001), while the mean MCS was within the normal range at 3 months, with no change at 12 months (46 vs. 48, p = .05). Increasing age was associated with a good functional outcome. Lower clinical frailty and absence of diabetes mellitus were associated with a good PCS. A shorter duration of mechanical ventilation was associated with a good outcome for all three outcome measures.

Survivors of critical COVID-19 showed improved functional outcome and physical HRQoL from 3 to 12 months post-ICU. A shorter duration of mechanical ventilation is associated with good functional outcome and good HRQoL, while older age is associated with good functional outcome. Younger patients and those with comorbidities or higher frailty may require targeted follow-up and rehabilitation. Study registration ClinicalTrials.gov Identifier: NCT04974775, registered April 28, 2020.

The aim was to identify predictors for early identification of HFNC failure risk in patients with severe community-acquired (CAP) pneumonia or COVID-19. Data from adult critically ill patients admitted with CAP or COVID-19 and the need for ventilatory support were retrospectively analysed. HFNC failure was defined as the need for invasive ventilation or death before intubation. 60 patients with CAP and 185 with COVID-19 were included. 27 (45%) patients with CAP and 69 (37.3%) patients with COVID-19 showed HFNC failure. Lower oxygenation index, lower respiratory oxygenation (ROX) index, and higher respiratory rate at the start of HFNC were significantly associated with HFNC failure. ROC-analysis identified a respiratory rate of 27/min as the optimal cut-off for predicting HFNC failure, with a specificity of 59% and a sensitivity of 75%, and an oxygenation index after HFNC initiation of 99.6 (specificity 81%, sensitivity 74%). In COVID-19, an elevated CRB65-score at hospital admission and at HFNC-initiation was significantly associated with HFNC failure. In CAP and COVID patients an oxygenation index < 99.6, a respiratory rate > 27/min and a ROX index < 4.88 were predictors for HFNC failure whereas a CRB65 score > 3 at hospital admission and > 2 at HFNC start was predictive for HFNC failure in COVID-19.

To analyse the effects of tracheostomy timing on COVID-19 outcomes by comparing mortality rates at different time points (7, 10 and 14 days).

Systematic review and meta-analysis.

PubMed, Embase, Cochrane Library, Web of Science and Scopus were searched from 31 August 2023 to 6 September 2023.

The primary outcome was short-term mortality, defined as intensive care unit (ICU) mortality, hospital mortality and 28-day or 30-day mortality. The secondary outcomes included mechanical ventilation duration, ICU and hospital days.

Among 3465 patients from 12 studies, the 10-day subgroup analysis revealed higher mortality for earlier tracheostomy than for later tracheostomy (49.7% vs 32.6%, OR 1.91, 95% CI 1.37-2.65). No significant differences were observed at 7- and 14-day marks. Earlier tracheostomy was associated with shorter mechanical ventilation (mean difference=-7.35 days, 95% CI -11.63 to -0.38) and ICU stays (mean difference=-11.24 days, 95% CI -18.50 to -3.97) compared with later tracheostomy. Regarding hospital stay, the later tracheostomy group exhibited a trend towards longer-term inpatients, with no significant difference.

No significant difference in short-term mortality was observed between patients undergoing tracheostomy at 7 and 14 days; however, at 10 days, later tracheostomy resulted in a lower mortality rate. Accordingly, subtle timing differences may impact short-term results in COVID-19 patients. Considering that the later tracheostomy group had longer mechanical ventilation and ICU stays, additional research is required to determine an optimal timing that reduces mortality cost-effectively.

Background: Understanding the interactions between age and comorbidities is crucial for assessing COVID-19 mortality, particularly in patients with cardiac and pulmonary conditions. This study investigates the relationship between comorbidities and mortality outcomes in a cohort of hospitalized COVID-19 patients, emphasizing the interplay of age, cardiac, and pulmonary conditions. Methods: We analyzed a cohort of 3005 patients hospitalized with COVID-19 between 2020 and 2022. Key variables included age, comorbidities (diabetes, cardiac, pulmonary, and neoplasms), and clinical outcomes. Chi-square tests and logistic regression models were used to assess the association between comorbidities and mortality. Stratified analyses by age, diabetes, and pulmonary conditions were conducted to explore interaction effects. Additionally, interaction terms were included in multivariable logistic regression models to evaluate the combined impact of age, comorbidities, and mortality. Results: Cardiac conditions such as hypertension, ischemic cardiopathy, and myocardial infarction showed significant protective effects against mortality in younger patients and in those without pulmonary conditions (p < 0.001). However, these protective effects were diminished in older patients and those with pulmonary comorbidities. Age was found to be a significant modifier of the relationship between cardiac conditions and mortality, with a stronger protective effect observed in patients under the median age (p < 0.001). Pulmonary comorbidities significantly increased the risk of mortality, particularly when co-occurring with cardiac conditions (p < 0.001). Diabetes did not significantly modify the relationship between cardiac conditions and mortality. Conclusions: The findings highlight the complex interactions between age, cardiac conditions, and pulmonary conditions in predicting COVID-19 mortality. Younger patients with cardiac comorbidities show a protective effect against mortality, while pulmonary conditions increase mortality risk, especially in older patients. These insights suggest that individualized risk assessments incorporating age and comorbidities are essential for managing COVID-19 outcomes.

Epidemiological studies report the association of diverse cardiovascular conditions with coronavirus disease 2019 (COVID-19), but the causality has remained to be established. Specific genetic factors and the extent to which they can explain variation in susceptibility or severity are largely elusive. The present study aimed to evaluate the link between 32 cardio-metabolic traits and COVID-19. A total of 60 participants were enrolled, who were categorized into the following 4 groups: A control group with no COVID-19 or any other underlying pathologies, a group of patients with a certain form of dyslipidemia and predisposition to atherosclerotic disease, a COVID-19 group with mild or no symptoms and a COVID-19 group with severe symptomatology hospitalized at the Intensive Care Unit of Sotiria Hospital (Athens, Greece). Demographic, clinical and laboratory data were recorded and genetic material was isolated, followed by simultaneous analysis of the genes related to dyslipidemia using a custom-made next-generation sequencing panel. In the COVID-19 group with mild or absent symptoms, the variant c.112C>T:p.P38S was detected in the sodium channel epithelial 1 subunit α (SCNN1A) gene, with a major allele frequency (Maf) of <0.01. In the COVID-19 group with severe symptoms, the variant c.786G>A:p.T262T was detected in the SCNN1B gene, which encodes for the β-subunit of the epithelial sodium channel ENaC, with a Maf <0.01. None of the two rare variants were detected in the control or dyslipidemia groups. In conclusion, the current study suggests that ENaC variants are likely associated with genetic susceptibility to COVID-19, supporting the rationale for the risk and protective genetic factors for the morbidity and mortality of COVID-19.

The COVID-19 pandemic raised concerns about whether individuals with chronic respiratory diseases, such as asthma, were at higher risk of severe outcomes. Although several studies were published on this topic, not all included asthma as a risk factor. Therefore, describing the clinical characteristics of COVID-19-infected asthma patients in a specialized respiratory center is valuable as a real-life study.

To investigate the clinical characteristics and disease severity in SARS-CoV-2-infected adults with pre-existing asthma hospitalized at the National Institute of Respiratory Diseases (INER) in Mexico City.

We conducted a retrospective, observational study on adults with confirmed COVID-19 hospitalized from March 2020 to June 2021. Out of 2,249 reviewed medical records, we identified asthmatic patients and compared them with a matched non-asthmatic control group to assess asthma's impact on COVID-19 severity and outcomes.

Based on the clinical records, asthma prevalence among hospitalized patients was low (1.51%); of these, 73% had allergic and 27% had non-allergic asthma. COVID-19 severity did not vary significantly between asthma phenotypes, although there was higher mortality among patients with non-allergic asthma. Most patients in both groups developed a severe form of the disease and higher mortality rates than non-asthmatics, though the differences were not statistically significant.

Asthma prevalence among patients with COVID-19 was low, but mortality was higher in asthma patients. Although the small sample size limits the generalizability of these findings, this study in a Mexican population hospitalized in a reference hospital provides insights for improving asthma management in future pandemics.

We employ pDyn (derived from "pandemics dynamics"), an agent-based epidemiological model, to forecast the fourth wave of the SARS-CoV-2 epidemic, primarily driven by the Delta variant, in Polish society. The model captures spatiotemporal dynamics of the epidemic spread, predicting disease-related states based on pathogen properties and behavioral factors. We assess pDyn's validity, encompassing pathogen variant succession, immunization level, and the proportion of vaccinated among confirmed cases. We evaluate its predictive capacity for pandemic dynamics, including wave peak timing, magnitude, and duration for confirmed cases, hospitalizations, ICU admissions, and deaths, nationally and regionally in Poland. Validation involves comparing pDyn's estimates with real-world data (excluding data used for calibration) to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time. To assess the accuracy of pDyn's predictions, we compared simulation results with real-world data acquired after the simulation date. The findings affirm pDyn's accuracy in forecasting and enhancing our understanding of epidemic mechanisms.