COVID-19 Mortality Risk Research Articles

RE: A population-based study of COVID-19 mortality risk in US cancer patients.

RE: A population-based study of COVID-19 mortality risk in US cancer patients.

In-hospital outcomes of healthcare-associated COVID-19 (Omicron) versus healthcare-associated influenza: a retrospective, nationwide cohort study in Switzerland.

As COVID-19 is integrated into existing infectious disease control programs, it is important to understand the comparative clinical impact of COVID-19 and other respiratory diseases. We conducted a retrospective cohort study of patients with symptomatic healthcare-associated COVID-19 or influenza reported to the nationwide, hospital-based surveillance system in Switzerland. Included patients were adults (≥18 years) hospitalized for ≥3 days in tertiary care and large regional hospitals. Patients had COVID-19 symptoms and a RT-PCR-confirmed SARS-CoV-2 infection ≥3 days after hospital admission between 1 February 2022 and 30 April 2023, or influenza symptoms and a RT-PCR-confirmed influenza A or B infection ≥3 days after hospital admission between 1 November 2018 and 30 April 2023. Primary and secondary outcomes were 30-day in-hospital mortality and admission to intensive care unit (ICU), respectively. Cox regression (Fine-Gray model) was used to account for time-dependency and competing events, with inverse probability weighting to adjust for confounding. We included 2901 patients with symptomatic healthcare-associated COVID-19 (Omicron) and 868 patients with symptomatic healthcare-associated influenza from nine hospitals. We found a similar case fatality ratio between healthcare-associated COVID-19 (Omicron) (6.2%) and healthcare-associated influenza (6.1%) patients; after adjustment, patients had a comparable subdistribution hazard ratio for 30-day in-hospital mortality (0.91, 95%CI 0.67-1.24). A similar proportion of patients were admitted to ICU (2.4% COVID-19; 2.6% influenza). COVID-19 and influenza continue to cause severe disease among hospitalized patients. Our results suggest that in-hospital mortality risk of healthcare-associated COVID-19 (Omicron) and healthcare-associated influenza are comparable.

Convergent and divergent immune aberrations in COVID-19, post-COVID-19-Interstitial Lung Disease and Idiopathic Pulmonary Fibrosis.

We aimed to study transcriptional and phenotypic changes in circulating immune cells associated with increased risk of mortality in COVID-19, resolution of pulmonary fibrosis in post-COVID-19-Interstitial Lung Disease (ILD) and persistence of Idiopathic Pulmonary Fibrosis. Whole blood and Peripheral Blood Mononuclear cells (PBMC) were obtained from 227 subjects with COVID-19, post-COVID-19 Interstitial Lung Disease (ILD), IPF and controls. We measured a 50-gene signature (nCounter, Nanostring) previously found to be predictive of IPF and COVID-19 mortality along with plasma levels of several biomarkers by Luminex. Additionally, we performed single-cell RNA sequencing in PBMC (10X Genomics) to determine the cellular source of the 50-gene signature. We identified the presence of three genomic risk profiles in COVID-19 based on the 50-gene signature associated with low, intermediate, or high-risk of mortality and with significant differences in pro-inflammatory and pro-fibrotic cytokines. COVID-19 patients in the high-risk group had increased expression of seven genes in CD14+HLA-DRlowCD163+Monocytic-Myeloid-Derived Suppressive cells (7Gene-M-MDSCs) and decreased expression of 43 genes in CD4 and CD8 T cell subsets. The loss of 7Gene-M-MDSC and increased expression of these 43 genes in T cells was seen in survivors with post-COVID-19-ILD. On the contrary, IPF patients had low expression of the 43 genes in CD4 and CD8 T cells. Collectively, we showed that a 50-gene, high-risk profile, predictive of IPF and COVID-19 mortality is characterized by a genomic imbalance in monocyte and T-cell subsets. This imbalance reverses in survivors with post-COVID-19-ILD highlighting genomic differences between post-COVID-19-ILD and IPF.

Long term mortality trends in people with severe mental illnesses and how COVID-19, ethnicity and other chronic mental health comorbidities contributed: a retrospective cohort study.

People with schizophrenia-spectrum and bipolar disorders (severe mental illnesses; 'SMI') experience excess mortality. Our aim was to explore longer-term trends in mortality, including the COVID-19 pandemic period, with a focus on additional vulnerabilities (psychiatric comorbidities and race/ ethnicity) in SMI. Retrospective cohort study using electronic health records from secondary mental healthcare, covering a UK region of 1.3 million people. Mortality trends spanning fourteen years, including the COVID-19 pandemic, were assessed in adults with clinician-ascribed ICD-10 diagnoses for schizophrenia-spectrum and bipolar disorders. The sample comprised 22 361 people with SMI with median follow-up of 10.6 years. Standardized mortality ratios were more than double the population average pre-pandemic, increasing further during the pandemic, particularly in those with SMI and psychiatric comorbidities. Mortality risk increased steadily among people with SMI and comorbid depression, dementia, substance use disorders and anxiety over 13-years, increasing further during the pandemic. COVID-19 mortality was elevated in people with SMI and comorbid depression (sub-Hazard Ratio: 1.48 [95% CI 1.03-2.13]), dementia (sHR:1.96, 1.26-3.04) and learning disabilities (sHR:2.30, 1.30-4.06), compared to people with only SMI. COVID-19 mortality risk was similar for minority ethnic groups and White British people with SMI. Elevated all-cause mortality was evident in Black Caribbean (adjusted Rate Ratio: 1.40, 1.11-1.77) and Black African people with SMI (aRR: 1.59, 1.07-2.37) during the pandemic relative to earlier years. Mortality has increased over time in people with SMI. The pandemic exacerbated pre-existing trends. Actionable solutions are needed which address wider social determinants and address disease silos.

Impact of pharmaceutical and non-pharmaceutical interventions on COVID-19 in Tunisia

BackgroundIn COVID-19 management, a variety of pharmaceutical interventions (PI) and non- pharmaceutical interventions (NPI) were adopted to limit the spread of the disease and its associated deaths. We aimed to evaluate the impact of PI and NPI on risks of COVID-19 transmission and deaths.MethodWe collected aggregate data from March 2nd, 2020, to December 1, 2022 from the Tunisian Ministry of Health's website and OurWorldInData.org site. NPI Periods (NPIP: March 2020 to March 2021) and PI Periods (PIP) were distributed to NPIP1, 2, 3 and 4 and to PIP1, 2, 3 and 4, respectively. We calculated the Relative Risks (RR) and 95% Confidence Intervals (CI) by comparing the subsequent period with previous one.ResultsThe risk of SARS-CoV-2 transmission increased progressively from the zero cases period (NPIP2) to the mitigate strategy period (NPIP3) (RR = 14.0; 95% CI: 12.4–15.8) and to the stop-and-go epidemic control period (NPIP4) (RR = 23.1 (95% CI: 22.4–23.9). It was stabilized in the targeted vaccination period (PIP1) (RR = 1.08, 95% CI: 1.07–1.08) and reduced during the mass vaccination period (PIP2) (RR: 0.50, 95% CI: 0.50–0.51). SARS-CoV-2 transmission, increased during PIP3 concomitant with the Omicron wave (RR = 2.65, 95% CI: 2.64–2.67). It remained at a low level in PIP4 (RR = 0.18; 95% CI: 0.18–0.18).Compared to NPIP2, NPIP3 and NPIP4 were associated with a higher risk of COVID-19 mortality (RR = 3.337; 95% CI: 1.797–6.195) and (RR = 72.63 (95% CI: 54.01–97.68), respectively. Since the start of the immunization program, the risk of COVID-19 death has consistently decreased. In comparison to each previous period, the risk transitioned in PIP1 to RR = 0.91; 95% CI: 0.88–0.93, then to RR = 0.85; 95% CI: 0.83–0.88 in PIP2, to RR = 0.47; 95% CI: 0.45–0.50 in PIP3, and to RR = 0.19; 95% CI: 0.18–0.24 during PIP4.ConclusionIn terms of lowering the risk of transmission and mortality, the NP strategy at the beginning of the epidemic outperformed the IP strategy during the outbreak.

Covid-19 mortality risk factors: Survival analysis of the hospitalized cohort in Sahloul university hospital in Tunisia

Background Tunisia was one of the most affected nations with COVID-19 disease. The clinical features of this illness range from asymptomatic illness to death. Aim To analyse the survival of patients admitted to a Tunisian tertiary care center (Sahloul University Hospital) and identify independent predictive factors for hospital COVID-19 mortality. Methods A longitudinal study was conducted among confirmed COVID-19 patients hospitalized in Sahloul University Hospital between September 2020 and September 2022. Cox univariate regression was used to calculate the Hazard Ratio (HR) of death for patient characteristics regarding the time at risk. Risk factors with a p value of 0.2 or less in the univariate analysis were initially included in the multivariate Cox regression models. Results During the study at Sahloul Hospital, 1978 patients were hospitalized, averaging 55.65 years in age (± 21.39). Among them, 417 deaths occurred, with a median survival of 30 days (± 3.11) [23.904-36.096]. Factors significantly affecting the survival curve were: Age, male gender, comorbidities, diabetes, hypertension, endocrine diseases, obesity, intubation, and ICU admission Multilevel survival analyses revealed that hypertension (aHR 1.24; CI [1.02-1.52]; p=0.028), ICU admission (aHR 12.20- CI [9.56-15.57]; p<10-3), and male gender (HR 1.19; CI [0.98-1.46]; p=0.076) were independent factors associated with COVID-19 mortality. Conclusion Our study concluded the importance of the early identification of high-risk COVID-19 patients to decrease this virus mortality. A good understanding of the possible clinical factors associated with COVID-19 severity is helpful for clinicians in identifying patients who are at high risk and require prioritized treatment to minimize death.

Short-term exposure to ambient air pollution and severe COVID-19: mortality and hospital admission to COVID-19 in the Netherlands from february to december 2020

This study aimed to examine acute effects of exposure to ambient air pollution on COVID-19 hospital admissions and mortality in the Netherlands. We hypothesized that exposure to increased air pollution in the preceding week might trigger an exacerbation of health of infected individuals.Associations between daily concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) and ≤10 µm (PM10), nitrogen dioxide (NO2), ozone (O3) and risk of hospital admissions and mortality due to COVID-19 from February to December 2020 was analyzed across all 352 Dutch municipalities grouped into 12 provinces. Time-series models were used to fit province-specific estimates, followed by meta-analyses to produce national estimates. Analyses were based on daily averages of PM2.5, PM10, NO2, and maximum 8-hour running average of O3 on a 1×1 km grid and averaged on municipality level by population weight. Models were adjusted for spatiotemporal confounders, including government policies in response to the number of COVID-19 infections. Since there were only few COVID-19 cases during the summertime when O3 levels were highest, associations between O3 and COVID-19 health outcomes were not further explored.We found associations between exposure to air pollution in the preceding week (average of lag 0-7 days) and COVID-19 hospital admissions and mortality. On a national level, an interquartile range increase in PM2.5, PM10 and NO2 exposure was associated with 11-12% increased mortality risk; the risk for hospital admissions was higher: 19-25%. Observed associations were more robust for PM than NO2 in two-pollutant models.Our results suggest that short-term exposure to PM2.5 and PM10 may increase the risk of COVID-19 mortality and hospital admission. This indicates that, consistent with previous studies on air pollution and respiratory infections, the population at risk of being hospitalized or dying of COVID-19 is extra vulnerable to the adverse effects of short-term air pollution exposure.

Development of decision tree classification algorithms in predicting mortality of COVID-19 patients

IntroductionThe accurate prediction of COVID-19 mortality risk, considering influencing factors, is crucial in guiding effective public policies to alleviate the strain on the healthcare system. As such, this study aimed to assess the efficacy of decision tree algorithms (CART, C5.0, and CHAID) in predicting COVID-19 mortality risk and compare their performance with that of the logistic model.MethodsThis retrospective cohort study examined 5080 cases of COVID-19 in Babol, a city in northern Iran, who tested positive for the virus via PCR from March 2020 to March 2022. In order to check the validity of the findings, the data was randomly divided into an 80% training set and a 20% testing set. The prediction models, such as Logistic regression models and decision tree algorithms, were trained on the 80% training data and tested on the 20% testing data. The accuracy of these methods for the test samples was assessed using measures like ROC curve, sensitivity, specificity, and AUC.ResultsThe findings revealed that the mortality rate for COVID-19 patients who were admitted to hospitals was 7.7%. Through cross validation, it was determined that the CHAID algorithm outperformed other decision tree and logistic regression algorithms in specificity, and precision but not sensitivity in predicting the risk of COVID-19 mortality. The CHAID algorithm demonstrated a specificity, precision, accuracy, and F-score of 0.98, 0.70, 0.95, and 0.52 respectively. All models indicated that factors such as ICU hospitalization, intubation, age, kidney disease, BUN, CRP, WBC, NLR, O2 sat, and hemoglobin were among the factors that influenced the mortality rate of COVID-19 patients.ConclusionsThe CART and C5.0 models had outperformed in sensitivity but CHAID demonstrates a better performance compared to other decision tree algorithms in specificity, precision, accuracy and shows a slight improvement over the logistic regression method in predicting the risk of COVID-19 mortality in the population under study.

Mediating Effect of the Stay-at-Home Order on the Association between Mobility, Weather, and COVID-19 Infection and Mortality in Indiana and Kentucky: March to May 2020

(1) Background: The association of COVID-19 infection and mortality with mobility and weather in Indiana and Kentucky was compared for the period from 1 March to 15 May 2020. (2) Methods: The risk of COVID-19 infection and mortality was evaluated using Cox regressions with the following covariates: mobility (retail/recreation, grocery/pharmacy, and workplace), weather (precipitation, minimum and maximum temperature, ultraviolet [UV] index), and metropolitan status. (3) Results: A higher maximum temperature (adjusted hazard ratioinfection (aHRi) = 1.01; adjusted hazard ratiodeath (aHRd) = 1.001), metropolitan status (aHRi = 1.12; aHRd = 2.05), and a higher minimum temperature (aHRi = 1.01) were associated with increased risks of COVID-19 infection and/or mortality. Protection against COVID-19 infection and/or mortality was associated with retail/recreation (aHRi = 0.97; aHRd = 0.937), grocery/pharmacy (aHRi = 0.991; aHRd = 0.992), workplace (aHRi = 0.99; aHRd = 0.965), precipitation (aHRi = 0.999; aHRd = 0.9978), UV index (aHRi = 0.37; aHRd = 0.748), and a higher minimum temperature (aHRd = 0.994). COVID-19 infection (aHRi = 1.18) and mortality (aHRd = 1.59) risks were higher in Indiana compared to Kentucky. (4) Conclusions: COVID-19 infection and mortality were 18% and 59% more likely among Indiana residents compared to Kentucky residents, respectively. This may be attributed to variations in stay-at-home order compliance and enforcement between Indiana and Kentucky.

Prevalence and Correlates of SARS-CoV-2 Vaccine Uptake and Hesitancy Among People With HIV Across the United States.

People with HIV (PWH) have higher risk of COVID-19 mortality. SARS-CoV-2 vaccination is highly effective among PWH, although vaccine hesitancy could limit the population-level impact. From February 2021 to April 2022, PWH from 8 sites in the Centers for AIDS Research Network of Integrated Clinical Systems completed a vaccine hesitancy instrument as part of routine care. Participants were defined as vaccine hesitant if they had not received the SARS-CoV-2 vaccine and would probably/definitely not receive it. We assessed factors associated with SARS-CoV-2 vaccine hesitancy using logistic regression adjusted for demographics, unsuppressed viral load (VL > 200 copies/mL), month, and time on ART; using inverse probability weighting for survey nonresponse. Overall, 3288 PWH with a median age of 55 were included; 18% were female and 94% were virally suppressed. At the time of survey, 27% reported they had not received the SARS-CoV-2 vaccine, and 9% (n = 279) reported vaccine hesitancy. Factors associated with vaccine hesitancy included female sex (adjusted odds ratio [AOR] = 2.3; 95% confidence interval (CI): 1.6-3.2), Black vs. White race (AOR 1.7; 95% CI: 1.2 to 2.4), younger age (AOR 1.4; 95% CI: 1.2 to 1.5), and unsuppressed VL (AOR 1.9; 95% CI: 1.3 to 3.0). Overall, over one-quarter of PWH in this multisite cohort were unvaccinated for SARS-CoV-2 when interviewed February 21-April 22. Vaccine hesitancy was reported by approximately 9% of PWH and was higher among women, Black PWH, younger PWH, PWH with unsuppressed VL, and those in the South/Midwest. Renewed efforts are needed to address concerns of PWH about vaccinations against COVID-19 as the pandemic evolves, and vaccines in general, given the potential for future pandemics.

Clinical, Laboratory, and Demographic Factors Contributing to COVID-19 Mortality Risk

Background: This study aimed to identify COVID-19 mortality risk factors and relevant laboratory markers to inform prevention and treatment strategies. Methods: This cross-sectional study was conducted in Iran, involving 1561 patients diagnosed with COVID-19 between 2019 and 2021. Demographic, clinical, and laboratory data were collected from hospital records and analyzed using a logistic regression model. Results: The mortality rate attributed to COVID-19 in this study was 24.7%. Identified risk factors for mortality included intubation, multiple chronic conditions, liver or kidney disease or cancer, low RBC levels, and abnormal creatinine (Cr) levels. The adjusted odds ratios (adjOR) and 95% confidence intervals (CIs) for these factors were as follows: intubation (70.75, 14.07:355.84), concurrent chronic diseases (24.29, 3.25:181.24), liver or kidney disease or cancer (5.13, 1.21:21.81), low RBC levels (5.21, 1.24:21.79), and abnormal Cr levels (5.09, 1.21:21.43). Conclusion: The findings from this study indicated that several factors, including intubation, multiple chronic conditions, liver or kidney disease or cancer, and low Cr levels were associated with a higher risk of death from COVID-19. These results highlight the significance of continuous monitoring and specialized care for patients exhibiting these risk factors to reduce the risk of COVID-19 mortality.

Health insurance system fragmentation and COVID-19 mortality: Evidence from Peru.

Peru has a fragmented health insurance system in which most insureds can only access the providers in their insurer's network. The two largest sub-systems covered about 53% and 30% of the population at the start of the pandemic; however, some individuals have dual insurance and can thereby access both sets of providers. We use data on 24.7 million individuals who belonged to one or both sub-systems to investigate the effect of dual insurance on COVID-19 mortality. We estimate recursive bivariate probit models using the difference in the distance to the nearest hospital in the two insurance sub-systems as Instrumental Variable. The effect of dual insurance was to reduce COVID-19 mortality risk by 0.23% compared with the sample mean risk of 0.54%. This implies that the 133,128 COVID-19 deaths in the sample would have been reduced by 56,418 (95%CI: 34,894, 78,069) if all individuals in the sample had dual insurance.

Preferences for COVID-19 Vaccines: Systematic Literature Review of Discrete Choice Experiments.

Vaccination can be viewed as comprising the most important defensive barriers to protect susceptible groups from infection. However, vaccine hesitancy for COVID-19 is widespread worldwide. We aimed to systematically review studies eliciting the COVID-19 vaccine preference using discrete choice experiments. A literature search was conducted in PubMed, Embase, Web of Science, Scopus, and CINAHL Plus platforms in April 2023. Search terms included discrete choice experiments, COVID-19, and vaccines and related synonyms. Descriptive statistics were used to summarize the study characteristics. Subgroup analyses were performed by factors such as high-income countries and low- and middle-income countries and study period (before, during, and after the pandemic wave). Quality appraisal was performed using the 5-item Purpose, Respondents, Explanation, Findings, and Significance checklist. The search yield a total of 623 records, and 47 studies with 53 data points were finally included. Attributes were grouped into 4 categories: outcome, process, cost, and others. The vaccine effectiveness (21/53, 40%) and safety (7/53, 13%) were the most frequently reported and important attributes. Subgroup analyses showed that vaccine effectiveness was the most important attribute, although the preference varied by subgroups. Compared to high-income countries (3/29, 10%), a higher proportion of low- and middle-income countries (4/24, 17%) prioritized safety. As the pandemic progressed, the duration of protection (2/24, 8%) during the pandemic wave and COVID-19 mortality risk (5/25, 20%) after the pandemic wave emerged as 2 of the most important attributes. Our review revealed the critical role of vaccine effectiveness and safety in COVID-19 vaccine preference. However, it should be noticed that preference heterogeneity was observed across subpopulations and may change over time. PROSPERO CRD42023422720; https://tinyurl.com/2etf7ny7.

Chronic Obstructive Pulmonary Disease and the Omicron Variant of COVID-19 Prognosis: A Retrospective Cohort Study.

This retrospective cohort study aimed to investigate the association between chronic obstructive pulmonary disease (COPD) and the prognosis of COVID-19 patients infected with the Omicron variant. The primary objective was to determine if COVID-19 patients with COPD had higher mortality rates compared to those without COPD. Secondary objectives included assessing the risk of respiratory failure, hospital stay length, intensive care unit (ICU) admission, and oxygen requirements in COPD patients with COVID-19. The study included 2761 COVID-19 patients admitted to the Princess Margaret Hospital, Hong Kong, between January 1 and June 30, 2022. Among them, 7.4% (n = 205) had COPD. Demographic and clinical data, including vaccination status and comorbidities, were collected. The primary outcome was 30-day mortality, and secondary outcomes included respiratory support requirement, hospital stay length, and ICU admission. Logistic regression analyses were conducted, adjusting for potential confounders. COPD did not independently increase the risk of COVID-19 mortality after adjusting for confounders. Instead, older age, male sex, incomplete vaccination, long-term oxygen therapy use, and specific comorbidities were identified as significant predictors of 30-day mortality. COPD patients were more likely to require oxygen and noninvasive ventilation, but there were no significant differences in other secondary outcomes compared to non-COPD patients. COPD itself was not an independent risk factor for COVID-19 mortality. Age, sex, vaccination status, comorbidities, and long-term oxygen therapy use were important predictors of mortality. These findings underscore the importance of considering multiple factors when assessing the impact of COPD on COVID-19 prognosis, particularly with the Omicron variant.

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients.

Assessment of COVID-19 risk factors of early and long-term mortality with prediction models of clinical and laboratory variables

BackgroundCoronavirus disease (COVID-19) may lead to serious complications and increased mortality. The outcomes of patients who survive the early disease period are burdened with persistent long-term symptoms and increased long-term morbidity and mortality. The aim of our study was to determine which baseline parameters may provide the best prediction of early and long-term outcomes.MethodsThe study group comprised 141 patients hospitalized for COVID-19. Demographic data, clinical data and laboratory parameters were collected. The main study endpoints were defined as in-hospital mortality and 1-year mortality. The associations between the baseline data and the study endpoints were evaluated. Prediction models were created.ResultsThe in-hospital mortality rate was 20.5% (n = 29). Compared with survivors, nonsurvivors were significantly older (p = 0.001) and presented comorbidities, including diabetes (0.027) and atrial fibrillation (p = 0.006). Assessment of baseline laboratory markers and time to early death revealed negative correlations between time to early death and higher IL-6 levels (p = 0.032; Spearman rho − 0.398) and lower lymphocyte counts (p = 0.018; Pearson r -0.438). The one-year mortality rate was 35.5% (n = 50). The 1-year nonsurvivor subgroup was older (p < 0.001) and had more patients with arterial hypertension (p = 0.009), diabetes (p = 0.023), atrial fibrillation (p = 0.046) and active malignancy (p = 0.024) than did the survivor subgroup. The model composed of diabetes and atrial fibrillation and IL-6 with lymphocyte count revealed the highest value for 1-year mortality risk prediction.ConclusionsDiabetes and atrial fibrillation, as clinical factors, and LDH, IL-6 and lymphocyte count, as laboratory determinants, are the best predictors of COVID-19 mortality risk.

Eight-Fold Increased COVID-19 Mortality in Autosomal Dominant Tubulointerstitial Kidney Disease due to MUC1 Mutations: An Observational Study.

MUC1 and UMOD pathogenic variants cause autosomal dominant tubulointerstitial kidney disease (ADTKD). MUC1 is expressed in kidney, nasal mucosa and respiratory tract, while UMOD is expressed only in kidney. Due to haplo-insufficiency ADTKD- MUC1 patients produce approximately 50% of normal mucin-1. To determine whether decreased mucin-1 production was associated with an increased COVID-19 risk, we sent a survey to members of an ADTKD registry in September 2021, after the initial, severe wave of COVID-19. We linked results to previously obtained ADTKD genotype and plasma CA15-3 (mucin-1) levels and created a longitudinal registry of COVID-19 related deaths. Surveys were emailed to 637 individuals, with responses from 89 ADTKD- MUC1 and 132 ADTKD- UMOD individuals. 19/83 (23%) ADTKD- MUC1 survey respondents reported a prior COVID-19 infection vs. 14/125 (11%) ADTKD- UMOD respondents (odds ratio (OR) 2.35 (95%CI 1.60-3.11, P = 0.0260). Including additional familial cases reported from survey respondents, 10/41 (24%) ADTKD- MUC1 individuals died of COVID-19 vs. 1/30 (3%) with ADTKD- UMOD , with OR 9.21 (95%CI 1.22-69.32), P = 0.03. The mean plasma mucin-1 level prior to infection in 14 infected and 27 uninfected ADTKD- MUC1 individuals was 7.06±4.12 vs. 10.21±4.02 U/mL ( P = 0.035). Over three years duration, our longitudinal registry identified 19 COVID-19 deaths in 360 ADTKD- MUC1 individuals (5%) vs. 3 deaths in 478 ADTKD- UMOD individuals (0.6%) ( P = 0.0007). Multivariate logistic regression revealed the following odds ratios (95% confidence interval) for COVID-19 deaths: ADTKD- MUC1 8.4 (2.9-29.5), kidney transplant 5.5 (1.6-9.1), body mass index (kg/m 2 ) 1.1 (1.0-1.2), age (y) 1.04 (1.0-1.1). Individuals with ADTKD- MUC1 are at an eight-fold increased risk of COVID-19 mortality vs. ADTKD- UMOD individuals. Haplo-insufficient production of mucin-1 may be responsible.

Shared genetics and causal association between plasma levels of SARS-CoV-2 entry receptor ACE2 and Alzheimer's disease.

Alzheimer's disease (AD) is the highest risk of COVID-19 infection, hospitalization, and mortality. However, it remains largely unclear about the link between AD and COVID-19 outcomes. ACE2 is an entry receptor for SARS-CoV-2. Circulating ACE2 is a novel biomarker of death and associated with COVID-19 outcomes. Here, we explored the shared genetics and causal association between AD and plasma ACE2 levels using large-scale genome-wide association study, gene expression, expression quantitative trait loci, and high-throughput plasma proteomic profiling datasets. We found a significant causal effect of genetically increased circulating ACE2 on increased risk of AD. Cross-trait association analysis identified 19 shared genetic variants, and three variants rs3104412, rs2395166, and rs3135344 at chromosome 6p21.32 were associated with COVID-19 infection, hospitalization, and severity. We mapped 19 variants to 117 genes, which were significantly upregulated in lung, spleen, and small intestine, downregulated in brain tissues, and involved in immune system, immune disease, and infectious disease pathways. The plasma proteins corresponding to LST1, AGER, TNXB, and APOC1 were predominantly associated with COVID-19 infection, ventilation, and death. Together, our findings suggest the shared genetics and causal association between AD and plasma ACE2 levels, which may partially explain the link between AD and COVID-19.

Role of Inflammatory Markers in Severity, ICU Admission, and Mortality in COVID-19: A Systematic Review and Meta-analysis of 79,934 Patients

Introduction Despite extensive investigations into the roles of inflammatory biomarkers in the prognosis of COVID-19 through systematic reviews and meta-analyses, they are limited by small sample sizes and focus on a specific marker. This meta-analysis investigated the role of 11 inflammatory biomarkers in severity, intensive care unit (ICU) admission, and mortality among COVID-19 patients. Methods Studies up to October 25, 2023, were identified through a search of Google Scholar, limited to human studies published in English. Inclusion criteria required confirmed COVID-19 cases diagnosed via reliable laboratory methods, original articles from eligible journals, proper grouping of severity status, ICU admission, or mortality outcomes, and presentation of continuous data in mean and standard deviation, median with range, or interquartile range. Results A total of 241 studies, comprising 79,934 cases of COVID-19, were included in this study. Albumin levels significantly declined in severe, ICU, and dead cases compared to mild, moderate, non-ICU, and survived cases (p&lt;0.001). C-reactive protein (CRP), D-dimer, erythrocyte sedimentation rate (ESR), ferritin, fibrinogen, Interleukin-6 (IL-6), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), procalcitonin, and white blood cell (WBC) were all significantly (p&lt;0.001) increased and correlated with the severity of COVID-19. CRP, D-dimer, ferritin, fibrinogen, IL-6, LDH, NLR, procalcitonin, and WBC were all significantly (p&lt;0.05) elevated and correlated with the risk of ICU admission (except fibrinogen) and mortality in COVID-19 in both fixed and random effects. Conclusion Inflammatory biomarkers like albumin, CRP, D-dimer, ferritin, IL-6, LDH, NLR, procalcitonin, and WBC all significantly impact severity status, ICU admission, and mortality in COVID-19.

The Value of Ursodeoxycholic Acid and Mesenchymal Stem Cells in the Treatment of Severe COVID-19.

Objective: The objective of this study was to evaluate the therapeutic efficacy of ursodeoxycholic acid (UDCA) and mesenchymal stem cells (MSCs) in patients with severe COVID-19. Methods: We included severe COVID-19 patients hospitalized at Shulan (Hangzhou) Hospital between December 2022 and June 2023. We used a logistic regression model to compare the use of UDCA and MSCs in the two distinct groups of improved and poor outcomes. It is noteworthy that the deterioration group encompassed instances of both death and abandonment of treatment. The receiver operating characteristic (ROC) curve was plotted to assess the performance of the model. The aim was to assess the therapeutic effect of UDCA and MSCs on the outcome of severe COVID-19 patients. Results: A total of 167 patients with severe COVID-19 were included in this study. The analysis revealed that out of 42 patients (25.1%), 17 patients (10.2%) had taken UDCA, and 17 patients (10.2%) had used MSCs. Following a multivariable logistic regression, the results indicated a negative association between UDCA treatment (OR = 0.38 (0.16-0.91), p = 0.029), MSCs treatment (OR = 0.21 (0.07-0.65), p = 0.007), and the risk of severe COVID-19 mortality. Additionally, age showed a positive association with the risk of mortality (OR = 1.03 (1.01-1.07), p = 0.025). Conclusions: UDCA and MSCs have shown potential in improving the prognosis of severe COVID-19 patients and could be considered as additional treatments for COVID-19 in the future.