Individualizing Risk Prediction for Positive Coronavirus Disease 2019 Testing: Results From 11,672 Patients

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network. Leveraging the BP neural network's non-linear fitting capabilities, it captures the intricate relationship between project resource demand and supply. Additionally, the model employs self-learning to continuously adapt to new scenarios based on historical data, enabling more precise resource conflict risk assessments. Finally, the model's performance is analyzed. The results reveal that risks in scientific research project management primarily fall into six categories: material, equipment, personnel, financial, time, and organizational factors. This study's model algorithm exhibits the highest accuracy in predicting time-related risks, achieving 97.21%, surpassing convolutional neural network algorithms. Furthermore, the Root Mean Squared Error of the model algorithm remains stable at approximately 0.03, regardless of the number of hidden layer neurons, demonstrating excellent fitting capabilities. The developed BP neural network risk prediction framework in this study, while not directly influencing resource utilization efficiency or mitigating resource conflicts, aims to offer robust data support for research project managers when making decisions on resource allocation. The framework provides valuable insights through sensitivity analysis of organizational risks and other factors, with their relative importance reaching up to 20%. Further research should focus on defining specific strategies for various risk factors to effectively enhance resource utilization efficiency and manage resource conflicts.

Individualized Prediction of SARS-CoV-2 Infection in Mexico City Municipality during the First Six Waves of the Pandemic

After COVID-19 emerged, alternative methods to laboratory tests for the individualized prediction of SARS-CoV-2 were developed in several world regions. The objective of this investigation was to develop models for the individualized prediction of SARS-CoV-2 infection in a large municipality of Mexico. The study included data from 36,949 patients with suspected SARS-CoV-2 infection who received a diagnostic tested at health centers of the Alvaro Obregon Jurisdiction in Mexico City registered in the Epidemiological Surveillance System for Viral Respiratory Diseases (SISVER-SINAVE). The variables that were different between a positive test and a negative test were used to generate multivariate binary logistic regression models. There was a large variation in the prediction variables for the models of different pandemic waves. The models obtained an overall accuracy of 73% (63-82%), sensitivity of 52% (18-71%), and specificity of 84% (71-92%). In conclusion, the individualized prediction models of a positive COVID-19 test based on SISVER-SINAVE data had good performance. The large variation in the prediction variables for the models of different pandemic waves highlights the continuous change in the factors that influence the spread of COVID-19. These prediction models could be applied in early case identification strategies, especially in vulnerable populations.

Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model

This study aims to develop an interpretable prediction model based on explainable artificial intelligence to predict bacterial sepsis and discover important biomarkers. A total of 1572 adult patients, 560 of whom were sepsis positive and 1012 of whom were negative, who were admitted to the emergency department with suspicion of sepsis, were examined. We investigated the performance characteristics of sepsis biomarkers alone and in combination for confirmed sepsis diagnosis using Sepsis-3 criteria. Three different tree-based algorithms-Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost)-were used for sepsis prediction, and after examining comprehensive performance metrics, descriptions of the optimal model were obtained with the SHAP method. The XGBoost model achieved accuracy of 0.898 (0.868-0.929) and area under the ROC curve (AUC) of 0.940 (0.898-0.980) with a 95% confidence interval. The five biomarkers for predicting sepsis were age, respiratory rate, oxygen saturation, procalcitonin, and positive blood culture. SHAP results revealed that older age, higher respiratory rate, procalcitonin, neutrophil-lymphocyte count ratio, C-reactive protein, plaque, leukocyte particle concentration, as well as lower oxygen saturation, systolic blood pressure, and hemoglobin levels increased the risk of sepsis. As a result, the Explainable Artificial Intelligence (XAI)-based prediction model can guide clinicians in the early diagnosis and treatment of sepsis, providing more effective sepsis management and potentially reducing mortality rates and medical costs.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit

OBJECTIVE

Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness.

DESIGN

We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm.

SETTING AND PARTICIPANTS

We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022.

RESULTS

5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states.

CONCLUSION

We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.

Molecular docking as a tool for the discovery of novel insight about the role of acid sphingomyelinase inhibitors in SARS- CoV-2 infectivity

Recently, COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, caused > 6 million deaths. Symptoms included respiratory strain and complications, leading to severe pneumonia. SARS-CoV-2 attaches to the ACE-2 receptor of the host cell membrane to enter. Targeting the SARS-CoV-2 entry may effectively inhibit infection. Acid sphingomyelinase (ASMase) is a lysosomal protein that catalyzes the conversion of sphingolipid (sphingomyelin) to ceramide. Ceramide molecules aggregate/assemble on the plasma membrane to form "platforms" that facilitate the viral intake into the cell. Impairing the ASMase activity will eventually disrupt viral entry into the cell. In this review, we identified the metabolism of sphingolipids, sphingolipids' role in cell signal transduction cascades, and viral infection mechanisms. Also, we outlined ASMase structure and underlying mechanisms inhibiting viral entry 40 with the aid of inhibitors of acid sphingomyelinase (FIASMAs). In silico molecular docking analyses of FIASMAs with inhibitors revealed that dilazep (S = - 12.58 kcal/mol), emetine (S = - 11.65 kcal/mol), pimozide (S = - 11.29 kcal/mol), carvedilol (S = - 11.28 kcal/mol), mebeverine (S = - 11.14 kcal/mol), cepharanthine (S = - 11.06 kcal/mol), hydroxyzin (S = - 10.96 kcal/mol), astemizole (S = - 10.81 kcal/mol), sertindole (S = - 10.55 kcal/mol), and bepridil (S = - 10.47 kcal/mol) have higher inhibition activity than the candidate drug amiodarone (S = - 10.43 kcal/mol), making them better options for inhibition.

Protective effects of IL18-105G > A and IL18-137C > Ggenetic variants on severity of COVID-19

OBJECTIVE AND DESIGN

A cross-sectional study evaluated the IL18-105G > A (rs360717) and IL18-137C > G (rs187238) variants on Coronavírus Disease 2019 (COVID-19) severity.

SUBJECTS AND METHODS

528 patients with COVID-19 classifed with mild (n = 157), moderate (n = 63) and critical (n = 308) disease were genotpyed for the IL18-105G > A and IL18-137C > G variants.

RESULTS

We observed associations between severe + critical COVID-19 groups (reference group was mild COVID-19) and the IL18-105G > A (p = 0.008) and IL18-137C > G (p = 0.01) variants, which remained significant after adjusting for sex, ethnicity and age. Consequently, we have examined the associations between moderate + critical COVID-19 and the genotypes of both variants using different genetic models. The IL18-105G > A was associated with severe disease (moderate + critical), with effects of the GA genotype in the codominant [Odds ratio (OR), (95 % confidence interval) 0.55, 0.34-0.89, p = 0.015], overdominant (0.56, 0.35-0.89, p = 0.014) and dominant (0.60, 0.38-0.96, p = 0.031) models. IL18-105 GA coupled with age, chest computed tomograhy scan anormalities, body mass index, heart diseases, type 2 diabetes mellitus, hypertension, and inflammation may be used to predict the patients who develop severe disease with an accuracy of 84.3 % (sensitivity: 83.3 % and specificity: 86.5 %). Therefore, the presence of the IL18-105 A allele in homozygosis or heterozygosis conferred about 44.0 % of protection in the development of moderate and severe COVID-19. The IL18-137C > G variant was also associated with protective effects in the codominant (0.55, 0.34-0.89, p = 0.015), overdominant (0.57, 0.36-0.91, p = 0.018), and dominant models (0.59, 0.37-0.93, p = 0.025). Therefore, the IL18-137 G allele showed a protective effect against COVID-19 severity.

CONCLUSION

The IL18-105G > A and IL18-137C > Gvariants may contribute with protective effects for COVID-19 severity and the effects of IL18-137C > G may be modulating IL-18 production and Th1-mediated immune responses.

Vaccination against influenza viruses reduces infection, not hospitalization or death, from respiratory COVID-19: A systematic review and meta-analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and has brought a huge burden in terms of human lives. Strict social distance and influenza vaccination have been recommended to avoid co-infections between influenza viruses and SARS-CoV-2. Scattered reports suggested a protective effect of influenza vaccine on COVID-19 development and severity. We analyzed 51 studies on the capacity of influenza vaccination to affect infection with SARS-CoV-2, hospitalization, admission to Intensive Care Units (ICU), and mortality. All subjects taken into consideration did not receive any anti-SARS-CoV-2 vaccine, although their status with respect to previous infections with SARS-CoV-2 is not known. Comparison between vaccinated and not-vaccinated subjects for each of the four endpoints was expressed as odds ratio (OR), with 95% confidence intervals (CIs); all analyses were performed by DerSimonian and Laird model, and Hartung-Knapp model when studies were less than 10. In a total of 61 029 936 subjects from 33 studies, influenza vaccination reduced frequency of SARS-CoV-2 infection [OR plus 95% CI = 0.70 (0.65-0.77)]. The effect was significant in all studies together, in health care workers and in the general population; distance from influenza vaccination and the type of vaccine were also of importance. In 98 174 subjects from 11 studies, frequency of ICU admission was reduced with influenza vaccination [OR (95% CI) = 0.71 (0.54-0.94)]; the effect was significant in all studies together, in pregnant women and in hospitalized subjects. In contrast, in 4 737 328 subjects from 14 studies hospitalization was not modified [OR (95% CI) = 1.05 (0.82-1.35)], and in 4 139 660 subjects from 19 studies, mortality was not modified [OR (95% CI) = 0.76 (0.26-2.20)]. Our study emphasizes the importance of influenza vaccination in the protection against SARS-CoV-2 infection.

Determinants of SARS-CoV-2 nasopharyngeal testing in a rural community sample susceptible of first infection: the CHRIS COVID-19 study

To characterize COVID-19 epidemiology, numerous population-based studies have been undertaken to model the risk of SARS-CoV-2 infection. Less is known about what may drive the probability to undergo testing. Understanding how much testing is driven by contextual or individual conditions is important to delineate the role of individual behavior and to shape public health interventions and resource allocation. In the Val Venosta/Vinschgau district (South Tyrol, Italy), we conducted a population-representative longitudinal study on 697 individuals susceptible to first infection who completed 4,512 repeated online questionnaires at four-week intervals between September 2020 and May 2021. Mixed-effects logistic regression models were fitted to investigate associations of self-reported SARS-CoV-2 testing with individual characteristics (social, demographic, and biological) and contextual determinants. Testing was associated with month of reporting, reflecting the timing of both the pandemic intensity and public health interventions, COVID-19-related symptoms (odds ratio, OR:8.26; 95% confidence interval, CI:6.04-11.31), contacts with infected individuals within home (OR:7.47, 95%CI:3.81-14.62) or outside home (OR:9.87, 95%CI:5.78-16.85), and being retired (OR:0.50, 95%CI:0.34-0.73). Symptoms and next within- and outside-home contacts were the leading determinants of swab testing predisposition in the most acute phase of the pandemics. Testing was not associated with age, sex, education, comorbidities, or lifestyle factors. In the study area, contextual determinants reflecting the course of the pandemic were predominant compared to individual sociodemographic characteristics in explaining the SARS-CoV-2 probability of testing. Decision makers should evaluate whether the intended target groups were correctly prioritized by the testing campaign.

Methodologic Issues Specific to Prediction Model Development and Evaluation

Developing and evaluating statistical prediction models is challenging, and many pitfalls can arise. This article identifies what the authors believe are some common methodologic concerns that may be encountered. We describe each problem and make suggestions regarding how to address them. The hope is that this article will result in higher-quality publications of statistical prediction models.

Intra-host mutation rate of acute SARS-CoV-2 infection during the initial pandemic wave

SARS-CoV-2 mutation is minimized through a proofreading function encoded by NSP-14. Most estimates of the SARS-CoV-2 mutation rate are derived from population based sequence data. Our understanding of SARS-CoV-2 evolution might be enhanced through analysis of intra-host viral mutation rates in specific populations. Viral genome analysis was performed between paired samples and mutations quantified at allele frequencies (AF) ≥ 0.25, ≥ 0.5 and ≥ 0.75. Mutation rate was determined employing F81 and JC69 evolution models and compared between isolates with (ΔNSP-14) and without (wtNSP-14) non-synonymous mutations in NSP-14 and by patient comorbidity. Forty paired samples with median interval of 13 days [IQR 8.5-20] were analyzed. The estimated mutation rate by F81 modeling was 93.6 (95%CI 90.8-96.4], 40.7 (95%CI 38.9-42.6) and 34.7 (95%CI 33.0-36.4) substitutions/genome/year at AF ≥ 0.25, ≥ 0.5, ≥ 0.75 respectively. Mutation rate in ΔNSP-14 were significantly elevated at AF ≥ 0.25 vs wtNSP-14. Patients with immune comorbidities had higher mutation rate at all allele frequencies. Intra-host SARS-CoV-2 mutation rates are substantially higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated mutation rate at low AF. Immunosuppressed patients have elevated mutation rate at all AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling.

Quantifying association and disparities between diabetes complications and COVID-19 outcomes: A retrospective study using electronic health records

BACKGROUND

Despite established relationships between diabetic status and an increased risk for COVID-19 severe outcomes, there is a limited number of studies examining the relationships between diabetes complications and COVID-19-related risks. We use the Adapted Diabetes Complications Severity Index to define seven diabetes complications. We aim to understand the risk for COVID-19 infection, hospitalization, mortality, and longer length of stay of diabetes patients with complications.

METHODS

We perform a retrospective case-control study using Electronic Health Records (EHRs) to measure differences in the risks for COVID-19 severe outcomes amongst those with diabetes complications. Using multiple logistic regression, we calculate adjusted odds ratios (OR) for COVID-19 infection, hospitalization, and in-hospital mortality of the case group (patients with diabetes complications) compared to a control group (patients without diabetes). We also calculate adjusted mean difference in length of stay between the case and control groups using multiple linear regression.

RESULTS

Adjusting demographics and comorbidities, diabetes patients with renal complications have the highest odds for COVID-19 infection (OR = 1.85, 95% CI = [1.71, 1.99]) while those with metabolic complications have the highest odds for COVID-19 hospitalization (OR = 5.58, 95% CI = [3.54, 8.77]) and in-hospital mortality (OR = 2.41, 95% CI = [1.35, 4.31]). The adjusted mean difference (MD) of hospital length-of-stay for diabetes patients, especially those with cardiovascular (MD = 0.94, 95% CI = [0.17, 1.71]) or peripheral vascular (MD = 1.72, 95% CI = [0.84, 2.60]) complications, is significantly higher than non-diabetes patients. African American patients have higher odds for COVID-19 infection (OR = 1.79, 95% CI = [1.66, 1.92]) and hospitalization (OR = 1.62, 95% CI = [1.39, 1.90]) than White patients in the general diabetes population. However, White diabetes patients have higher odds for COVID-19 in-hospital mortality. Hispanic patients have higher odds for COVID-19 infection (OR = 2.86, 95% CI = [2.42, 3.38]) and shorter mean length of hospital stay than non-Hispanic patients in the general diabetes population. Although there is no significant difference in the odds for COVID-19 hospitalization and in-hospital mortality between Hispanic and non-Hispanic patients in the general diabetes population, Hispanic patients have higher odds for COVID-19 hospitalization (OR = 1.83, 95% CI = [1.16, 2.89]) and in-hospital mortality (OR = 3.69, 95% CI = [1.18, 11.50]) in the diabetes population with no complications.

CONCLUSIONS

The presence of diabetes complications increases the risks of COVID-19 infection, hospitalization, and worse health outcomes with respect to in-hospital mortality and longer hospital length of stay. We show the presence of health disparities in COVID-19 outcomes across demographic groups in our diabetes population. One such disparity is that African American and Hispanic diabetes patients have higher odds of COVID-19 infection than White and Non-Hispanic diabetes patients, respectively. Furthermore, Hispanic patients might have less access to the hospital care compared to non-Hispanic patients when longer hospitalizations are needed due to their diabetes complications. Finally, diabetes complications, which are generally associated with worse COVID-19 outcomes, might be predominantly determining the COVID-19 severity in those infected patients resulting in less demographic differences in COVID-19 hospitalization and in-hospital mortality.

Association of Metformin, Dipeptidyl Dipeptidase-4 Inhibitors, and Insulin with Coronavirus Disease 2019-Related Hospital Outcomes in Patients with Type 2 Diabetes

OBJECTIVE

The effects of diabetes medications on COVID-19 hospitalization outcomes have not been consistent. We sought to determine the effect of metformin, dipeptidyl peptidase-4 inhibitors (DPP-4i), and insulin on admission to the intensive care unit (ICU), need for assisted ventilation, development of renal insufficiency, and mortality in patients admitted with COVID-19 infection after controlling for clinical variables and other relevant diabetes-related medications in patients with type 2 diabetes mellitus (DM).

METHODS

This was a retrospective study of patients hospitalized with COVID-19 from a single hospital system. Univariate and multivariate analyses were performed that included demographic data, glycated hemoglobin, kidney function, smoking status, insurance, Charlson comorbidity index, number of diabetes medications, and use of angiotensin-converting enzyme inhibitors and statin prior to admission and glucocorticoids during admission.

RESULTS

A total of 529 patients with type 2 DM were included in our final analysis. Neither metformin nor DPP4i prescription was associated with ICU admission, need for assisted ventilation, or mortality. Insulin prescription was associated with increased ICU admission but not with need for assisted ventilation or mortality. There was no association of any of these medications with development of renal insufficiency.

CONCLUSIONS

In this population, limited to type 2 DM and controlled for multiple variables that have not been consistently studied (such as a measure of general health, glycated hemoglobin, and insurance status), insulin prescription was associated with increased ICU admission. Metformin and DPP4i prescriptions did not have an association with the outcomes.

SARS-CoV-2 and Its Bacterial Co- or Super-Infections Synergize to Trigger COVID-19 Autoimmune Cardiopathies

Autoimmune cardiopathies (AC) following COVID-19 and vaccination against SARS-CoV-2 occur at significant rates but are of unknown etiology. This study investigated the possible roles of viral and bacterial mimicry, as well as viral-bacterial co-infections, as possible inducers of COVID-19 AC using proteomic methods and enzyme-linked immunoadsorption assays. BLAST and LALIGN results of this study demonstrate that SARS-CoV-2 shares a significantly greater number of high quality similarities to some cardiac protein compared with other viruses; that bacteria such as Streptococci, Staphylococci and Enterococci also display very significant similarities to cardiac proteins but to a different set than SARS-CoV-2; that the importance of these similarities is largely validated by ELISA experiments demonstrating that polyclonal antibodies against SARS-CoV-2 and COVID-19-associated bacteria recognize cardiac proteins with high affinity; that to account for the range of cardiac proteins targeted by autoantibodies in COVID-19-associated autoimmune myocarditis, both viral and bacterial triggers are probably required; that the targets of the viral and bacterial antibodies are often molecularly complementary antigens such as actin and myosin, laminin and collagen, or creatine kinase and pyruvate kinase, that are known to bind to each other; and that the corresponding viral and bacterial antibodies recognizing these complementary antigens also bind to each other with high affinity as if they have an idiotype-anti-idiotype relationship. These results suggest that AC results from SARS-CoV-2 infections or vaccination complicated by bacterial infections. Vaccination against some of these bacterial infections, such as Streptococci and Haemophilus, may therefore decrease AC risk, as may the appropriate and timely use of antibiotics among COVID-19 patients and careful screening of vaccinees for signs of infection such as fever, diarrhea, infected wounds, gum disease, etc.

The Effect of Combining the COVID-19 Vaccine with the Seasonal Influenza Vaccine on Reducing COVID-19 Vaccine Rejection Among Libyans

BACKGROUND

Coronavirus disease 2019 (COVID-19) vaccine coverage remains low in Libya compared to other countries in the Eastern Mediterranean Region. This study aimed to evaluate the willingness of the general public in Libya to receive COVID-19 and seasonal influenza vaccines. Additionally, the study aimed to investigate the potential effect of combining the two vaccines to reduce COVID-19 vaccine rejection.

METHODS

An anonymous nationwide online cross-sectional survey was carried out from 1st September to 16th October 2022. Libyans aged 18 years or older were recruited using convenience and snowball sampling approaches. The participants were surveyed for sociodemographic information, health status, and vaccination attitude towards COVID-19 and seasonal influenza vaccines.

RESULTS

A total of 2484 participants formed the final study sample: 68.7% were females, 39.4% were aged 18-25 years, 50.4% were single, 32.5% had previous COVID-19 infection, and 47.2% experienced COVID-19 death among relatives. Three-fourths of the respondents showed COVID-19 vaccine rejection: 57.3% did not receive COVID-19 vaccination, 10.1% would not complete the primary vaccination series, and 7.8% refused booster doses. About 55.0% rejected seasonal influenza vaccination, while 1.9% reported influenza vaccine uptake and 21.2% were willing to get the influenza vaccine for the first time. Additionally, 18.8% had already received influenza vaccination in the last year and intended to get the vaccine this season, while 3.3% were unwilling to get influenza vaccination this year despite receiving it in the last influenza season. Age, sex, and occupation were significantly associated with COVID-19 and influenza vaccine rejection. Rejection of COVID-19 vaccination decreased if its combination with influenza vaccine as a single dose was suggested, with 28.2% of the COVID-19 vaccine rejector group accepting the combined vaccine as it would be safer (50.9%), needing fewer injections (24.0%), would be more effective (19.1%), and would be less expensive (3%). Approximately 73.0% of the COVID-19 vaccine rejector group refused this combination due to fear of side effects (48.7%), absence of published studies on this combination (29.8%), and considering this combination as useless (11.2%).

CONCLUSION

In Libya, the prevalence of COVID-19 vaccine rejection was high, while the rejection of seasonal influenza vaccination was relatively lower. If influenza and COVID-19 vaccines are administered simultaneously as a single injection, this may reduce the rejection of the COVID-19 vaccine due to better-perceived vaccine safety and efficacy besides being more convenient in terms of the number of injections and cost.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

In the SARS-CoV-2 Pandora Pandemic: Can the Stance of Premorbid Intestinal Innate Immune System as Measured by Fecal Adnab-9 Binding of p87:Blood Ferritin, Yielding the FERAD Ratio, Predict COVID-19 Susceptibility and Survival in a Prospective Population Database?

SARS-CoV-2 severity predictions are feasible, though individual susceptibility is not. The latter prediction allows for planning vaccination strategies and the quarantine of vulnerable targets. Ironically, the innate immune response (InImS) is both an antiviral defense and the potential cause of adverse immune outcomes. The competition for iron has been recognized between both the immune system and invading pathogens and expressed in a ratio of ferritin divided by p87 (as defined by the Adnab-9 ELISA stool-binding optical density, minus the background), known as the FERAD ratio. Associations with the FERAD ratio may allow predictive modeling for the susceptibility and severity of disease. We evaluated other potential COVID-19 biomarkers prospectively. Patients with PCR+ COVID-19 tests (Group 1; n = 28) were compared to three other groups. In Group 2 (n = 36), and 13 patients displayed COVID-19-like symptoms but had negative PCR or negative antibody tests. Group 3 (n = 90) had no symptoms and were negative when routinely PCR-tested before medical procedures. Group 4 (n = 2129) comprised a pool of patients who had stool tests and symptoms, but their COVID-19 diagnoses were unknown; therefore, they were chosen to represent the general population. Twenty percent of the Group 4 patients (n = 432) had sufficient data to calculate their FERAD ratios, which were inversely correlated with the risk of COVID-19 in the future. In a case report of a neonate, we studied three biomarkers implicated in COVID-19, including p87, Src (cellular-p60-sarcoma antigen), and Abl (ABL-proto-oncogene 2). The InImS of the first two were positively correlated. An inverse correlation was found between ferritin and lysozyme in serum (p < 0.05), suggesting that iron could have impaired an important innate immune system anti-viral effector and could partially explain future COVID-19 susceptibility.

An Elaboration on Sample Size Planning for Performing a One-Sample Sensitivity and Specificity Analysis by Basing on Calculations on a Specified 95% Confidence Interval Width

Sample size calculation based on a specified width of 95% confidence interval will offer researchers the freedom to set the level of accuracy of the statistics that they aim to achieve for a particular study. This paper provides a description of the general conceptual context for performing sensitivity and specificity analysis. Subsequently, sample size tables for sensitivity and specificity analysis based on a specified 95% confidence interval width is then provided. Such recommendations for sample size planning are provided based on two different scenarios: one for a diagnostic purpose and another for a screening purpose. Further discussion on all the other relevant considerations for the determination of a minimum sample size requirement and on how to draft the sample size statement for performing sensitivity and specificity analysis are also provided.

Immunometabolic rewiring in long COVID patients with chronic headache

Almost 20% of patients with COVID-19 experience long-term effects, known as post-COVID condition or long COVID. Among many lingering neurologic symptoms, chronic headache is the most common. Despite this health concern, the etiology of long COVID headache is still not well characterized. Here, we present a longitudinal multi-omics analysis of blood leukocyte transcriptomics, plasma proteomics and metabolomics of long COVID patients with chronic headache. Long COVID patients experienced a state of hyper-inflammation prior to chronic headache onset and maintained persistent inflammatory activation throughout the progression of chronic headache. Metabolomic analysis also revealed augmented arginine and lipid metabolisms, skewing towards a nitric oxide-based pro-inflammation. Furthermore, metabolisms of neurotransmitters including serotonin, dopamine, glutamate, and GABA were markedly dysregulated during the progression of long COVID headache. Overall, these findings illustrate the immuno-metabolomics landscape of long COVID patients with chronic headache, which may provide insights to potential therapeutic interventions.

From Co-Infections to Autoimmune Disease via Hyperactivated Innate Immunity: COVID-19 Autoimmune Coagulopathies, Autoimmune Myocarditis and Multisystem Inflammatory Syndrome in Children

Neutrophilia and the production of neutrophil extracellular traps (NETs) are two of many measures of increased inflammation in severe COVID-19 that also accompany its autoimmune complications, including coagulopathies, myocarditis and multisystem inflammatory syndrome in children (MIS-C). This paper integrates currently disparate measures of innate hyperactivation in severe COVID-19 and its autoimmune complications, and relates these to SARS-CoV-2 activation of innate immunity. Aggregated data include activation of Toll-like receptors (TLRs), nucleotide-binding oligomerization domain (NOD) receptors, NOD leucine-rich repeat and pyrin-domain-containing receptors (NLRPs), retinoic acid-inducible gene I (RIG-I) and melanoma-differentiation-associated gene 5 (MDA-5). SARS-CoV-2 mainly activates the virus-associated innate receptors TLR3, TLR7, TLR8, NLRP3, RIG-1 and MDA-5. Severe COVID-19, however, is characterized by additional activation of TLR1, TLR2, TLR4, TLR5, TLR6, NOD1 and NOD2, which are primarily responsive to bacterial antigens. The innate activation patterns in autoimmune coagulopathies, myocarditis and Kawasaki disease, or MIS-C, mimic those of severe COVID-19 rather than SARS-CoV-2 alone suggesting that autoimmunity follows combined SARS-CoV-2-bacterial infections. Viral and bacterial receptors are known to synergize to produce the increased inflammation required to support autoimmune disease pathology. Additional studies demonstrate that anti-bacterial antibodies are also required to account for known autoantigen targets in COVID-19 autoimmune complications.

Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study

Background

Acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2) is characterised by heterogeneous levels of disease severity. It is not necessarily apparent whether a patient will develop a severe disease or not. This cross-sectional study explores whether acoustic properties of the cough sound of patients with coronavirus disease (COVID-19), the illness caused by SARS-CoV-2, correlate with their disease and pneumonia severity, with the aim of identifying patients with a severe disease.

Methods

Voluntary cough sounds were recorded using a smartphone in 70 COVID-19 patients within the first 24 h of their hospital arrival, between April 2020 and May 2021. Based on gas exchange abnormalities, patients were classified as mild, moderate, or severe. Time- and frequency-based variables were obtained from each cough effort and analysed using a linear mixed-effects modelling approach.

Results

Records from 62 patients (37% female) were eligible for inclusion in the analysis, with mild, moderate, and severe groups consisting of 31, 14 and 17 patients respectively. 5 of the parameters examined were found to be significantly different in the cough of patients at different disease levels of severity, with a further 2 parameters found to be affected differently by the disease severity in men and women.

Conclusions

We suggest that all these differences reflect the progressive pathophysiological alterations occurring in the respiratory system of COVID-19 patients, and potentially would provide an easy and cost-effective way to initially stratify patients, identifying those with more severe disease, and thereby most effectively allocate healthcare resources.

Validation of the RIM Score-COVID in the Spanish SEMI-COVID-19 Registry

The significant impact of COVID-19 worldwide has made it necessary to develop tools to identify patients at high risk of severe disease and death. This work aims to validate the RIM Score-COVID in the SEMI-COVID-19 Registry. The RIM Score-COVID is a simple nomogram with high predictive capacity for in-hospital death due to COVID-19 designed using clinical and analytical parameters of patients diagnosed in the first wave of the pandemic. The nomogram uses five variables measured on arrival to the emergency department (ED): age, sex, oxygen saturation, C-reactive protein level, and neutrophil-to-platelet ratio. Validation was performed in the Spanish SEMI-COVID-19 Registry, which included consecutive patients hospitalized with confirmed COVID-19 in Spain. The cohort was divided into three time periods: T1 from February 1 to June 10, 2020 (first wave), T2 from June 11 to December 31, 2020 (second wave, pre-vaccination period), and T3 from January 1 to December 5, 2021 (vaccination period). The model's accuracy in predicting in-hospital COVID-19 mortality was assessed using the area under the receiver operating characteristics curve (AUROC). Clinical and laboratory data from 22,566 patients were analyzed: 15,976 (70.7%) from T1, 4,233 (18.7%) from T2, and 2,357 from T3 (10.4%). AUROC of the RIM Score-COVID in the entire SEMI-COVID-19 Registry was 0.823 (95%CI 0.819-0.827) and was 0.834 (95%CI 0.830-0.839) in T1, 0.792 (95%CI 0.781-0.803) in T2, and 0.799 (95%CI 0.785-0.813) in T3. The RIM Score-COVID is a simple, easy-to-use method for predicting in-hospital COVID-19 mortality that uses parameters measured in most EDs. This tool showed good predictive ability in successive disease waves.

T Cell Receptor Sequences Amplified during Severe COVID-19 and Multisystem Inflammatory Syndrome in Children Mimic SARS-CoV-2, Its Bacterial Co-Infections and Host Autoantigens

Published hypervariable region V-beta T cell receptor (TCR) sequences were collected from people with severe COVID-19 characterized by having various autoimmune complications, including blood coagulopathies and cardiac autoimmunity, as well as from patients diagnosed with the Kawasaki disease (KD)-like multisystem inflammatory syndrome in children (MIS-C). These were compared with comparable published v-beta TCR sequences from people diagnosed with KD and from healthy individuals. Since TCR V-beta sequences are supposed to be complementary to antigens that induce clonal expansion, it was surprising that only a quarter of the TCR sequences derived from severe COVID-19 and MIS-C patients mimicked SARS-CoV-2 proteins. Thirty percent of the KD-derived TCR mimicked coronaviruses other than SARS-CoV-2. In contrast, only three percent of the TCR sequences from healthy individuals and those diagnosed with autoimmune myocarditis displayed similarities to any coronavirus. In each disease, significant increases were found in the amount of TCRs from healthy individuals mimicking specific bacterial co-infections (especially Enterococcus faecium, Staphylococcal and Streptococcal antigens) and host autoantigens targeted by autoimmune diseases (especially myosin, collagen, phospholipid-associated proteins, and blood coagulation proteins). Theoretical explanations for these surprising observations and implications to unravel the causes of autoimmune diseases are explored.

Sensibility and Specificity of the VitaPCR™ SARS-CoV-2 Assay for the Rapid Diagnosis of COVID-19 in Older Adults in the Emergency Department

(1) Background: During the COVID-19 pandemic, rapid and reliable diagnostic tools are needed for detecting SARS-CoV-2 infection in urgent cases at admission to the hospital. We aimed to assess the performances of the rapid molecular VitaPCR™ test (Menarini Diagnostics) in a sample of older adults admitted to the Emergency Department of two Italian hospitals (2) Methods: The comparison between the rapid VitaPCR™ and the RT-PCR was performed in 1695 samples. Two naso-pharyngeal swab samplings from each individual were obtained and processed using the VitaPCR™ and the RT-PCR for the detection of SARS-CoV-2 (3) Results: VitaPCR™ exhibited good precision (<3% CV) and an almost perfect overall agreement (Cohen’s K = 0.90) with the RT-PCR. The limit of detection of the VitaPCR™ was 4.1 copies/µL. Compared to the RT-PCR, the sensitivity, the specificity, and the positive and negative predictive values of VitaPCR™ were 83.4%, 99.9%, 99.2% and 98.3%, respectively (4) Conclusions: The VitaPCR™ showed similar sensitivity and specificity to other molecular-based rapid tests. This study suggests that the VitaPCR™ can allow the rapid management of patients within the Emergency Department. Nevertheless, it is advisable to obtain a negative result by a RT-PCR assay before admitting a patient to a regular ward.

Assessing the impact of coronavirus disease 2019 on mortality: a population-based, matched case-control study

OBJECTIVES

Estimating the isolated effect of coronavirus disease 2019 (COVID-19) on the risk of mortality is challenging. We aimed to determine whether COVID-19 was associated with high rates of mortality independently of age, sex and underlying disorders.

METHODS

A population-based, matched, case-control study of adults insured by Clalit Health Services was performed. Cases were defined as patients who died of all causes between July and December 2020. Each case was matched in a ratio of 1:1 with a living control based on age, sex and co-morbidities. An unconditional logistic regression analysis was performed to identify independent risk factors for mortality.

RESULTS

A total of 2874 patients who died were successfully matched with 2874 living controls. The prevalence of COVID-19 was higher among the patients who died than among the controls (13.5% [387/2874] vs. 4% [115/2874], respectively; OR, 3.73; 95% CI, 3.01-4.63; p < 0.001). A significantly increased odds of mortality was also observed in patients with COVID-19 without underlying diseases (OR, 3.67; 95% CI, 2.58-5.23) and in patients with COVID-19 and underlying diseases (OR, 3.77; 95% CI, 2.87-4.94). A multi-variate logistic analysis showed that COVID-19 (OR, 2.01; 95% CI, 1.07-3.77), low socio-economic status (OR, 1.36; 95% CI, 1.02-1.82), dementia (OR, 2.50; 95% CI, 2.10-3.01), smoking (OR, 1.35; 95% CI, 1.13-1.63) and an interaction variable of age >80 years and COVID-19 (OR, 2.27; 95% CI, 1.14-4.54) were independent risk factors for mortality, whereas influenza vaccination and high body mass index were associated with lower rates of mortality.

CONCLUSION

Testing positive for COVID-19 increased the risk of death three folds, regardless of underlying disorders. These results emphasize the effect of COVID-19 on mortality during the early period of the COVID-19 outbreak, when no vaccines or effective therapeutics were available.

Multiple sclerosis and COVID-19: A retrospective study in Iran

OBJECTIVES

Previous studies suggested a higher rate of COVID-19 infection in patients with multiple sclerosis than in the general population, and limited studies addressed the impact of COVID-19 and its vaccination in patients with multiple sclerosis in Iran. We decided to investigate the factors associated with COVID-19 infection, the effects and side effects of the COVID-19 vaccination in patients with multiple sclerosis (MS).

METHODS

We used the data of the patients with multiple sclerosis registered in a referral clinic in Kerman, one of the large cities in Iran (a population of 537,000 inhabitants), to explore the association between demographic variables, the history of COVID-19 vaccination, and the clinical outcomes.

RESULTS

Of the 367 participants in this study, 88.3% received the COVID-19 vaccine, 35.4% were confirmed COVID-19 cases, and the incidence of COVID-19 was much higher before vaccination (24.5% before vaccination versus 10.1% after vaccination). The multivariable logistic regression model showed that male gender (OR = 2.64, 95% confidence interval: 1.21, 5.74) and current employment (OR = 3.04, 95% confidence interval: 1.59, 5.80) were associated with an increased risk of COVID-19. The only factor associated with the adverse effects of COVID-19 vaccination was the type of vaccine (AstraZeneca).

CONCLUSION

Our findings showed that the vaccination protected MS cases considerably against COVID-19. In addition, the side effects of the vaccines were not noticeably high in these cases as well. Among all COVID-19 vaccines, AstraZeneca had the most common side effects, so people must be aware of them before vaccination. The male gender and employment were the most important variables in the prevalence of COVID-19 in patients with multiple sclerosis in our study.

The impact of influenza vaccination on surgical outcomes in COVID-19 positive patients: An analysis of 43,580 patients

BACKGROUND

Multiple recent studies suggest a possible protective effect of the influenza vaccine against severe acute respiratory coronavirus 2 (SARS-CoV-2). This effect has yet to be evaluated in surgical patients. This study utilizes a continuously updated federated electronic medical record (EMR) network (TriNetX, Cambridge, MA) to analyze the influence of the influenza vaccine against post-operative complications in SARS-CoV-2-positive patients.

METHODS

The de-identified records of 73,341,020 patients globally were retrospectively screened. Two balanced cohorts totaling 43,580 surgical patients were assessed from January 2020-January 2021. Cohort One received the influenza vaccine six months-two weeks prior to SARS-CoV-2-positive diagnosis, while Cohort Two did not. Post-operative complications within 30, 60, 90, and 120 days of undergoing surgery were analyzed using common procedural terminology(CPT) codes. Outcomes were propensity score matched for characteristics including age, race, gender, diabetes, obesity, and smoking.

RESULTS

SARS-CoV-2-positive patients receiving the influenza vaccine experienced significantly decreased risks of sepsis, deep vein thrombosis, dehiscence, acute myocardial infarction, surgical site infections, and death across multiple time points(p<0.05, Bonferroni Correction p = 0.0011). Number needed to vaccinate (NNV) was calculated for all significant and nominally significant findings.

CONCLUSION

Our analysis examines the potential protective effect of influenza vaccination in SARS-CoV-2-positive surgical patients. Limitations include this study's retrospective nature and reliance on accuracy of medical coding. Future prospective studies are warranted to confirm our findings.

Influenza and Pneumococcal Vaccination and the Risk of COVID-19: A Systematic Review and Meta-Analysis

A number of studies have investigated the potential on-specific effects of some routinely administered vaccines (e.g., influenza, pneumococcal) on COVID-19 related outcomes, with contrasting results. In order to elucidate this discrepancy, we conducted a systematic review and meta-analysis to assess the association between seasonal influenza vaccination and pneumococcal vaccination with SARS-CoV-2 infection and its clinical outcomes. PubMed and medRxiv databases were searched up to April 2022. A random effects model was used in the meta-analysis to pool odds ratio (OR) and adjusted estimates with 95% confidence intervals (CIs). Heterogeneity was quantitatively assessed using the Cochran's Q and the I2 index. Subgroup analysis, sensitivity analysis and assessment of publication bias were performed for all outcomes. In total, 38 observational studies were included in the meta-analysis and there was substantial heterogeneity. Influenza and pneumococcal vaccination were associated with lower risk of SARS-CoV-2 infection (OR: 0.80, 95% CI: 0.75-0.86 and OR: 0.70, 95% CI: 0.57-0.88, respectively). Regarding influenza vaccination, it seems that the majority of studies did not properly adjust for all potential confounders, so when the analysis was limited to studies that adjusted for age, gender, comorbidities and socioeconomic indices, the association diminished. This is not the case regarding pneumococcal vaccination, for which even after adjustment for such factors the association persisted. Regarding harder endpoints such as ICU admission and death, current data do not support the association. Possible explanations are discussed, including trained immunity, inadequate matching for socioeconomic indices and possible coinfection.

Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test

BACKGROUND

To update and internally validate a model to predict children and young people (CYP) most likely to experience long COVID (i.e. at least one impairing symptom) 3 months after SARS-CoV-2 PCR testing and to determine whether the impact of predictors differed by SARS-CoV-2 status.

METHODS

Data from a nationally matched cohort of SARS-CoV-2 test-positive and test-negative CYP aged 11-17 years was used. The main outcome measure, long COVID, was defined as one or more impairing symptoms 3 months after PCR testing. Potential pre-specified predictors included SARS-CoV-2 status, sex, age, ethnicity, deprivation, quality of life/functioning (five EQ-5D-Y items), physical and mental health and loneliness (prior to testing) and number of symptoms at testing. The model was developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping and the final model was adjusted for overfitting.

RESULTS

A total of 7139 (3246 test-positives, 3893 test-negatives) completing a questionnaire 3 months post-test were included. 25.2% (817/3246) of SARS-CoV-2 PCR-positives and 18.5% (719/3893) of SARS-CoV-2 PCR-negatives had one or more impairing symptoms 3 months post-test. The final model contained SARS-CoV-2 status, number of symptoms at testing, sex, age, ethnicity, physical and mental health, loneliness and four EQ-5D-Y items before testing. Internal validation showed minimal overfitting with excellent calibration and discrimination measures (optimism-adjusted calibration slope: 0.96575; C-statistic: 0.83130).

CONCLUSIONS

We updated a risk prediction equation to identify those most at risk of long COVID 3 months after a SARS-CoV-2 PCR test which could serve as a useful triage and management tool for CYP during the ongoing pandemic. External validation is required before large-scale implementation.

Antioxidants affect endoplasmic reticulum stress-related diseases

The endoplasmic reticulum (ER) is a complex multifunctional organelle that maintains cell homeostasis. Intrinsic and extrinsic factors alter ER functions, including the rate of protein folding that triggers the accumulation of misfolded proteins and alters homeostasis, thus generating stress in the ER, which activates the unfolded protein response (UPR) pathway to promote cell survival and restore their homeostasis; however, if the damage is not corrected, it could also trigger cell death. In addition, ER stress and oxidative stress are closely related because excessive production of reactive oxygen species (ROS), a well-known inducer of ER stress, promotes the accumulation of misfolded proteins; at the same time, the ER stress enhances ROS production, generating a pathological cycle. Furthermore, it has been described that the dysregulation of the UPR contributes to the progression of various diseases, so the use of compounds capable of regulating ER stress, such as antioxidants, has been used in several experimental models of diseases to alleviate the damage induced by the maladaptive signaling of the UPR, the mechanism of action of antioxidants generally is dose-dependent, and it is specific in each tissue and pathology, could decrease or enhance specific proteins of the UPR to have beneficial or detrimental effects.

A multistage multimodal deep learning model for disease severity assessment and early warnings of high-risk patients of COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) has caused massive infections and large death tolls worldwide. Despite many studies on the clinical characteristics and the treatment plans of COVID-19, they rarely conduct in-depth prognostic research on leveraging consecutive rounds of multimodal clinical examination and laboratory test data to facilitate clinical decision-making for the treatment of COVID-19. To address this issue, we propose a multistage multimodal deep learning (MMDL) model to (1) first assess the patient's current condition (i.e., the mild and severe symptoms), then (2) give early warnings to patients with mild symptoms who are at high risk to develop severe illness. In MMDL, we build a sequential stage-wise learning architecture whose design philosophy embodies the model's predicted outcome and does not only depend on the current situation but also the history. Concretely, we meticulously combine the latest round of multimodal clinical data and the decayed past information to make assessments and predictions. In each round (stage), we design a two-layer multimodal feature extractor to extract the latent feature representation across different modalities of clinical data, including patient demographics, clinical manifestation, and 11 modalities of laboratory test results. We conduct experiments on a clinical dataset consisting of 216 COVID-19 patients that have passed the ethical review of the medical ethics committee. Experimental results validate our assumption that sequential stage-wise learning outperforms single-stage learning, but history long ago has little influence on the learning outcome. Also, comparison tests show the advantage of multimodal learning. MMDL with multimodal inputs can beat any reduced model with single-modal inputs only. In addition, we have deployed the prototype of MMDL in a hospital for clinical comparison tests and to assist doctors in clinical diagnosis.

Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Primary Health Care Electronic Records

BACKGROUND

A possible link between influenza immunization and susceptibility to the complications of COVID-19 infection has been previously suggested owing to a boost in the immunity against SARS-CoV-2.

OBJECTIVE

This study aimed to investigate whether individuals with COVID-19 could have benefited from vaccination against influenza. We hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2.

METHODS

We performed a population-based cohort study including all patients with COVID-19 with registered entries in the primary health care (PHC) electronic records during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) in Catalonia, Spain. We compared individuals who took an influenza vaccine before being infected with COVID-19, with those who had not taken one. Data were obtained from Information System for Research in Primary Care, capturing PHC information of 5.8 million people from Catalonia. The main outcomes assessed during follow-up were a diagnosis of pneumonia, hospital admission, and mortality.

RESULTS

We included 309,039 individuals with COVID-19 and compared them on the basis of their influenza immunization status, with 114,181 (36.9%) having been vaccinated at least once and 194,858 (63.1%) having never been vaccinated. In total, 21,721 (19%) vaccinated individuals and 11,000 (5.7%) unvaccinated individuals had at least one of their outcomes assessed. Those vaccinated against influenza at any time (odds ratio [OR] 1.14, 95% CI 1.10-1.19), recently (OR 1.13, 95% CI 1.10-1.18), or recurrently (OR 1.10, 95% CI 1.05-1.15) before being infected with COVID-19 had a higher risk of presenting at least one of the outcomes than did unvaccinated individuals. When we excluded people living in long-term care facilities, the results were similar.

CONCLUSIONS

We could not establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection, as the risk of COVID-19 complications was higher in vaccinated than in unvaccinated individuals. Our results correspond to the first wave of the COVID-19 pandemic, where more complications and mortalities due to COVID-19 had occurred. Despite that, our study adds more evidence for the analysis of a possible link between the quality of immunity and COVID-19 outcomes, particularly in the PHC setting.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

COVID-19 susceptibility and severity risks in a cross-sectional survey of over 500 000 US adults

OBJECTIVES

The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data.

DESIGN

A cross-sectional study.

SETTING

AncestryDNA customers in the USA who consented to research.

PARTICIPANTS

The AncestryDNA COVID-19 Study collected self-reported survey data on symptoms, outcomes, risk factors and exposures for over 563 000 adult individuals in the USA in just under 4 months, including over 4700 COVID-19 cases as measured by a self-reported positive test.

RESULTS

We replicated previously reported associations between several risk factors and COVID-19 susceptibility and severity outcomes, and additionally found that differences in known exposures accounted for many of the susceptibility associations. A notable exception was elevated susceptibility for men even after adjusting for known exposures and age (adjusted OR=1.36, 95% CI=1.19 to 1.55). We also demonstrated that self-reported data can be used to build accurate risk models to predict individualised COVID-19 susceptibility (area under the curve (AUC)=0.84) and severity outcomes including hospitalisation and critical illness (AUC=0.87 and 0.90, respectively). The risk models achieved robust discriminative performance across different age, sex and genetic ancestry groups within the study.

CONCLUSIONS

The results highlight the value of self-reported epidemiological data to rapidly provide public health insights into the evolving COVID-19 pandemic.

Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants

Background

Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

Method

Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

Results

In total 2779 patients identified with either Alpha (n = 1153), Gamma (n = 122), Delta (n = 808), or Omicron variants (n = 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (χ2 = 12.8, P &lt; .001; χ2 = 21.6, P &lt; .002; χ2 = 9.6, P = .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9%] and 513/696 [73.7%], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

Conclusions

Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants

BACKGROUND

Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited.

METHOD

Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes.

RESULTS

In total 2779 patients identified with either Alpha (n 1153), Gamma (n 122), Delta (n 808), or Omicron variants (n 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (2 12.8, P .001; 2 21.6, P .002; 2 9.6, P .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9] and 513/696 [73.7], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2.

CONCLUSIONS

Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Acute skeletal muscle loss in SARS-CoV-2 infection contributes to poor clinical outcomes in COVID-19 patients

BACKGROUND

Chronic disease causes skeletal muscle loss that contributes to morbidity and mortality. There are limited data on the impact of dynamic muscle loss on clinical outcomes in COVID-19. We hypothesized that acute COVID-19-related muscle loss (acute sarcopenia) is associated with adverse outcomes.

METHODS

A retrospective analysis of a prospective clinical registry of COVID-19 patients was performed in consecutive hospitalized patients with acute COVID-19 (n = 95) and compared with non-COVID-19 controls (n = 19) with two temporally unique CT scans. Pectoralis muscle (PM), erector spinae muscle (ESM) and 30 day standardized per cent change in cross sectional muscle area were quantified. Primary outcomes included mortality and need for intensive care unit (ICU) admission. Multivariate linear and logistic regression were performed. Cox proportional hazard ratios were generated for ICU admission or mortality for the per cent muscle loss standardized to 30 days.

RESULTS

The COVID-19 CT scan cohort (n = 95) had an average age of 63.3 ± 14.3 years, comorbidities including COPD (28.4%) and diabetes mellitus (42.1%), and was predominantly Caucasian (64.9%). The proportion of those admitted to the ICU was 54.7%, with 10.5% requiring tracheostomy and overall mortality 16.8%. Median duration between CT scans was 32 days (IQR: 16-63 days). Significant reductions in median per cent loss was noted for PM (-2.64% loss [IQR: -0.28, -5.47] in COVID-19 vs. -0.06 loss [IQR: -0.01, -0.28] in non-COVID-19 CT controls, P < 0.001) and ESM (-1.86% loss [IQR: -0.28, -5.47] in COVID-19 vs. -0.06 loss [IQR: -0.02, -0.11]) in non-COVID-19 CT controls, P < 0.001). Multivariate linear regression analysis of per cent loss in PM was significantly associated with mortality (-10.8% loss [95% CI: -21.5 to -0.19]) and ICU admission (-11.1% loss [95% CI: -19.4 to -2.67]), and not significant for ESM. Cox proportional hazard ratios demonstrated greater association with ICU admission (adj HR 2.01 [95% CI: 1.14-3.55]) and mortality (adj HR 5.30 [95% CI: 1.19-23.6]) for those with significant per cent loss in PM, and greater association with ICU admission (adj HR 8.22 [95% CI: 1.11-61.04]) but not mortality (adj HR 2.20 [95% CI: 0.70-6.97]) for those with significant per cent loss in ESM.

CONCLUSIONS

In a well-characterized cohort of 95 hospitalized patients with acute COVID-19 and two temporally distinct CT scans, acute sarcopenia, determined by standardized reductions in PM and ESM, was associated with worse clinical outcomes. These data lay the foundation for evaluating dynamic muscle loss as a predictor of clinical outcomes and targeting acute sarcopenia to improve clinical outcomes for COVID-19.

Complementary Sets of Autoantibodies Induced by SARS-CoV-2, Adenovirus and Bacterial Antigens Cross-React with Human Blood Protein Antigens in COVID-19 Coagulopathies

COVID-19 patients often develop coagulopathies including microclotting, thrombotic strokes or thrombocytopenia. Autoantibodies are present against blood-related proteins including cardiolipin (CL), serum albumin (SA), platelet factor 4 (PF4), beta 2 glycoprotein 1 (β2GPI), phosphodiesterases (PDE), and coagulation factors such as Factor II, IX, X and von Willebrand factor (vWF). Different combinations of autoantibodies associate with different coagulopathies. Previous research revealed similarities between proteins with blood clotting functions and SARS-CoV-2 proteins, adenovirus, and bacterial proteins associated with moderate-to-severe COVID-19 infections. This study investigated whether polyclonal antibodies (mainly goat and rabbit) against these viruses and bacteria recognize human blood-related proteins. Antibodies against SARS-CoV-2 and adenovirus recognized vWF, PDE and PF4 and SARS-CoV-2 antibodies also recognized additional antigens. Most bacterial antibodies tested (group A streptococci [GAS], staphylococci, Escherichia coli [E. coli], Klebsiella pneumoniae, Clostridia, and Mycobacterium tuberculosis) cross-reacted with CL and PF4. while GAS antibodies also bound to F2, Factor VIII, Factor IX, and vWF, and E. coli antibodies to PDE. All cross-reactive interactions involved antibody-antigen binding constants smaller than 100 nM. Since most COVID-19 coagulopathy patients display autoantibodies against vWF, PDE and PF4 along with CL, combinations of viral and bacterial infections appear to be necessary to initiate their autoimmune coagulopathies.

Epidemiological and clinical characteristics of pregnant women and neonates with COVID-19 in Northwest Mexico

INTRODUCTION

The SARS-CoV-2 virus, which causes COVID-19, has spread quickly worldwide, causing millions of cases and thousands of deaths. Some risk factors in the general population are related to the development of severe COVID-19 or death, but in pregnant women and neonates, the information is limited.

OBJECTIVE

To determine the epidemiological and clinical characteristics of pregnant women and neonates diagnosed with COVID-19 by RT-PCR and serological tests, and analyze the relationship between the influenza vaccination and COVID-19 symptoms in infected pregnant women in Sinaloa state.

METHODS

We collected samples from 116 pregnant women and 84 neonates from the Women´s Hospital of Sinaloa. They were diagnosed with COVID-19 by RT-PCR and serological tests (IgG), and sociodemographic, clinical and laboratory parameters were recorded.

RESULTS

A total of 11.2% (13/116) of the pregnant women were RT-PCR+, 25% (29/116) were IgG+ and 4.3% (5/116) were positive for both tests. Symptoms such as rhinorrhea (P = .04), cough (P = .02) and polypnea (P = .04) in pregnant women were related to COVID-19, also leukocyte index was higher in pregnant women with COVID-19 (P = .03), but the associations were lost after the Bonferroni correction. No laboratory parameters or underlying diseases were associated with COVID-19, and most infected pregnant women had mild cases. We found an association between the influenza vaccine and less common COVID-19 symptoms in pregnant women who were infected (P = .01). A total of 7.2% (6/84) of neonates were RT-PCR+, 35.7% (30/84) were IgG+, and there were no symptoms or underlying diseases associated with neonates who were infected. In conclusion, this work demonstrated that some symptoms were related to COVID-19, most pregnant women and neonates had mild cases, and the influenza vaccine could decrease the severity of COVID-19 cases in pregnant women.

Vaccinations Status against Vaccine-Preventable Diseases and Willingness to Be Vaccinated in an Italian Sample of Frail Subjects

BACKGROUND

Study aim was to investigate the vaccination status against vaccine-preventable diseases (VPD) of frail adults during the SARS-CoV-2 pandemic and, for those subjects eligible for at least one vaccine, with respect to the recommended vaccination in line with the Italian National Vaccination Prevention Plane (NPVP), to explore the willingness to be vaccinated.

METHODS

A cross-sectional study was carried out among adults aged ≥ 60, immunocompromised or subjects affected by chronic conditions.

RESULTS

Among the 427 participants, a vaccination coverage rate lower than the targets for all the vaccines considered was found. Of those, 72.6% of subjects stated their willingness to receive recommended vaccinations, and 75.2% of the respondents stated that the advice to undergo vaccinations was received by the General Practitioner (GP). In a multivariable logistic regression model, higher odds of recommended VPD vaccination uptake (defined as having two or more of the recommended vaccinations) were associated with the willingness towards recommended VPD vaccination (Odds Ratio = 3.55, 95% Confidence Interval: 1.39 to 9.07), university education (OR = 2.03, 95% CI: 1.03 to 3.97), but having another person in the household (OR = 0.52, 95% CI: 0.28 to 0.97), and history of oncological disease (OR = 0.39, 95% CI: 0.18 to 0.87) were predictive of lower odds of vaccination uptake. In another multivariable model, higher odds of willingness to receive vaccines were associated with kidney disease (OR = 3.3, 95% CI: 1.01 to 10.5), perceived risk of VPD (OR = 1.9, 95% CI: 1.02 to 3.3), previous influenza vaccination (OR = 3.4, 95% CI: 1.8 to 6.5), and previous pneumococcal vaccination (OR = 3.1, 95% CI: 1.3 to 7.7), but increasing age (OR = 0.93 per year, 95% CI: 0.91 to 0.97), working (OR = 0.40, 95% CI: 0.20 to 0.78), and fear of vaccine side effects (OR = 0.38, 95% CI: 0.21 to 0.68) were predictive of lower odds of willingness to receive vaccines.

CONCLUSIONS

Despite specific recommendations, vaccination coverage rates are far below international targets for frail subjects. Reducing missed opportunities for vaccination could be a useful strategy to increase vaccination coverage in frail patients during the routine checks performed by GPs and specialists.

Influenza and pneumococcal vaccination and COVID-19 in kidney transplant patients

Background

This study aims to investigate the effect of recent influenza and pneumococcal vaccines' administration on the development of COVID-19 infection in kidney transplant recipients during the pandemic.

Methods

The effect of influenza and pneumococcal vaccines on the clinical course of the disease in COVID-positive (COVID group, n: 105) and COVID-negative (control group, n: 127) recipients has been examined. The control group included patients with negative rRT-PCR test results. At the time of the study, no patient was vaccinated with COVID-19 vaccine. The patients' influenza and/or pneumococcal vaccination rates in 2019 and 2020 were determined. In 2019 and 2020, 32 and 33 people in the COVID-positive group and 61 and 54 people in the COVID-negative group had received influenza and/or pneumococcal vaccines, respectively. The median study follow-up times of the COVID-negative and COVID-positive groups were 13.04 and 8.31 months, respectively.

Results

Compared with the COVID-negative group, the patients in the COVID-positive group were younger and had a longer post-transplant time. In addition, the rate of transplantation from a living donor and the rate of COVID positivity in family members were also higher. The influenza vaccination rates in the COVID negative group were significantly higher than the COVID-positive group in 2020 (23.8% vs 37%, p = 0.031). Multivariate logistic regression analysis revealed that the presence of COVID-19 in family members and lack of pneumococcal vaccination in 2020 increased the risk of being positive for COVID-19. There was no significant difference in the hospitalization rates, the need for dialysis and intensive care, the hospital stay, and the graft dysfunction in the COVID-positive patients with and without influenza and pneumococcal vaccines.

Conclusion

The observations made throughout this study suggest that influenza and pneumococcal vaccination in transplant patients may reduce the risk of COVID-19 disease and provide additional benefits during the pandemic period.

Assessing the added value of linking electronic health records to improve the prediction of self-reported COVID-19 testing and diagnosis

Since the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic, a focus of research has been to identify risk factors associated with COVID-19-related outcomes, such as testing and diagnosis, and use them to build prediction models. Existing studies have used data from digital surveys or electronic health records (EHRs), but very few have linked the two sources to build joint predictive models. In this study, we used survey data on 7,054 patients from the Michigan Genomics Initiative biorepository to evaluate how well self-reported data could be integrated with electronic records for the purpose of modeling COVID-19-related outcomes. We observed that among survey respondents, self-reported COVID-19 diagnosis captured a larger number of cases than the corresponding EHRs, suggesting that self-reported outcomes may be better than EHRs for distinguishing COVID-19 cases from controls. In the modeling context, we compared the utility of survey- and EHR-derived predictor variables in models of survey-reported COVID-19 testing and diagnosis. We found that survey-derived predictors produced uniformly stronger models than EHR-derived predictors—likely due to their specificity, temporal proximity, and breadth—and that combining predictors from both sources offered no consistent improvement compared to using survey-based predictors alone. Our results suggest that, even though general EHRs are useful in predictive models of COVID-19 outcomes, they may not be essential in those models when rich survey data are already available. The two data sources together may offer better prediction for COVID severity, but we did not have enough severe cases in the survey respondents to assess that hypothesis in in our study.

Dynamic Predictive Models With Visualized Machine Learning for Assessing Chondrosarcoma Overall Survival

Chondrosarcoma is a malignant bone tumor with a low incidence rate. Accurate risk evaluation is crucial for chondrosarcoma treatment. Due to the limited reliability of existing predictive models, we intended to develop a credible predictor for clinical chondrosarcoma based on the Surveillance, Epidemiology, and End Results data and four Chinese medical institutes. Three algorithms (Best Subset Regression, Univariate and Cox regression, and Least Absolute Shrinkage and Selector Operator) were used for the joint training. A nomogram predictor including eight variables—age, sex, grade, T, N, M, surgery, and chemotherapy—is constructed. The predictor provides good performance in discrimination and calibration, with area under the curve ≥0.8 in the receiver operating characteristic curves of both internal and external validations. The predictor especially had very good clinical utility in terms of net benefit to patients at the 3- and 5-year points in both North America and China. A convenient web calculator based on the prediction model is available at https://drwenle029.shinyapps.io/CHSSapp, which is free and open to all clinicians.

The Association Between Previous Influenza Vaccination and COVID-19 Infection Risk and Severity: A Systematic Review and Meta-analysis

INTRODUCTION

The association between influenza vaccination and COVID-19 remains controversial. This meta-analysis aimed to investigate whether influenza vaccination reduces the susceptibility and severity of SARS-CoV-2 infection.

METHODS

A systematic literature search of PubMed, Web of Science, the Cochrane Library, Embase, China National Knowledge Infrastructure, SinoMed, Wanfang Data Knowledge Service Platform, and China Science and Technology Journal VIP Database was conducted from database inception to August 2021. The pooled RR with 95% CI was used to estimate the effect of influenza vaccination on COVID-19. The I² value was used to assess heterogeneity. If I²>50%, the random-effects model was used as the pooling method.

RESULTS

A total of 23 published articles with 1,037,445 participants were identified. This meta-analysis showed that influenza vaccination was associated with reduced risk of COVID-19 infection (RR=0.83, 95% CI=0.76, 0.90) and hospitalization (RR=0.71, 95% CI=0.59, 0.84), although not significantly associated with intensive care unit admission and death (risk of intensive care unit admission: RR=0.93, 95% CI=0.64, 1.36; risk of death: RR=0.83, 95% CI=0.68, 1.01). Further analysis suggested that the tetravalent influenza vaccine may be associated with a reduced risk of COVID-19 infection (RR=0.74, 95% CI=0.65, 0.84).

DISCUSSION

The results suggest that influenza vaccination is associated with reduced susceptibility to or disease severity of COVID-19 and that influenza vaccination may reduce the risk of COVID-19 and improve clinical outcomes.

Melatonin and multiple sclerosis: antioxidant, anti-inflammatory and immunomodulator mechanism of action

BACKGROUND

Melatonin is an indole hormone secreted primarily by the pineal gland that showing anti-oxidant, anti-inflammatory and anti-apoptotic capacity. It can play an important role in the pathophysiological mechanisms of various diseases. In this regard, different studies have shown that there is a relationship between Melatonin and Multiple Sclerosis (MS). MS is a chronic immune-mediated disease of the Central Nervous System.

AIM

The objective of this review was to evaluate the mechanisms of action of melatonin on oxidative stress, inflammation and intestinal dysbiosis caused by MS, as well as its interaction with different hormones and factors that can influence the pathophysiology of the disease.

RESULTS

Melatonin causes a significant increase in the levels of catalase, superoxide dismutase, glutathione peroxidase, glutathione and can counteract and inhibit the effects of the NLRP3 inflammasome, which would also be beneficial during SARS-CoV-2 infection. In addition, melatonin increases antimicrobial peptides, especially Reg3β, which could be useful in controlling the microbiota.

CONCLUSION

Melatonin could exert a beneficial effect in people suffering from MS, running as a promising candidate for the treatment of this disease. However, more research in human is needed to help understand the possible interaction between melatonin and certain sex hormones, such as estrogens, to know the potential therapeutic efficacy in both men and women.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Melatonin as adjunctive therapy in patients admitted to the Covid-19

Objective

Coronavirus has disrupted the natural order of the world since September 2019 with no specific medication. The beneficial effects of melatonin on sepsis and viral influenza were demonstrated previously, but its effects on covid-19, especially COVID -19 ICU, is unclear. Therefore, our aim was to determine the effects of melatonin in COVID-19 ICU patients.

Methods

This is a retrospective cohort study in which the records of patients admitted to COVID -19 ICU of (XXX) during March to June 2020 were reviewed. According to inclusion criteria, patients who received 15 mg of melatonin daily were called MRG and the rest were called NMRG.

Results

Thirty-one patients were included and analyzed, of which twelve patients were in MRG. Demographic and clinical characteristics, and laboratory data were similar between two groups at ICU admission. Melatonin had no significant effect on ICU duration, CRP and ESR, also the trend of changes was in favor of melatonin. Nevertheless, melatonin significantly reduced the NLR (OR = −9.81, p = 0.003), and also declined mortality marginally (p = 0.09). Melatonin was well tolerated with no major adverse effects, moreover the thrombocytopenia occurrence was significantly lower in MRG (p = 0.005). In MRG, survival increased and mortality risk decreased, although the difference between groups wasn't significant (p = 0.37), which might be related to the small sample-size.

Conclusion

Our study showed that melatonin is unlikely to reduce mortality among COVID19 patients and with no significant effect on disease-specific biochemical parameters.

-

Coronavirus has disrupted the natural order of the world since September 2019 with no specific medication.

-

The beneficial effects of melatonin on sepsis and viral influenza were demonstrated previously.

-

Our survey showed melatonin had a beneficial effect on survival and mortality risk.

-

As well as platelets and lymphocytes without life-threatening complications.

-

Melatonin was an essential adjuvant therapy in patients admitted to covid-19 ICU.

Health-Related Quality of Life Mildly Affected Following COVID-19: a Retrospective Pre-post Cohort Study with a Propensity Score-Matched Control Group

IMPORTANCE

Long-term health effects have been indicated following COVID-19; however, the impact of COVID-19 on health-related quality of life (HRQOL), including who may experience ongoing symptoms, is unknown.

OBJECTIVE

To identify change in HRQOL following COVID-19 compared to pre-infection HRQOL and a matched control group, and identify predictors of patients who worsen.

DESIGN

Retrospective pre-post cohort study with a matched control group.

SETTING

Large healthcare system in northeast Ohio.

PARTICIPANTS

A total of 3,690 adult patients diagnosed with COVID-19 who completed HRQOL surveys during routine care for ambulatory visits before and after infection. Propensity-score 1:1 match was utilized to identify controls without COVID who completed HRQOL at two time points.

MAIN OUTCOMES

HRQOL was assessed with PROMIS Global Health: global mental and physical health summary scores. Pre- and post-COVID PROMIS Global Health was completed as part of routine care from 1/1/2019 to 2/29/2020 and 4/4/2020 to 11/1/2021, respectively, and extracted from the electronic health record.

RESULTS

COVID-19 patients (mean age 53±15; 66% female) completed PROMIS Global Health in the year prior (median 11.1 months) and after diagnosis (median 7.8 months). Compared to before infection, COVID-19 patients had a significant reduction in global mental health and stable global physical health (-0.85 and 0.05 T-score points, respectively) with clinically meaningful reduction (≥5 T-score points) experienced by 27% and 23% of patients, respectively. Predictors of worsening global health included being female, having depression, being hospitalized for COVID-19, and better pre-COVID global health. Compared to the control group, there was significantly worse global mental and physical health decline following COVID-19 (-0.53 and -0.37 T-score points, respectively).

CONCLUSIONS AND RELEVANCE

A quarter of patients with COVID-19 experienced meaningful reductions in HRQOL. Reductions in global mental and physical health were modest, although significantly worse than a control group. Additionally, identified predictors of patients who worsen may assist clinicians when counseling patients of their risk of worse HRQOL following COVID-19.

"MATH+" Multi-Modal Hospital Treatment Protocol for COVID-19 Infection: Clinical and Scientific Rationale

In December 2019, coronavirus disease 2019 (COVID-19), a severe respiratory illness caused by the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China. The greatest impact that COVID-19 had was on intensive care units (ICUs), given that approximately 20% of hospitalized cases developed acute respiratory failure (ARF) requiring ICU admission. Based on the assumption that COVID-19 represented a viral pneumonia and no anti-coronaviral therapy existed, nearly all national and international health care societies recommended "supportive care only" avoiding other therapies outside of randomized controlled trials, with a specific prohibition against the use of corticosteroids in treatment. However, early studies of COVID-19-associated ARF reported inexplicably high mortality rates, with frequent prolonged durations of mechanical ventilation (MV), even from centers expert in such supportive care strategies. These reports led the authors to form a clinical expert panel called the Front-Line COVID-19 Critical Care Alliance (www.flccc.net). The panel collaboratively reviewed the emerging clinical, radiographic, and pathological reports of COVID-19 while initiating multiple discussions among a wide clinical network of front-line clinical ICU experts from initial outbreak areas in China, Italy, and New York. Based on the shared early impressions of "what was working and what wasn't working", the increasing medical journal publications and the rapidly accumulating personal clinical experiences with COVID-19 patients, a treatment protocol was created for the hospitalized patients based on the core therapies of methylprednisolone, ascorbic acid, thiamine, heparin and non-antiviral co-interventions (MATH+). This manuscript reviews the scientific and clinical rationale behind MATH+ based on published in-vitro, pre-clinical, and clinical data in support of each medicine, with a special emphasis of studies supporting their use in the treatment of patients with viral syndromes and COVID-19 specifically.

The Influenza Vaccine May Protect Pregnant and Postpartum Women against Severe COVID-19

The SARS-CoV-2 pandemic has imposed a huge challenge on the antenatal care of pregnant women worldwide, with the maternal mortality rate being raised to alarming levels. While COVID-19 vaccines were developed, some studies highlighted a possible relationship between influenza vaccination and lower odds of COVID-19 infection. As obstetric patients belong to a high-risk group for respiratory diseases, this study evaluated whether influenza vaccination reduces the severity of COVID-19 infection and mortality among pregnant and postpartum women. We conducted a retrospective cohort study on 3370 pregnant and postpartum women from the Brazilian national database, where they were grouped according to their influenza vaccination status before the onset of COVID-19 symptoms. The intensive care unit admission and intubation rates were significantly higher among subjects in the unvaccinated group (p = 0.002 and p < 0.001, respectively). The odds of mortality risk among those who received the vaccine was 0.33, with a 95% confidence interval of 0.23–0.47. The numbers of patients who needed to be vaccinated to avoid a case of intensive care unit admission, intubation, or death due to COVID-19 were 11, 15, and 11, respectively. Influenza vaccines could confer protection against severe COVID-19 infection in pregnant and postpartum women.