Viral respiratory infections: a cause of community-acquired pneumonia or a predisposing factor?

Insights into parasites and COVID-19 co-infections in Iran: a systematic review

BACKGROUND

It is well-documented that using immunosuppressive drugs such as corticosteroids or cytokine blockers in treating coronavirus disease 2019 (COVID-19) increases the risk of co-infections. Here we systematically summarized the cases of COVID-19-associated parasitic infections (CAPIs) in Iran.

METHODS

From 19 February 2020 to 10 May 2023, all studies on Iranian patients suffering from CAPIs were collected from several databases using a systematic search strategy.

RESULTS

Of 540 records, 11 studies remained for data extraction. In this research, most of the studies were related to Lophomonas and Toxoplasma. Of 411 cases of CAPIs, toxoplasmosis (385 [93.7%]) had the highest rate of infection among Iranian patients, followed by blastocystosis (15 [3.6%]), fascioliasis (4 [0.97%]), leishmaniasis (3 [0.7%]), lophomoniasis (3 [0.7%]) and strongyloidiasis (1 [0.2%]). In general, Blastocystis enhanced diarrhoea in patients with COVID-19. Lophomonas, Toxoplasma and Strongyloides increased the severity of COVID-19, but Fasciola decreased its intensity. Patients with a history of cutaneous leishmaniasis showed mild symptoms of COVID-19. Also, patients with a prior history of hydatid cysts were not affected by COVID-19.

CONCLUSIONS

Due to the similar symptoms of some parasitic diseases and COVID-19 and immunosuppressive treatment regimens in these patients that may cause the reactivation or recurrence of parasitic infections, early diagnosis and treatment are required.

You must be myths-taken: Examining belief in falsehoods during the COVID-19 health crisis

The prevalence of health myths is increasing with the rise of Internet use. Left unaddressed, online falsehoods can lead to harmful behaviours. In times of crisis, such as the recent COVID-19 pandemic, the circulation of many myths is exacerbated, often to varying degrees among different cultures. Singapore is a multicultural hub in Asia with Western and Asian influences. Although several studies have examined health myths from a Western or Eastern perspective, little research has investigated online health falsehoods in a population that is culturally exposed to both. Furthermore, most studies examined myths cross-sectionally instead of capturing trends in myth prevalence over time, particularly during crisis situations. Given these literature gaps, we investigated popular myths surrounding the recent COVID-19 pandemic within the multicultural setting of Singapore, by examining its general population. We further examined changes in myth beliefs over the two-year period during the pandemic, and population demographic differences in myth beliefs. Using randomised sampling, two online surveys of nationally representative samples of adults (aged 21-70 years) residing in Singapore were conducted, the first between October 2020 and February 2021 (N = 949), and the second between March and April 2022 (N = 1084). Results showed that 12.7% to 57.5% of the population were unable to identify various myths, such as COVID-19 was manmade, and that three of these myths persisted significantly over time (increases ranging from 3.9% to 9.8%). However, belief in myths varied across population demographics, with ethnic minorities (Indians and Malays), females, young adults and those with lower education levels being more susceptible to myths than their counterparts (p < 0.05). Our findings suggest that current debunking efforts are insufficient to effectively counter misinformation beliefs during health crises. Instead, a post-COVID-19 landscape will require targeted approaches aimed at vulnerable population sub-groups, that also focus on the erroneous beliefs with long staying power.

Community-acquired and hospital-acquired bacterial co-infections in patients hospitalized with Covid-19 or influenza: a retrospective cohort study

BACKGROUND

Bacterial co-infections are believed to be less frequent in patients with Covid-19 than influenza, but frequencies varied between studies.

METHODS

This single-center retrospective, propensity score-matched analysis included adult patients with Covid-19 or influenza admitted to normal-care wards between 02/2014 and 12/2021. Covid-19 cases were propensity score matched to influenza cases at a 2:1 ratio. Community-acquired and hospital-acquired bacterial co-infections were defined as positive blood or respiratory cultures ≤ 48 h or > 48 h after hospital admission, respectively. The primary outcome was comparison of community-acquired and hospital-acquired bacterial infections between patients with Covid-19 and influenza in the propensity score-matched cohort. Secondary outcomes included frequency of early and late microbiological testing.

RESULTS

A total of 1337 patients were included in the overall analysis, of which 360 patients with Covid-19 were matched to 180 patients with influenza. Early (≤ 48 h) microbiological sampling was performed in 138 (38.3%) patients with Covid-19 and 75 (41.7%) patients with influenza. Community-acquired bacterial co-infections were found in 14 (3.9%) of 360 patients with Covid-19 and 7 (3.9%) of 180 patients with influenza (OR 1.0, 95% CI 0.3-2.7). Late (> 48 h) microbiological sampling was performed in 129 (35.8%) patients with Covid-19 and 74 (41.1%) patients with influenza. Hospital-acquired bacterial co-infections were found in 40 (11.1%) of 360 patients with Covid-19 and 20 (11.1%) of 180 patients with influenza (OR 1.0, 95% CI 0.5-1.8).

CONCLUSION

The rate of community-acquired and hospital-acquired bacterial co-infections was similar in hospitalized Covid-19 and influenza patients. These findings contrast previous literature reporting that bacterial co-infections are less common in Covid-19 than influenza.

Association between breakthrough infection with COVID-19 and Toxoplasma gondii: a cross-sectional study

The breakthrough infection following COVID-19 vaccination has been a subject of concern recently. Evidence suggests that COVID-19 vaccine efficacy diminishes over time due to multiple factors related to the host, and vaccine. Coinfection with other pathogens was claimed earlier as a contributing cause for this phenomenon. Hence, we aimed to stratify the association of post-COVID-19 vaccination breakthrough coinfection with Toxoplasma gondii (T. gondii) and its impact on disease severity. This cross-sectional study included 330 COVID-19-vaccinated patients confirmed by RT-PCR. They were also screened for anti- T. gondii antibodies using ELISA. Toxoplasma seropositive cases' whole blood was screened for DNA using PCR to correlate results with COVID-19 severity. Out of 330 COVID-19 vaccinated patients with breakthrough infection, 34.5% (114 patients) showed positivity for Toxoplasma IgG by ELISA, and none of the cases was IgM positive. Eleven patients (9.6%) of the IgG-positive cases were positive by PCR. Positive PCR cases correlated positively with the Toxoplasma IgG titer (P < 0.001), and the Cutoff point was 191.5. Molecular analysis of Toxoplasma and COVID-19 severity showed that 8 (72.7%), 1 (9.1%), and 2 cases (18.2%) had mild, moderate, and severe courses of the disease, respectively, with no significant correlation. Our study reported a heightened prevalence of latent toxoplasmosis among mild cases of COVID-19 breakthrough infection. Nevertheless, a discernible correlation between latent toxoplasmosis and COVID-19 severity is lacking. Hence, implementing studies on a larger scale could provide a more comprehensive comprehension of this association.

Prevalence of secondary infections and association with mortality rates of hospitalized COVID-19 patients

BACKGROUND

ICU and other patients hospitalized with corona-virus disease 2019 (COVID-19) are more susceptible to secondary infections. Undetected secondary infections tend to have a severe clinical impact, associated with prolonged hospitalization and higher rates of inpatient mortality.

OBJECTIVES

Estimate the prevalence of secondary infections, determine the frequency of microbial species detected at different body sites, and measure the association between secondary infections and outcomes among hospitalized COVID-19 patients.

DESIGN

Cross-sectional analytical study.

SETTING

Tertiary care center in Riyadh PATIENTS AND METHODS: Data were collected through retrospective chart review of hospitalized COVID-19 patients >18 years old from March 2020 until May 2022 at King Saud University Medical City (27 months). Rates of secondary infections among hospitalized COVID-19 patients were described and data on clinical outcomes (intensive care admission, invasive management procedures and mortality) was collected.

MAIN OUTCOME MEASURES

Features and rates of infection and mortality.

SAMPLE SIZE

260 RESULTS: In total, 24.2% of the study population had secondary infections. However, only 68.8% of patients had secondary infection testing, from which 35.2% had a confirmed secondary infection. These patients had a significantly higher prevalence of diabetes mellitus (P<.0001) and cardiovascular diseases (P=.001). The odds of ICU admissions (63.3%) among secondarily infected patients was 8.4 times higher compared to patients with only COVID-19 infection (17.3%). Secondarily infected patients were more likely to receive invasive procedures (OR=5.068) and had a longer duration of hospital stay compared to COVID-19 only patients. Overall mortality was 16.2%, with a predominantly higher proportion among those secondarily infected (47.6% vs 6.1%) (OR=14.015). Bacteria were the most commonly isolated organisms, primarily from blood (23.3%), followed by fungal isolates, which were mostly detected in urine (17.2%). The most detected organism was Candida albicans (17.2%), followed by Escherichia coli (9.2%), Klebsiella pneumoniae (9.2%) and Pseudomonas aeruginosa (9.2%).

CONCLUSION

Secondary infections were prevalent among hospitalized COVID-19 patients. Secondarily infected patients had longer hospital stay, higher odds of ICU admission, mortality, and invasive procedures.

LIMITATION

Single-center study, retrospective design and small sample size.

CONFLICT OF INTEREST

None.

Secondary fungal infections in SARS-CoV-2 patients: pathological whereabouts, cautionary measures, and steadfast treatments

The earliest documented COVID-19 case caused by the SARS-CoV-2 coronavirus occurred in Wuhan, China, in December 2019. Since then, several SARS-CoV-2 mutants have rapidly disseminated as exemplified by the community spread of the recent omicron variant. The disease already attained a pandemic status with ever-dwindling mortality even after two and half years of identification and considerable vaccination. Aspergillosis, candidiasis, cryptococcosis and mucormycosis are the prominent fungal infections experienced by the majority of SARS-CoV-2 high-risk patients. In its entirety, COVID-19's nexus with these fungal infections may worsen the intricacies in the already beleaguered high-risk patients, making this a topic of substantial clinical concern. Thus, thorough knowledge of the subject is necessary. This article focuses on the concomitant fungal infection(s) in COVID-19 patients, taking into account their underlying causes, the screening methods, manifested drug resistance, and long-term effects. The information and knowledge shared herein could be crucial for the management of critically ill, aged, and immunocompromised SARS-CoV-2 patients who have had secondary fungal infections (SFIs).

The Dark Side of Nosocomial Infections in Critically Ill COVID-19 Patients

Coronavirus disease 2019 (COVID-19) is a potentially serious acute respiratory infection caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Since the World Health Organization (WHO) declared COVID-19 a global pandemic, the virus has spread to more than 200 countries with more than 500 million cases and more than 6 million deaths reported globally. It has long been known that viral respiratory tract infections predispose patients to bacterial infections and that these co-infections often have an unfavourable clinical outcome. Moreover, nosocomial infections, also known as healthcare-associated infections (HAIs), are those infections that are absent at the time of admission and acquired after hospitalization. However, the impact of coinfections or secondary infections on the progression of COVID-19 disease and its lethal outcome is still debated. The aim of this review was to assess the literature on the incidence of bacterial co-infections and superinfections in patients with COVID-19. The review also highlights the importance of the rational use of antibiotics in patients with COVID-19 and the need to implement antimicrobial stewardship principles to prevent the transmission of drug-resistant organisms in healthcare settings. Finally, alternative antimicrobial agents to counter the emergence of multidrug-resistant bacteria causing healthcare-associated infections in COVID-19 patients will also be discussed.

Fasting Plasma Glucose Levels at the Time of Admission Predict 90-Day Mortality in Patients with Viral Pneumonia. A Prospective Study

PURPOSE

To determine the effect of fasting plasma glucose (FPG) level at admission affects the 90-day mortality rate in patients with viral pneumonia.

METHODS

Two hundred fifty viral pneumonia patients were stratified into normal FPG (FPG<7.0 mmol/L), moderately-elevated FPG (FPG=7.0-14.0 mmol/L), and highly-elevated FPG groups (FPG≥14.0 mmol/L) according to the FPG level at the time of admission. The clinical characteristics, etiologies, and prognosis of different groups of patients were compared. Kaplan-Meier survival and Cox regression analyses were used to determine the relationship between the FPG level and 90-day all-cause mortality rate in patients with viral pneumonia.

RESULTS

Patients in the moderately- and highly-elevated FPG groups had a higher proportion of severe disease and mortality compared with the normal FPG group (P<0.001). Kaplan-Meier survival analysis showed a significant trend toward higher mortality and increased cumulative risk at 30, 60, and 90 d in patients with an FPG=7.0-14.0 mmol/L and an FPG≥14 mmol/L (χ²=51. 77, P<0.001). Multivariate Cox regression analysis revealed that compared with an FPG<7.0 mmol/L, FPG=7.0 and 14.0 mmol/L (HR: 9.236, 95% CI: 1.106-77.119, P=0.040) and FPG≥14.0 mmol/L (HR: 25.935, 95% CI: 2.586-246.213, P=0.005) were independent risk factors for predicting the 90-day mortality rate in viral pneumonia patients.

CONCLUSIONS

The higher the FPG level at admission in a patient with viral pneumonia, the higher the risk of all-cause mortality within 90 d.

Early Bacterial Coinfections in Patients Admitted to the ICU With COVID-19 or Influenza: A Retrospective Cohort Study

Previous findings suggest that bacterial coinfections are less common in ICU patients with COVID-19 than with influenza, but evidence is limited.

OBJECTIVES

This study aimed to compare the rate of early bacterial coinfections in ICU patients with COVID-19 or influenza.

DESIGN SETTING AND PARTICIPANTS

Retrospective propensity score matched cohort study. We included patients admitted to ICUs of a single academic center with COVID-19 or influenza (January 2015 to April 2022).

MAIN OUTCOMES AND MEASURES

The primary outcome was early bacterial coinfection (i.e., positive blood or respiratory culture within 2 d of ICU admission) in the propensity score matched cohort. Key secondary outcomes included frequency of early microbiological testing, antibiotic use, and 30-day all-cause mortality.

RESULTS

Out of 289 patients with COVID-19 and 39 patients with influenza, 117 (n = 78 vs 39) were included in the matched analysis. In the matched cohort, the rate of early bacterial coinfections was similar between COVID-19 and influenza (18/78 [23%] vs 8/39 [21%]; odds ratio, 1.16; 95% CI, 0.42-3.45; p = 0.82). The frequency of early microbiological testing and antibiotic use was similar between the two groups. Within the overall COVID-19 group, early bacterial coinfections were associated with a statistically significant increase in 30-day all-cause mortality (21/68 [30.9%] vs 40/221 [18.1%]; hazard ratio, 1.84; 95% CI, 1.01-3.32).

CONCLUSIONS AND RELEVANCE

Our data suggest similar rates of early bacterial coinfections in ICU patients with COVID-19 and influenza. In addition, early bacterial coinfections were significantly associated with an increased 30-day mortality in patients with COVID-19.

Risk factors and effect on mortality of superinfections in a newly established COVID-19 respiratory sub-intensive care unit at University Hospital in Rome

BACKGROUND

Little is known on the burden of co-infections and superinfections in a specific setting such as the respiratory COVID-19 sub-intensive care unit. This study aims to (i) assess the prevalence of concurrent and superinfections in a respiratory sub-intensive care unit, (ii) evaluate the risk factors for superinfections development and (iii) assess the impact of superinfections on in-hospital mortality.

METHODS

Single-center retrospective analysis of prospectively collected data including COVID-19 patients hospitalized in a newly established respiratory sub-intensive care unit managed by pneumologists which has been set up from September 2020 at a large (1200 beds) University Hospital in Rome. Inclusion criteria were: (i) COVID-19 respiratory failure and/or ARDS; (ii) hospitalization in respiratory sub-intensive care unit and (iii) age > 18 years. Survival was analyzed by Kaplan-Meier curves and the statistical significance of the differences between the two groups was assessed using the log-rank test. Multivariable logistic regression and Cox regression model were performed to tease out the independent predictors for superinfections' development and for mortality, respectively.

RESULTS

A total of 201 patients were included. The majority (106, 52%) presented severe COVID-19. Co-infections were 4 (1.9%), whereas 46 patients (22%) developed superinfections, mostly primary bloodstream infections and pneumonia. In 40.6% of cases, multi-drug resistant pathogens were detected, with carbapenem-resistant Acinetobacter baumannii (CR-Ab) isolated in 47%. Overall mortality rate was 30%. Prior (30-d) infection and exposure to antibiotic therapy were independent risk factors for superinfection development whereas the development of superinfections was an independent risk factors for in-hospital mortality. CR-Ab resulted independently associated with 14-d mortality.

CONCLUSION

In a COVID-19 respiratory sub-intensive care unit, superinfections were common and represented an independent predictor of mortality. CR-Ab infections occurred in almost half of patients and were associated with high mortality. Infection control rules and antimicrobial stewardship are crucial in this specific setting to limit the spread of multi-drug resistant organisms.

Bacterial profiles and their antibiotic resistance background in superinfections caused by multidrug-resistant bacteria among COVID-19 ICU patients from southwest Iran

This study investigated the bacterial causes of superinfections and their antibiotic resistance pattern in severe coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) of Razi Hospital in Ahvaz, southwest Iran. In this cross-sectional study, endotracheal tube (ETT) secretion samples of 77 intubated COVID-19 patients, confirmed by reverse transcription-quantitative polymerase chain reaction, were investigated by standard microbiology test and analytical profile index kit. Antibiotic susceptibility testing was performed by disc diffusion. The presence of Haemophilus influenzae and Mycoplasma pneumoniae was investigated by the polymerase chain reaction (PCR). Using culture and PCR methods, 56 (72.7%) of the 77 COVID-19 patients (mean age of 55 years, 29 male and 27 female) had superinfections. Using culture, 67 isolates including 29 (43.2%) Gram-positive and 38 (56.7%) Gram-negative bacteria (GNB) were identified from 49 COVID-19 patients. The GNB were more predominant than the Gram-positive pathogens. Klebsiella pneumoniae (28.4%, n = 19/67) was the most common isolate followed by Staphylococcus aureus (22.4%, n = 15/67). Using PCR, 10.4% (8/77) and 11.7% (9/77) of ETT secretion specimens had H. influenzae and M. pneumoniae amplicons, respectively. Gram-positive and Gram-negative isolates showed high resistance rates (>70.0%) to majority of the tested antibiotics including fluoroquinolone, carbapenems, and cephalosporins and 68.7% (46/67) of isolates were multidrug-resistant (MDR). This study showed a high frequency rate of superinfections by MDR bacteria among COVID-19 patients in southwest Iran. The prevention of long-term consequences caused by COVID-19, demands continuous antibiotic surveillance particularly in management of bacterial superinfections.

Using a 29-mRNA Host Response Classifier To Detect Bacterial Coinfections and Predict Outcomes in COVID-19 Patients Presenting to the Emergency Department

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial coinfection, and determining illness severity since current practices require separate workflows. Here, we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and bacterial coinfections and predicting clinical severity of COVID-19. A total of 161 patients with PCR-confirmed COVID-19 (52.2% female; median age, 50.0 years; 51% hospitalized; 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene blood RNA), and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter. The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrollment, and the remaining patients oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial coinfection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e., Clostridioides difficile colitis (n = 1), urinary tract infection (n = 1), and clinically diagnosed bacterial infections (n = 3), for a specificity of 99.4%. Two of 101 (2.8%) patients in the IMX-SEV-3 "Low" severity classification and 7/60 (11.7%) in the "Moderate" severity classification died within 30 days of enrollment. IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19 and bacterial coinfections and predicted patients' risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management, including more accurate treatment decisions and optimized resource utilization. IMPORTANCE We assay the utility of the single-test IMX-BVN-3/IMX-SEV-3 classifiers that require just 2.5 mL of patient blood in concurrently detecting viral and bacterial infections as well as predicting the severity and 30-day outcome from the infection. A point-of-care device, in development, will circumvent the need for blood culturing and drastically reduce the time needed to detect an infection. This will negate the need for empirical use of broad-spectrum antibiotics and allow for antibiotic use stewardship. Additionally, accurate classification of the severity of infection and the prediction of 30-day severe outcomes will allow for appropriate allocation of hospital resources.

Profile of Co-Infection Prevalence and Antibiotics Use among COVID-19 Patients

Bacterial co-infection in COVID-19 patients significantly contributes to the worsening of the prognosis based on morbidity and mortality. Information on the co-infection profile in such patients could help to optimize treatment. The purpose of this study was to describe bacterial co-infections associated with microbiological, clinical, and laboratory data to reduce or avoid a secondary infection. A retrospective cohort study was conducted at Sant'Anna and San Sebastiano Hospital from January 2020 to December 2021. Bacterial co-infection was detected in 14.3% of the COVID-19-positive patients. The laboratory findings on admission showed significant alterations in the median D-dimer, C-reactive protein, interleukin-6, and lactate dehydrogenase values compared to normal values. All inflammatory markers were significantly elevated. The most common pathogens isolated from blood cultures were E. faecalis and S. aureus. Instead, the high prevalence of respiratory tract infections in the COVID-19 patients was caused by P. aeruginosa (41%). In our study, 220 (82.4%) of the COVID-19 patients received antimicrobial treatment. Aminoglycosides and β-lactams/β-lactamase inhibitors showed the highest resistance rates. Our results showed that older age, underlying conditions, and abnormal laboratory parameters can be risk factors for co-infection in COVID-19 patients. The antibiotic susceptibility profile of bacterial pathogen infection provides evidence on the importance, for the clinicians, to rationalize and individualize antibiotic usage.

The Prevalence and Impact of Coinfection and Superinfection on the Severity and Outcome of COVID-19 Infection: An Updated Literature Review

Patients with viral illness are at higher risk of secondary infections—whether bacterial, viral, or parasitic—that usually lead to a worse prognosis. In the setting of Corona Virus Disease 2019 (COVID-19), the Severe Acute Respiratory Syndrome Coronavirus-type 2 (SARS-CoV-2) infection may be preceded by a prior microbial infection or has a concurrent or superinfection. Previous reports documented a significantly higher risk of microbial coinfection in SARS-CoV-2-positive patients. Initial results from the United States (U.S.) and Europe found a significantly higher risk of mortality and severe illness among hospitalized patients with SARS-CoV-2 and bacterial coinfection. However, later studies found contradictory results concerning the impact of coinfection on the outcomes of COVID-19. Thus, we conducted the present literature review to provide updated evidence regarding the prevalence of coinfection and superinfection amongst patients with SARS-CoV-2, possible mechanisms underlying the higher risk of coinfection and superinfection in SARS-CoV-2 patients, and the impact of coinfection and superinfection on the outcomes of patients with COVID-19.

A Retrospective Analysis of the Bacterial Infections, Antibiotic Use, and Mortality Predictors of COVID-19 Patients

Purpose

This study aimed to investigate the rate and profile of bacterial infections, mortality-associated predictors, and report the most common microorganisms and antibiotic use in coronavirus disease-19 (COVID-19) patients.

Patients and Methods

This study used a retrospective approach to evaluate the bacterial culture, antibiotic use, comorbidities, imaging, and laboratory discoveries of patients with COVID-19 (hospitalized) confirmed by reverse transcription polymerase chain reaction (RT-PCR) between May and December 2020. We have selected 906 COVID-19 positive patients using a consecutive sampling technique and analyzed data using IBM SPSS-22 statistical software. Statistical analysis included univariate, bivariate, and multivariate analysis. It was carried out using multivariable logistic regression analysis to predict the mortality of COVID-19 patients.

Results

A total of 410 patients, which involved 247 males with a mean age of 53.9 years were evaluated. Based on the results, the positive bacterial culture was detected in 18.3% of all patients who sent the culture sample test, representing bacterial infections. The Acinetobacter baumannii was the most commonly identified organism, while the proportion of patients treated with antibiotics was 83.4%. Furthermore, azithromycin was prescribed in the highest number of patients with approximately 44.3% of all antibiotics. The total mortality rate was 39.8% and its ratio was higher in COVID-19 patients with bacterial infections (65.3%, X² = 25.1, P<0.001). Patients mortality who used antibiotics were also higher compared to those who did not (89% vs 11%, P<0.014). Age, length of hospitalization, bacterial infection, shortness of breath, neutrophil-to-lymphocyte ratio (NLR), and diabetes mellitus were also associated predictors to increased hospital mortality (adjusted OR (aOR) 0.382, P<0.013; aOR 4.265, P<0.001; aOR 3.720, P<0.001; aOR 3.889, P<0.001; aOR 6.839, P<0.003; aOR 1.844, P<0.030), respectively.

Conclusion

This study discovered that there is high use of antibiotics amongst COVID-19 patients; however, the bacterial infection rates did not exceed one-fifth of the total patients. Furthermore, older age, bacterial infections, a longer length of hospitalization, diabetes mellitus, shortness of breath, and higher NLR have a significant impact on the mortality of COVID-19 patients.

Predictors of severity and mortality in COVID-19 patients

Background

Due to limited capacity, health care systems worldwide have been put in challenging situations since the emergence of COVID-19. To prioritize patients who need hospital admission, a better understanding of the clinical predictors of disease severity is required. In the current study, we investigated the predictors of mortality and severity of illness in COVID-19 from a single center in Cairo, Egypt.

Methods

This retrospective cohort study included 175 patients hospitalized with COVID-19 pneumonia and had positive real-time polymerase chain reaction (RT-PCR) results for SARS-CoV-2 from 1 May 2020 to 1 December 2020. Severe COVID-19 was defined as requiring high-flow oxygen (flow rate of more than 8 L/min or use of high flow oxygen cannula), noninvasive ventilation, or invasive mechanical ventilation at any time point during the hospitalization. We used univariate and multivariate regression analyses to examine the differences in patient demographics and clinical and laboratory data collected during the first 24 h of hospitalization related to severe disease or death in all 175 patients.

Results

Sixty-seven (38.3%) of the study subjects had a severe or critical disease. Elevated d-dimer, leukocytosis, and elevated CRP were found to be independent predictors of severe disease. In-hospital mortality occurred in 34 (19.4%) of the cases. Elevated TLC, urea, the use of invasive mechanical ventilation, and the presence of respiratory bacterial co-infection were found to be independently associated with mortality.

Conclusion

Clinical and laboratory data of COVID-19 patients at their hospital admission may aid clinicians in the early identification and triage of high-risk patients.

Detection of bacterial co-infections and prediction of fatal outcomes in COVID-19 patients presenting to the emergency department using a 29 mRNA host response classifier

Objective

Clinicians in the emergency department (ED) face challenges in concurrently assessing patients with suspected COVID-19 infection, detecting bacterial co-infection, and determining illness severity since current practices require separate workflows. Here we explore the accuracy of the IMX-BVN-3/IMX-SEV-3 29 mRNA host response classifiers in simultaneously detecting SARS-CoV-2 infection, bacterial co-infections, and predicting clinical severity of COVID-19.

Methods

161 patients with PCR-confirmed COVID-19 (52.2% female, median age 50.0 years, 51% hospitalized, 5.6% deaths) were enrolled at the Stanford Hospital ED. RNA was extracted (2.5 mL whole blood in PAXgene Blood RNA) and 29 host mRNAs in response to the infection were quantified using Nanostring nCounter.

Results

The IMX-BVN-3 classifier identified SARS-CoV-2 infection in 151 patients with a sensitivity of 93.8%. Six of 10 patients undetected by the classifier had positive COVID tests more than 9 days prior to enrolment and the remaining oscillated between positive and negative results in subsequent tests. The classifier also predicted that 6 (3.7%) patients had a bacterial co-infection. Clinical adjudication confirmed that 5/6 (83.3%) of the patients had bacterial infections, i.e. Clostridioides difficile colitis (n=1), urinary tract infection (n=1), and clinically diagnosed bacterial infections (n=3) for a specificity of 99.4%. 2/101 (2.8%) patients in the IMX-SEV-3 Low and 7/60 (11.7%) in the Moderate severity classifications died within thirty days of enrollment.

Conclusions

IMX-BVN-3/IMX-SEV-3 classifiers accurately identified patients with COVID-19, bacterial co-infections, and predicted patients’ risk of death. A point-of-care version of these classifiers, under development, could improve ED patient management including more accurate treatment decisions and optimized resource utilization.

A drug-free strategy to combat bacterial infections with magnetic nanoparticles biosynthesized in bacterial pathogens

The extensive and indiscriminate use of antibiotics in the ongoing COVID-19 pandemic might significantly contribute to the growing number of multiple drug resistant (MDR) bacteria. With the dwindling pipeline of new and effective antibiotics, we might soon end up in a post-antibiotic era, in which even common bacterial infections would be a challenge to control. To prevent this, an antibiotic-free strategy would be highly desirable. Magnetic nanoparticle (MNP)-mediated hyperthermia-induced antimicrobial therapy is an attractive option as it is considered safe for human use. Given that iron and zinc are critical for bacterial virulence, we evaluated the response of multiple pathogenic bacteria to these elements. Treatment with 1 mM iron and zinc precursors resulted in the intracellular biosynthesis of MNPs in multiple Gram-positive and Gram-negative disease-causing bacteria. The superparamagnetic nanoparticles in the treated bacteria/biofilms, generated heat upon exposure to an alternating magnetic field (AMF), which resulted in an increase in the temperature (5-6 °C) of the milieu with a subsequent decrease in bacterial viability. Furthermore, we observed for the first time that virulent bacteria derived from infected samples harbour MNPs, suggesting that the bacteria had biosynthesised the MNPs using the metal ions acquired from the host. AMF treatment of the bacterial isolates from the infected specimens resulted in a strong reduction in viability (3-4 logs) as compared to vancomycin/ciprofloxacin treatment. The therapeutic efficacy of the MNPs to induce bacterial death with AMF alone was confirmed ex vivo using infected tissues. Our proposed antibiotic-free approach for killing bacteria using intracellular MNPs is likely to evolve as a promising strategy to combat a wide range of bacterial infections.

Root Causes of Fungal Coinfections in COVID-19 Infected Patients

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has infected over 200 million people, causing over 4 million deaths. COVID-19 infection has been shown to lead to hypoxia, immunosuppression, host iron depletion, hyperglycemia secondary to diabetes mellitus, as well as prolonged hospitalizations. These clinical manifestations provide favorable conditions for opportunistic fungal pathogens to infect hosts with COVID-19. Interventions such as treatment with corticosteroids and mechanical ventilation may further predispose COVID-19 patients to acquiring fungal coinfections. Our literature review found that fungal coinfections in COVID-19 infected patients were most commonly caused by Aspergillus, Candida species, Cryptococcus neoformans, and fungi of the Mucorales order. The distribution of these infections, particularly Mucormycosis, was found to be markedly skewed towards low- and middle-income countries. The purpose of this review is to identify possible explanations for the increase in fungal coinfections seen in COVID-19 infected patients so that physicians and healthcare providers can be conscious of factors that may predispose these patients to fungal coinfections in order to provide more favorable patient outcomes. After identifying risk factors for coinfections, measures should be taken to minimize the dosage and duration of drugs such as corticosteroids, immunosuppressants, and antibiotics.

A host transcriptomic signature for identification of respiratory viral infections in the community

BACKGROUND

Fever-7 is a test evaluating host mRNA expression levels of IFI27, JUP, LAX, HK3, TNIP1, GPAA1 and CTSB in blood able to detect viral infections. This test has been validated mostly in hospital settings. Here we have evaluated Fever-7 to identify the presence of respiratory viral infections in a Community Health Center.

METHODS

A prospective study was conducted in the "Servicio de Urgencias de Atención Primaria" in Salamanca, Spain. Patients with clinical signs of respiratory infection and at least one point in the National Early Warning Score were recruited. Fever-7 mRNAs were profiled on a Nanostring nCounter® SPRINT instrument from blood collected upon patient enrolment. Viral diagnosis was performed on nasopharyngeal aspirates (NPAs) using the Biofire-RP2 panel.

RESULTS

A respiratory virus was detected in the NPAs of 66 of the 100 patients enrolled. Median National Early Warning Score was 7 in the group with no virus detected and 6.5 in the group with a respiratory viral infection (P > .05). The Fever-7 score yielded an overall AUC of 0.81 to predict a positive viral syndromic test. The optimal operating point for the Fever-7 score yielded a sensitivity of 82% with a specificity of 71%. Multivariate analysis showed that Fever-7 was a robust marker of viral infection independently of age, sex, major comorbidities and disease severity at presentation (OR [CI95%], 3.73 [2.14-6.51], P < .001).

CONCLUSIONS

Fever-7 is a promising host immune mRNA signature for the early identification of a respiratory viral infection in the community.

Comparison of ROX and HACOR scales to predict high-flow nasal cannula failure in patients with SARS-CoV-2 pneumonia

The pandemic of SARSCov2 infection has created a challenge in health services worldwide. Some scales have been applied to evaluate the risk of intubation, such as the ROX and HACOR. The objective of this study is to compare the predictive capacity of the HACOR scale and the ROX index and define the optimal cut-off points. Study of diagnostic tests based on a retrospective cohort. Composite outcome was the proportion of patients that needed endotracheal intubation (ETI) or died of COVID19 pneumonia. Discrimination capacity was compared by the area under the curve of each of the two scales and the optimal cut-off point was determined using the Liu method. 245 patients were included, of which 140 (57%) required ETI and 152 (62%) had the composite end result of high-flow nasal cannula (HFNC) failure. The discrimination capacity was similar for the two scales with an area under receiver operating characteristic curve of 0.71 and 0.72 for the HACOR scale for the ROX index, respectively. The optimal cut-off point for the ROX index was 5.6 (sensitivity 62% specificity 65%), while the optimal cut-off point for the HACOR scale was 5.5 (sensitivity 66% specificity 65%). The HACOR scale and the ROX index have a moderate predictive capacity to predict failures to the HFNC strategy. They can be used in conjunction with other clinical variables to define which patients may require invasive mechanical ventilation.

The role of co-infections and secondary infections in patients with COVID-19

BACKGROUND

It has been recognised for a considerable time-period, that viral respiratory infections predispose patients to bacterial infections, and that these co-infections have a worse outcome than either infection on its own. However, it is still unclear what exact roles co-infections and/or superinfections play in patients with COVID-19 infection.

MAIN BODY

This was an extensive review of the current literature regarding co-infections and superinfections in patients with SARS-CoV-2 infection. The definitions used were those of the Centers for Disease Control and Prevention (US), which defines coinfection as one occurring concurrently with the initial infection, while superinfections are those infections that follow on a previous infection, especially when caused by microorganisms that are resistant, or have become resistant, to the antibiotics used earlier. Some researchers have envisioned three potential scenarios of bacterial/SARS-CoV-2 co-infection; namely, secondary SARS-CoV-2 infection following bacterial infection or colonisation, combined viral/bacterial pneumonia, or secondary bacterial superinfection following SARS-CoV-2. There are a myriad of published articles ranging from letters to the editor to systematic reviews and meta-analyses describing varying ranges of co-infection and/or superinfection in patients with COVID-19. The concomitant infections described included other respiratory viruses, bacteria, including mycobacteria, fungi, as well as other, more unusual, pathogens. However, as will be seen in this review, there is often not a clear distinction made in the literature as to what the authors are referring to, whether true concomitant/co-infections or superinfections. In addition, possible mechanisms of the interactions between viral infections, including SARS-CoV-2, and other infections, particularly bacterial infections are discussed further. Lastly, the impact of these co-infections and superinfections in the severity of COVID-19 infections and their outcome is also described.

CONCLUSION

The current review describes varying rates of co-infections and/or superinfections in patients with COVID-19 infections, although often a clear distinction between the two is not clear in the literature. When they occur, these infections appear to be associated with both severity of COVID-19 as well as poorer outcomes.

Epidemic and pandemic viral infections: impact on tuberculosis and the lung: A consensus by the World Association for Infectious Diseases and Immunological Disorders (WAidid), Global Tuberculosis Network (GTN), and members of the European Society of Clinical Microbiology and Infectious Diseases Study Group for Mycobacterial Infections (ESGMYC)

Major epidemics, including some that qualify as pandemics, such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), HIV, influenza A (H1N1)pdm/09 and most recently COVID-19, affect the lung. Tuberculosis (TB) remains the top infectious disease killer, but apart from syndemic TB/HIV little is known regarding the interaction of viral epidemics and pandemics with TB. The aim of this consensus-based document is to describe the effects of viral infections resulting in epidemics and pandemics that affect the lung (MERS, SARS, HIV, influenza A (H1N1)pdm/09 and COVID-19) and their interactions with TB. A search of the scientific literature was performed. A writing committee of international experts including the European Centre for Disease Prevention and Control Public Health Emergency (ECDC PHE) team, the World Association for Infectious Diseases and Immunological Disorders (WAidid), the Global Tuberculosis Network (GTN), and members of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Mycobacterial Infections (ESGMYC) was established. Consensus was achieved after multiple rounds of revisions between the writing committee and a larger expert group. A Delphi process involving the core group of authors (excluding the ECDC PHE team) identified the areas requiring review/consensus, followed by a second round to refine the definitive consensus elements. The epidemiology and immunology of these viral infections and their interactions with TB are discussed with implications for diagnosis, treatment and prevention of airborne infections (infection control, viral containment and workplace safety). This consensus document represents a rapid and comprehensive summary on what is known on the topic.