COVID-19 Mortality Research Articles

Forecasting infection fatality rate of COVID-19: measuring the efficiency of several hybrid models

The main goal of this paper is to delve into a crucial epidemiological metric the daily infection fatality rate in the context of the ongoing COVID-19 pandemic. The significance of understanding this metric lies in its potential to provide insights into the severity and impact of the virus on a daily basis. Methods: To achieve this overarching objective, we employ a comprehensive approach by applying various hybrid models that hybridize both machine learning and statistical techniques. In our pursuit of a deeper understanding, we leverage advanced machine learning algorithms, including Support Vector Machine and Random Forest. These techniques allow us to capture intricate patterns and relationships within the data, contributing to a more nuanced analysis of the infection fatality rate. The application of machine-learning models in epidemiological studies has gained prominence due to their ability to adapt to complex and evolving patterns inherent in infectious disease dynamics. Complementing our machine-learning arsenal, we integrate traditional statistical models such as ARIMA (AutoRegressive Integrated Moving Average), fractional ARIMA, and BATS (Bayesian Structural Time Series). Results. To assess the performance of these models, we employ key evaluation metrics, including Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). These metrics serve as critical benchmarks, allowing us to quantify the accuracy and reliability of our models in predicting the daily infection fatality rate. A meticulous evaluation of model performance is crucial for ensuring the validity and of our findings. According to these measures, we see that hybrid models performed well especially ARIMA-RF model RMSE: 0.29, MSE: 0.084, MAE: 0.215 for the horizon 60 and for horizon 120 ARIMA-RF still the best performance, RMSE: 0.268, MSE: 0.071, MAE: 0.183, we get these results due to the capacity of this approach to handle complex patterns contrarily to other model ARIMA, BATS, RF and SVM. Conclusion. This work adopted this approach in order to build a model to predict infection fatality rate, we aspire to provide a nuanced understanding of the factors influencing the severity of the virus, ultimately contributing to the ongoing discourse on effective public health interventions and mitigation strategies.

Exploratory analysis of serum Krebs von den Lungen-6, blood gas analysis & Brixia score in determining COVID-19 severity & mortality.

Background & objectives Krebs von den Lungen-6 (KL-6) is primarily expressed by the damaged type II pneumocytes. In this context, the relationship of KL-6 with blood gas analysis (BGA) parameters and Brixia score is still limitedly discussed. This study aims to analyze the correlation of KL-6, BGA and Brixia scores to the severity and mortality of COVID-19. Methods A cross-sectional study was conducted in adult COVID-19 positive individuals at Universitas Airlangga Hospital, Surabaya, East Java, Indonesia, from March to August 2021. KL-6, BGA, and Brixia scores were compared according to severity (severe vs. non-severe) and mortality (non-survivor vs. survivor). The receiver operating characteristic (ROC) analysis was also performed to define the optimal cut-off, sensitivity, as well as the specificity of KL-6, BGA and Brixia scores to determine the COVID-19 severity and mortality. Results Total 35 severe and 20 non-severe COVID-19 positive individuals were enrolled in this study. Of those, there were 22 non-survivors. No significant difference in serum KL-6 levels was observed in the severity and mortality groups. KL-6 and HCO3- had positive correlation in the severe group (r=0.37). KL-6 and Brixia scores showed a significant negative correlation among COVID-19 positive individuals (r=-0.283; P=0.036). KL-6 and Brixia scores together served as the best severity markers in the current study [AUC 0.809 (0.697-0.920); Sn/Sp=0.686/0.900)], followed by KL-6 and P/F ratio [AUC 0.800 (0.637-0.963); Sn/Sp=0.971/0.750]. Interpretation & conclusions The findings of this study suggest that KL-6 has the potential to be a useful adjunct laboratory parameter to the BGA and Brixia score representing COVID-19 severity and mortality.

Heterologous mRNA/MVA delivering trimeric-RBD as effective vaccination regimen against SARS-CoV-2: COVARNA Consortium

ABSTRACT Despite the high efficiency of current SARS-CoV-2 mRNA vaccines in reducing COVID-19 morbidity and mortality, waning immunity and the emergence of resistant variants underscore the need for novel vaccination strategies. This study explores a heterologous mRNA/Modified Vaccinia virus Ankara (MVA) prime/boost regimen employing a trimeric form of the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) protein compared to a homologous MVA/MVA regimen. In C57BL/6 mice, the RBD was delivered during priming via an mRNA vector encapsulated in nanoemulsions (NE) or lipid nanoparticles (LNP), followed by a booster with a replication-deficient MVA-based recombinant virus (MVA-RBD). This heterologous mRNA/MVA regimen elicited strong anti-RBD binding and neutralizing antibodies (BAbs and NAbs) against both the ancestral SARS-CoV-2 strain and different variants of concern (VoCs). Additionally, this protocol induced robust and polyfunctional RBD-specific CD4 and CD8 T cell responses, particularly in animals primed with mLNP-RBD. In K18-hACE2 transgenic mice, the LNP-RBD/MVA combination provided complete protection from morbidity and mortality following a live SARS-CoV-2 challenge compared with the partial protection observed with mNE-RBD/MVA or MVA/MVA regimens. Although the mNE-RBD/MVA regimen only protects half of the animals, it was able to induce antibodies with Fc-mediated effector functions besides NAbs. Moreover, viral replication and viral load in the respiratory tract were markedly reduced and decreased pro-inflammatory cytokine levels were observed. These results support the efficacy of heterologous mRNA/MVA vaccine combinations over homologous MVA/MVA regimen, using alternative nanocarriers that circumvent intellectual property restrictions of current mRNA vaccine formulations.

Determinants of COVID-19 severity and mortality in children: A retrospective and multicenter cohort study in Medan, Indonesia.

This study investigated indicators of the severity and mortality of COVID-19 in children in Medan, Sumatera Utara Province, Indonesia. The aim of this study was to identify determinants of severity and outcome of children with COVID-19 as the lesson learned from the COVID-19 pandemic, particularly the limited health facilities in Indonesia. This retrospective cohort study was conducted in 2020, 2021, and 2022 at multiple centers. Inpatient and outpatient children confirmed to be SARS-CoV-2 positive were randomly recruited in the selected hospitals. Baseline data (demographic, clinical, laboratory and radiological data) were collected, and outcomes were classified as recovered/deceased (for the inpatient group) or returned to the hospital (for the outpatient group). Severity status was identified based on the Indonesia COVID-19 guidelines. The laboratory data were categorized according to international standards and data were analyzed using univariate analyzes followed by multivariate logistic regression. A total of 303 inpatient and 114 outpatient children were included in the analysis. Out of the total inpatient cases, nine patients died, with 2.9 mortality rate. Our final multivariate indicated that the presence of shortness of breath (SOB), anemia, and abnormal C-reactive protein (CRP) levels were significantly associated with the severity or the presence of emergency signs, while the presence of SOB and comorbidities were significantly associated with mortality in inpatient children with COVID-19. The presence of fever, cough, SOB, muscle ache and diarrhea were the reasons why the children were returned to the hospital from self-isolation at home among outpatient COVID-19 cases; however, the cough was the only significant factor in the final multivariate mode. This study highlights important determinants of COVID-19 severity and mortality in children, which should be considered during clinical decision-making in low-resource settings of healthcare centers in Indonesia.

Measuring Social Vulnerability Indicators to CoVID Pandemic: A GIS Based Analysis in North 24 Parganas, West Bengal, India

Abstract Identifying highly vulnerable communities and implementing effective strategies are crucial components in interrupting the transmission chain of any virus. This, in turn, can mitigate the economic and social repercussions of a virus outbreak. As part of this research, a CoVID-19 vulnerability map was developed for the North 24 Parganas district in West Bengal, India. The objective of this study is to explore the impact of rural-urban patterns, spatial dynamics, and morphological factors on Coronavirus infections. This investigation involves examining micro-level differentiations within the district area. Various explanatory variables, including socioeconomic, behavioral, environmental, geographic, and demographic factors, are employed to assess the factors influencing the regional spread of infectious diseases. North 24 Parganas has observed the clustering of CoVID-19 cases at the block level. Incorporating socioeconomic and environmental risk factors such as age, gender, caste, religion, language, occupation, and urbanization status into CoVID-19 case and mortality data will undoubtedly aid in elucidating the geographical origins of the pandemic.

Nirmatrelvir/Ritonavir (Paxlovid) Use Among Individuals at Risk of Severe COVID-19: An Analysis of the National COVID Cohort Collaborative (N3C).

Paxlovid is effective in reducing COVID-19 hospitalization and mortality. This study characterized Paxlovid use and evaluated racial/ethnic disparities over time among community-dwelling adults at high risk of progression to severe COVID-19 disease. This retrospective cohort study used the National COVID Cohort Collaborative (N3C) data and included individuals aged 18 years or older diagnosed with COVID-19 between January 2022 and December 2023. The study cohort included nonhospitalized individuals who were at high risk of COVID-19 progression, and selected the first COVID-19 episode in each quarter, including reinfection episodes. Paxlovid use was defined as receiving Paxlovid within ±5 days of a COVID-19 diagnosis. We used descriptive statistics to characterize Paxlovid use overall and by calendar quarter and race/ethnicity. We used a generalized estimating equations (GEE) models to quantify the association of race/ethnicity with Paxlovid use controlling for age, gender, and clinical characteristics. Among 1 264 215 individuals at high risk of disease progression (1 404 607 episodes), Paxlovid use increased from 1.2% in January-March 2022 to 35.1% in October-December 2023. Paxlovid use was more common among non-Hispanic White individuals (23.9%) than non-Hispanic Black (16.5%) and Latinx/e (16.7%) patients. After adjusting age, gender, and clinical characteristics, Paxlovid use was less likely among non-Hispanic Black (odds ratio [OR] 0.69, 95% confidence interval [CI] 0.68-0.70) and Latinx/e (OR 0.72, CI 0.71-0.73) patients than non-Hispanic White patients. Among a large, diverse cohort of community-dwelling individuals with COVID-19, nearly two out of three eligible individuals did not receive Paxlovid, and minoritized racial/ethnic groups were less likely to use Paxlovid than their non-Hispanic White individuals.

Clinical and diagnostic features of long-COVID patients presenting with neurologic symptoms in Chicago.

Long COVID, a condition characterized by persistent symptoms after COVID-19 infection, is increasingly being recognized worldwide. Neurologic symptoms are frequently reported in survivors of COVID-19, making it crucial to better understand this phenomenon both on a societal scale and for the quality of life of these patients. Between January 1, 2020, and July 31, 2022, Illinois(IL) had a standardized cumulative death rate that ranked it 24th out of the 51 states in the United States (US). However, the US had one of the highest per capita COVID-19 death rates among large, high-income countries. [Bollyky T. et al. 2023] As a result of the increased number of COVID-19 infections, there was a rise in the number of patients experiencing Long COVID. At our neuro-infectious disease clinic in Chicago(IL), we observed an increasing number of patients presenting with cognitive and other neurologic symptoms after contracting COVID-19. Initially, we needed to provide these individuals with a better understanding of their condition and expected outcomes. We were thus motivated to further evaluate this group of patients for any patterns in presentation, neurologic findings, and diagnostic testing that would help us better understand this phenomenon. We aim to contribute to the growing body of research on Long COVID, including its presentation, diagnostic testing results, and outcomes to enlighten the long COVID syndrome. We hypothesize that the neurological symptoms resulting from long COVID persist for over 12 months. We conducted a retrospective analysis of clinical data from 44 patients with long-COVID. Cognitive symptoms were the most common presenting concern. Abnormalities in Montreal Cognitive Assessment, electroencephalogram, serum autoantibody testing, and cerebrospinal fluid were found in minority subsets of our cohort. At 12 months, most patients continue to experience neurologic symptoms, though more than half reported moderate or marked improvement compared to initial presentation. Although most of the patients in this study did not show a consistent occurrence of symptoms suggesting a cohesive underlying etiology, our clinical data demonstrated some features of Long COVID patients in Chicago (IL) that could lead to new research avenues, helping us better understand this syndrome that affects patients worldwide.

Prehospital Spinal Muscle Mass Is Unlikely to Be a Predictor of COVID-19 Mortality.

Prehospital Spinal Muscle Mass Is Unlikely to Be a Predictor of COVID-19 Mortality.

Spatial co-occurrence of firearm homicides and opioid overdose deaths in Chicago by level of COVID-19 mortality, 2017–2021

BackgroundFirearm homicide and opioid overdoses were already leading causes of death in the U.S. before both problems surged during the COVID-19 pandemic. Firearm violence, overdoses, and COVID-19 have all disproportionately harmed communities that are socially and economically marginalized, but the co-occurrence of these problems in the same communities has received little attention. To describe the co-occurrence of firearm homicides and opioid overdose deaths with COVID-19 mortality we used 2017–2021 medical examiner’s data from Chicago, IL. Deaths were assigned to zip codes based on decedents’ residence. We stratified zip codes into quartiles by COVID-19 mortality rate, then compared firearm homicide and fatal opioid overdose rates by COVID-19 quartile.FindingsThroughout the study period, firearm homicide and opioid overdose rates were highest in the highest COVID-19 mortality quartile and lowest in the lowest COVID-19 mortality quartile. Increases in firearm homicide and opioid overdose were observed across all COVID-19 mortality quartiles.ConclusionsHigh co-occurrence of these deaths at the community level call for addressing the systemic forces which made them most vulnerable before the pandemic. Such strategies should consider the environments where people reside, not only where fatal injuries occur.

Predictive factors for COVID-19 severity and mortality in hospitalized children

BackgroundUnderstanding the factors influencing disease progression and severity in pediatric COVID-19 cases is essential for effective management and intervention strategies. This study aimed to evaluate the discriminative ability of clinical and laboratory parameters to identify predictors of COVID-19 severity and mortality in hospitalized children.MethodsIn this multicenter retrospective cohort study, we included 468 pediatric patients with COVID-19. We developed a predictive model using their demographic, clinical, and laboratory data. The performance of the model was assessed using various metrics including sensitivity, specificity, positive predictive value rates, and receiver operating characteristics (ROC).ResultsOur findings demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.818 for severity and 0.873 for mortality prediction. Key risk factors for severe COVID-19 in children include low albumin levels, elevated C-reactive protein (CRP), lactate dehydrogenase (LDH), and underlying medical conditions. Furthermore, ROC curve analysis highlights the predictive value of CRP, LDH, and albumin, with AUC values of 0.789, 0.752, and 0.758, respectively.ConclusionOur study indicates that laboratory values are valuable in predicting COVID-19 severity in children. Various factors, including CRP, LDH, and albumin levels, demonstrated statistically significant differences between patient groups, suggesting their potential as predictive markers for disease severity. Implementing predictive analyses based on these markers could aid clinicians in making informed decisions regarding patient management.

Power spectral density and similarity analysis of COVID-19 mortality waves across countries

BackgroundDuring the COVID-19 pandemic, the Johns Hopkins University Center for Systems Science and Engineering (CSSE) established a comprehensive database detailing daily mortality rates across countries. This dataset revealed fluctuating global mortality trends attributable to COVID-19; however, the specific differences and similarities in mortality patterns between countries remain insufficiently explored. Consequently, this study employs Fourier and similarity analyses to examine these patterns within the frequency domain, thereby offering novel insights into the dynamics of COVID-19 mortality waves across different nations. MethodsWe employed the Fast Fourier transform to calculate the power spectral density (PSD) of COVID-19 mortality waves in 199 countries from January 22, 2020, to March 9, 2023. Moreover, we performed a cosine similarity analysis of these PSD patterns among all the countries. ResultsWe identified two dominant peaks in the grand averaged PSD: one at a frequency of 1.15 waves per year (i.e., one wave every 10.4 months) and another at 2.7 waves per year (i.e., one wave every 4.4 months). We also found a cosine similarity index distribution with a skewness of −0.54 and a global median of cosine similarity index of 0.84, thus revealing a remarkable similarity in the dominant peaks of the COVID-19 mortality waves. ConclusionThese findings could be helpful for planetary health if a future pandemic of a similar scale occurs so that effective confinement measures or other actions could be planned during these two identified periods.

Can peripheral perfusion index predict disease mortality in COVID-19 patients in the emergency department

BackgroundCoronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. The prognostic efficacy of Peripheral Perfusion Index (PPI) has been researched in different pathologies such as trauma and sepsis. We hypothesized that PPI may serve as predictor of mortality in hospitalized patients with COVID-19 infection. This study aimed to describe the association between PPI at admission and COVID-19 mortality, a new mortality prediction tool. MethodsThis retrospective, observational study was conducted at a tertiary care center in Turkey. Adult patients diagnosed with COVID-19 infection were enrolled in this study between Februrary 15, 2022 to April 15, 2023. Patient demographic and clinical data including vital signs, laboratory parameters and PPI on admission were collected from an electronic database. PPI was measured using Philips G30E patient monitor system. The primary outcome was in-hospital mortality. ResultsIn total, 200 patients with COVID-19 infection were included and 42 (21 %) in-hospital deaths were identified. For all parameters of study, age, oxygen saturation, respiratory rate, PPI, urea, creatinine, White Blood Cell (WBC), and High-sensitive cardiac Troponin T (hs-cTnT) values were significantly different between survivors vs non-survivors. hs-cTnT >21,25 pg/mL[HR:2.823 (95 % CI:1.211–6583)], PPI <2,15 [HR:2485 (95 % CI:1.194–5.175)], Oxygen saturation <87 % [HR:2258 (95 % CI:1.191–4.282)], and WBC >9680 x103/ml [HR:2.124 (95 % CI:1.083–4.163)] were independent predictors of in-hospital mortality. ConclusionsThis study identified the factors affecting in-hospital mortality among COVID-19 patients. Importantly, besides many parameter, PPI at admission was significantly associated with COVID-19 mortality and could be a feasible marker in emergency department to identify high risk patients.

Anti-SARS-CoV-2 Antibodies versus Vaccination Status in CAD Patients with COVID-19: A Prospective, Propensity Score-Matched Cohort Study.

Despite the currently prevailing, milder Omicron variant, coronary artery disease (CAD) patients constitute a major risk group in COVID-19, exhibiting 2.6 times the mortality risk of non-CAD patients and representing over 22% of non-survivors. No data are currently available on the efficacy of antibody levels in CAD patients, nor on the relevance of vaccination status versus antibody levels for predicting severe courses and COVID-19 mortality. Nor are there definitive indicators to assess if individual CAD patients are sufficiently protected from adverse outcomes or to determine the necessity of booster vaccinations. A prospective, propensity-score-matched, multicenter cohort study comprising 249 CAD patients and 903 controls was conducted. Anti-SARS-CoV-2-spike antibodies were measured on hospital admission. Prespecified endpoints were in-hospital mortality, intensive care, and oxygen administration. After adjustment for potential confounders, CAD patients exhibited 4.6 and 6.1-times higher mortality risks if antibody levels were <1200 BAU/mL and <182 BAU/mL, respectively, compared to CAD patients above these thresholds (aOR 4.598, 95%CI 2.426-8.714, p < 0.001; 6.147, 95%CI 2.529-14.941, p < 0.001). Risk of intensive care was 3.7 and 4.0 (p = 0.003; p < 0.001), and risk of oxygen administration 2.6 and 2.4 times higher below these thresholds (p = 0.004; p = 0.010). Vaccination status was a weaker predictor of all three outcomes than both antibody thresholds. Antibody levels are a stronger predictor of outcome in CAD patients with COVID-19 than vaccination status, with 1200 BAU/mL being the more conservative threshold. Measuring anti-SARS-CoV-2 antibodies in CAD patients may ensure enhanced protection by providing timely booster vaccinations and identifying high-risk CAD patients at hospital admission.

Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing.

The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method.

Predictivity of the Prognostic Nutritional Index and Systemic Inflammation Index for All-Cause In-Hospital Mortality in Geriatric and Adult COVID-19 Inpatients.

Background: The prognostic nutritional index (PNI) and the systemic immune inflammation index (SII) have been used as simple risk-stratification predictors for COVID-19 severity and mortality in the general population. However, the associations between these indices and mortality might differ due to age-related changes such as inflammaging and several comorbid conditions in older patients. Therefore, we aimed to compare the predictivity of the PNI and SII for mortality among hospitalized older patients and patients under 65 years old. Methods: Patients hospitalized with COVID-19 from March 2020 to December 2020 were retrospectively included. The PNI and SII were calculated from hospital records within the first 48 h after admission. Data were evaluated in the whole group and according to age groups (≥65 < years). Receiver operating characteristic curves were drawn to evaluate the predictivity of the PNI and SII. Results: Out of 407 patients included in this study, 48.4% (n = 197) were older patients, and 51.6% (n = 210) were under 65 years old. For mortality, the area under the curve (AUC) of the PNI and SII in the adult group (<65 years) was 0.706 (95% CI 0.583-0.828) (p = 0.003) and 0.697 (95% CI 0.567-0.827) (p < 0.005), respectively. The AUC of the PNI and SII in the older group was 0.515 (95% CI 0.427-0.604) (p = 0.739) and 0.500 (95% CI 0.411-0.590) (p = 0.993). Conclusions: The accuracy of the PNI and SII in predicting mortality in adult COVID-19 patients seemed to be fair, but no association was found in geriatric patients in this study. The predictivity of the PNI and SII for mortality varies according to age groups.

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.

The effect of pre-hospital use of RAS inhibitors on COVID-19 mortality.

The effect of pre-hospital use of renin-angiotensin system (RAS) inhibitors (angiotensin-converting enzyme inhibitors (ACEis)/angiotensin receptor blockers (ARBs)) on clinical outcomes of hypertensive patients with COVID-19 has been questioned due to conflicting reports on this issue. After applying exclusion criteria, 175 COVID-19 hospitalized patients admitted to the Tishreen Hospital from January 1 to July 31, 2021 were retrospectively enrolled in this study. Baseline characteristics and in-hospital mortality rate were assessed between hypertensive (N = 91, 52%) and non-hypertensive (N = 84, 48%) patients, as well as between patients taking ACEis/ARBs and non-ACEis/ARBs within the hypertensive group. A lower mortality rate (51.2 versus 31.9%, p = 0.009) was observed in the hypertensive group (mean age 64.6 years, 64.8% males) compared to the non-hypertensive (mean age 62.6 years, 66.7% males). Patients' mortality in the non-hypertensive group was associated with lower blood oxygen saturation (SPO2 = 75 versus 86%, p = 0.002), increased levels of inflammatory (CRP, white blood cell and neutrophils count), and tissue/renal injury markers (LDH, urea, and creatinine). In the hypertensive group, a lower mortality rate was noted in the ACEis/ARBs group compared to the non-ACEis/ARBs (24.1 versus 45.5%, p = 0.036), and this was associated with a decrease in D-DIMER levels, although not significant (1723 versus 2683 ng/mL, p > 0.05). Death in the non-ACEis/ARBs group was associated with decreased SPO2 and tissue/renal injury markers (LDH, CK, AST, urea, and creatinine). We concluded that hypertension is not a direct cause of poor prognosis in COVID-19 patients and that multi-organ damage is a significant indicator of death from COVID-19. RAS inhibitors could improve the survival of hypertensive COVID-19 patients.

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.

An in-depth investigation of serum Krebs von den Lungen-6 and other biomarkers in COVID-19 severity and mortality.

Krebs von den Lungen-6 (KL-6) is a glycoprotein mainly expressed by type II pneumocytes and recently known as a lung injury biomarker. However, the number of studies is still limited, especially in Indonesian COVID-19 populations. Therefore, we aim to provide correlation, sensitivity, and specificity analyses of KL-6 and other biomarkers in Indonesian COVID-19 severity and mortality. We conducted a cross-sectional study involving adult COVID-19 patients at Universitas Airlangga Hospital, Surabaya, East Java, Indonesia, between March 26, 2021, and August 25, 2021. KL-6 and other biomarker levels were compared according to severity (severe versus non-severe) and mortality (non-survivor versus survivor). We also included the receiver operating characteristic analysis to define the optimal cut-off, sensitivity, and specificity of KL-6 to determine COVID-19 severity and mortality. We enrolled 78 COVID- 19 patients (23 non-survivors), including 39 non-severe and 39 severe patients. There was no significant difference in serum KL-6 levels, neither in severity nor mortality groups. KL-6 had the strongest positive correlations with ferritin in severe patients (r=0.313) and non-survivors (r=0.467). We observed that the best sensitivity was KL-6 combined with platelet-to- lymphocyte ratio (PLR) (0.818) in severe patients and with neutrophil-to-lymphocyte ratio (NLR)/PLR/ferritin/C-reactive protein (0.867) in non-survivors. In contrast, the best specificity was found when KL-6 was combined with NLR/D-dimer (0.750) in severe patients and with D-dimer (0.889) in non-survivors. Serum KL-6 is a useful auxiliary laboratory evaluation index for COVID-19 lung injury to depict its severity and mortality.

UKB.COVID19: an R package for UK Biobank COVID-19 data processing and analysis

COVID-19 caused by SARS-CoV-2 has resulted in a global pandemic with a rapidly developing global health and economic crisis. Variations in the disease have been observed and have been associated with the genomic sequence of either the human host or the pathogen. Worldwide scientists scrambled initially to recruit patient cohorts to try and identify risk factors. A resource that presented itself early on was the UK Biobank (UKBB), which is investigating the respective contributions of genetic predisposition and environmental exposure to the development of disease. To enable COVID-19 studies, UKBB is now receiving COVID-19 test data for their participants every two weeks. In addition, UKBB is delivering more frequent updates of death and hospital inpatient data (including critical care admissions) on the UKBB Data Portal. This frequently changing dataset requires a tool that can rapidly process and analyse up-to-date data. We developed an R package specifically for the UKBB COVID-19 data, which summarises COVID-19 test results, performs association tests between COVID-19 susceptibility/severity and potential risk factors such as age, sex, blood type, comorbidities and generates input files for genome-wide association studies (GWAS). By applying the R package to data released in April 2021, we found that age, body mass index, socioeconomic status and smoking are positively associated with COVID-19 susceptibility, severity, and mortality. Males are at a higher risk of COVID-19 infection than females. People staying in aged care homes have a higher chance of being exposed to SARS-CoV-2. By performing GWAS, we replicated the 3p21.31 genetic finding for COVID-19 susceptibility and severity. The ability to iteratively perform such analyses is highly relevant since the UKBB data is updated frequently. As a caveat, users must arrange their own access to the UKBB data to use the R package.