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Review

Inflammation in COVID-19: A Risk for Superinfections

1
Internal Medicine Department, Galilee Medical Center, Nahariya 221001, Israel
2
The Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
*
Author to whom correspondence should be addressed.
COVID 2022, 2(11), 1609-1624; https://doi.org/10.3390/covid2110116
Submission received: 27 October 2022 / Revised: 9 November 2022 / Accepted: 10 November 2022 / Published: 18 November 2022

Abstract

:
The worldwide coronavirus pandemic has been one of the most significant health crisis threats in recent years. COVID-19 has not been the only cause of mortality in this pandemic. A dangerous but frequent complication of viral infections is secondary superinfection or superimposed bacterial infection. Despite lacking data on the prevalence, microbiology, and outcomes of co-infection and superinfection, limited publications have reported the high incidence of severe infection in COVID-19 patients and its effect on mortality. Those who have severe clinical symptoms of the disease, and others requiring prolonged stay in intensive care units (ICU), are more susceptible to developing superinfections by nosocomial pathogens. Ventilator-acquired pneumonia (VAP) is the most common type of infection observed among COVID-19 patients, followed by bacteraemia with sepsis, and urinary tract infections (UTI). There is an urgent need for prospective studies to provide epidemiological, clinical, and microbiological data on superinfections, which can be used to form effective antimicrobial guidelines that could have an important role in disease outcomes.

1. Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was medically challenging to health systems worldwide [1,2,3,4]. Due to respiratory complications, some patients needed to be hospitalized, and in more severe cases required mechanical ventilation in intensive care units [5,6]. High rates of mortality were observed, mainly in the elderly or in adults with serious comorbidities [7].
During outbreaks of respiratory infection, treatment usually focuses on the viral infection itself and its complications, but there always remains risk of secondary infections [8]. Viral respiratory infections predispose patients to bacterial infections that worsen outcomes of the original viral infection [9]. One such example documented in microbiological studies suggests that most deaths associated with the influenza pandemic of 1918–1919 were due to secondary infections [10,11,12]. Bacterial, viral, and fungal infections are common complications that have also been reported in other influenza virus pandemics [13,14,15,16]. As with other viral outbreaks, questions began to be asked about whether this novel coronavirus is associated with super pathogens or co-pathogens [17,18]. However, with thousands of cases diagnosed in a short period of time, many clinical decisions were made without scientific evidence. One such decision was the use of antibiotics. Although COVID-19 is a viral disease, 70% of ICU patients received antibiotics in addition to immunomodulatory drugs [19,20,21,22]. Coinfections describe simultaneous viral infection, while secondary infections typically refer to bacterial secondary infection, and both have been described in COVID-19 patients [23,24]. It is believed that the high mortality rates in severely ill COVID-19 patients are due to superinfections and viral replication, leading to severe lung injury and acute respiratory distress syndrome (ARDS) [25,26,27,28,29,30,31,32]. However, there is a lack of data regarding the frequency of superinfections in COVID-19 patients. As the diagnosis and treatment approaches for superinfections are not clear, some clinicians have argued for the use of empirical antibiotics while others have called for sampling severely ill patients for early detection and treatment [33]. The aim of this paper is to review superinfections as a major complication of COVID-19 incidence and clinical outcomes.

2. The Virus: Classification and Possible Origin

SARS-CoV-2 is an enveloped and positive-sense single stranded RNA (+ssRNA) virus [34]. It belongs to the betacoronavirus family, one of the four groups of the coronoviridae [35], which also includes two highly pathogenic viruses, Severe Acute Respiratory Syndrome Human Coronavirus (SARS-CoV) and the Middle Eastern Respiratory Syndrome Coronavirus (MERS-Cov) [34]. SARS-CoV-2 is a novel human-infecting betacoronavirus, genetically different from but related to SARS-CoV and MERS-CoV. Another study found a very close relation (96.2% genome identity) with the bat coronavirus BaTCoV RaTG13 detected in rhinolofus affinis. This almost identical genome suggests bats as a possible origin of the virus [35].

3. SARS-CoV-2 Transmission and Immune Response

SARS-CoV-2 is transmitted mainly by the person-to-person route, which was confirmed by infected clusters of medical staff and family members, in addition to the animal-to-human transmission route which was seen early in the epidemic [36]. Once infected, the spike protein which covers the SARS-CoV-2 surface binds to the host cell’s angiotensin converting enzyme-2 (ACE2) receptor, mediating viral entry [37]. The next challenge for the virus is to encounter the innate immune response. Unfortunately, it is still unknown how SARS-CoV2 evades the immune response and drives its pathogenesis [28].
The inflammation and cellular anti-viral activity caused by the immune response is critical in inhibiting the viral replication. However, an exaggerated response affects the host, resulting in severe pneumonia which can rapidly deteriorate to acute respiratory distress syndrome (ARDS) [38].
When the immune reaction does not resolve after completing its mission, it becomes chronic or hyperinflammed, which can result in organ failure and tissue damage. COVID-19 is manifested by uncontrolled production of inflammatory cytokines such as IL-6, G-CSF, IP10 (Interferon gamma-induced protein 10), MCP-1 (Monocyte chemoattractant protein-1), MIP-1α (Macrophage inflammatory protein-1 alpha), TNF-α, IL-10, IL-7, and IL-2 [39,40]. In addition, in ICU patients, GSCF, MCP, and TNF-a values are significantly higher than in non-ICU patients, suggesting that the cytokine storm is closely related to the severity and mortality of the disease [38].
Depletion of CD4+ T cells in COVID-19 patients has been shown to reduce pulmonary lymphocyte recruitment and production of cytokines and antibodies, processes that lead to severe pneumonitis and delayed clearance of the virus [41]. One study demonstrated that the viral replication in lungs continues for 10 days post-infection. However, lung inflammation was more intense after clearance of the virus, peaking at 14 days and remaining until day 28, suggesting that the early inflammation phase is dependent on viral replication, but the later stages are viral independent and are caused by the hyperinflammatory response [42].

4. Clinical Features: Mortality and Morbidity

The incubation period in every case defined as COVID-19 is 14 days [43]. Viral infection was mostly seen in adult males with median ages of 32–72 [44]. Those most affected by the virus were the immunocompromised and the vulnerable, such as those with cardiovascular or cerebrovascular diseases [45,46]. Clinically, the virus has a wide spectrum of symptoms ranging from asymptomatic infections to patients suffering from cytokine storms. Mild disease is defined as presenting various symptoms of COVID-19, such as fever, cough, sore throat, malaise, headache, and muscle pain, with no pneumonia or dyspnoea. Moderately ill patients have evidence of clinical and radiologic pneumonia or lower respiratory disease, but O2 saturation is preserved above 93% in room air. Severely ill patients are those who have one of the following: Spo2 < 94% in room air, a respiratory rate above 30 breaths per minute, or PaO2/FiO2 < 300 mmHg. Critically ill patients have one of the following: respiratory failure, septic shock, or multiple organ dysfunction and failure [47,48,49,50,51,52,53].

5. Complications

The hospitalization and mortality rate for COVID-19 in China was up to 10% in adults, with men being more likely to develop severe complications [54]. Complications reported included increased coagulopathy, necrotizing pneumonia with staphylococcus aureus that was usually fatal cardiovascular complications (pericarditis, left ventricular dysfunction, myocardial infarction, arrythmias), ARDS (approximately 5% of COVID-19 patients) ventilation associated pneumonia, massive pulmonary embolism with right heart failure, sepsis, septic shock with multiple organ failure, and higher mortality risk especially with severe hyperglycaemic disease, heart failure, or the use of high doses of corticosteroids [55,56,57,58].

6. Diagnosis and Definitions of Superinfections

Superinfection can be diagnosed when a patient exhibits clinical symptoms and signs of bacteraemia or pneumonia, with a positive culture from blood samples or lower respiratory tract samples taken at least 48 h following admission [59]. One new and potential future first-line method for detecting superinfection and helping to decide the use of antimicrobials is metagenomic sequencing, as reported by Qing Miao et al. [60]. A gold standard is yet to be attained for diagnosis of ventilator-acquired pneumonia (pneumonia developing in a person on a ventilator), but reasonable criteria to diagnose VAP would be new or progressive radiographic consolidation or infiltration, with at least two of the following: temperature >38 °C, leucocytosis > 12,000 cells/mm3, leukopenia < 4000 cells/mm3, and the presence of purulent secretions [61]. Sepsis and septic shock are defined based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Sepsis is also represented by an increase in sequential organ failure assessment (SOFA) score of two or more. Septic shock is identified by maintaining mean arterial pressure (MAP) of 65 mmHg or more with vasopressors or serum lactate greater than 2 mmol/L in the absence of hypovolemia [62]. Catheter-associated UTI is defined as a new appearance of bacteriuria or funguria with more than 103 CFU/mL.

7. Superinfections: Probable Mechanism and Risk Factors

After a viral infection, immune mechanisms impair the host’s defences, leaving the patient vulnerable to bacterial and fungal proliferation [63]. Synthesis of anti-inflammatory cytokines such IL4 and IL10 and of pro-inflammatory cytokines, for example IL6, IL2, IL2R, and TNFα, have both been proven harmful to host cells. The release of these cytokines causes lung damage and exhaustion of the immune system [63,64,65].
In addition to this immune exhaustion, mechanical ventilation also predisposes patients to secondary infection [64,65,66]. Environmental microorganisms circulating in the air may colonize in the respiratory tract. A diversity of strategies are carried out on the mucosal surface of the respiratory tract to hinder pathogen invasion. Physical defences include mucus, immunoglobulins, and cilia.The initial step of the pathogenesis of ventilator-associated pneumonia (VAP) is bacterial colonization of the gastric mucosa and the oropharynx, followed by translocation of the pathogen to the lower tract. Pneumonia is acquired via aspiration promoted by the patient’s supine position and placement of the nasogastric tube [67].
Furthermore, multiple factors have also been identified as risks for infection, including being over 60 years old, kidney failure requiring dialysis, admission to the intensive care unit, pharmacological immunosuppression (steroids or biological therapy), length of stay, ICU stay, and increased time on a ventilator (Table 1) [68].

8. Inflammatory State: Friendly Fire?

A state of hypo-inflammation that develops following a systemic inflammation can help to limit the cytokine response but may also cause a phase of immune dysfunction [69,70]. Epigenetic reprogramming is related to toll like receptor (TLR) activation that silences pro-inflammatory activity [71]. The activation of TLR results in stimulation of cellular metabolism which occurs in parallel with the immune-adaptive phase [70]. Following this hypo-inflammatory phase, immune dysfunction dominates with three probable processes. First is the shift to anti-inflammatory cytokines with prominent programmed death 1 (PD-1), as observed in immune paralysis especially in HIV patients [71]. Secondly, the loss of cells of the adaptive immune system is induced by apoptosis; and, thirdly, the decreased release of T-cell cytokines impairing response to antigens is considered a major cause of immune dysfunction during sepsis [72,73]. The phase of profound immune dysfunction results in serious consequences; its duration depends on the age of the patient [74], comorbidities, and the severity and location of infection [75].
Levels of soluble urokinase plasminogen receptor (suPAR) are a biomarker of chronic inflammation [76]. In the SAVE MORE trial, an increase of soluble urokinase plasminogen receptor levels (suPAR) in COVID-19 patients was indicative of increased risk of disease progression and respiratory failure. Treatment with anakinra (IL-1 inhibitor) significantly improved clinical outcomes and was associated with lower incidence of secondary infections [77].

9. Steroid Therapy and Superinfection

Use of dexamethasone improved survival in patients with severe COVID-19 in randomized trials. Based on the published results of the WHO REACT meta-analysis and the RECOVERY trial reports, physicians increased the use of dexamethasone in the treatment of severely ill patients, though multiple other studies contraindicated the use of glucocorticoids in the treatment guidelines for COVID-19 [78,79,80,81,82,83]. In spite of this, since dexamethasone-treated patients with influenza, MERS, and SARS had lower viral clearance [84,85], a recent study used single-cell RNA sequencing and plasma proteomics to compare dexamethasone users versus non-dexamethasone users. Results indicated that dexamethasone altered immune dynamics by downregulating the neutrophil proportion by the seventh day of treatment, suppressed ten host defensive proteins, restrained the neutrophil-programming systems, and rendered them more immunosuppressive [86,87]. In addition to the wide range of side effects (e.g., gastrointestinal bleeding, metabolic complications, hormonal imbalance, fluid retention, weight gain, anxiety, and disturbed sleep patterns), secondary infections were also found to be positively correlated with dexamethasone use [68].

10. Platelets Induce Immunosuppression

Platelets are key in inducing COVID-19-related systemic inflammation, resulting in the development of acute respiratory stress syndrome, cardiac dysfunction, and generalized immunosuppression, and complicating the outcomes of the acute viral infection [88,89,90,91]. Derived from megakaryocytes, platelets express angiotensin converting enzyme 2 (ACE2) that causes the platelets’ activation [92]. In early-stage disease, platelet activation is potentially protective. However, uncontrolled hyper-activation results in systemic immunosuppression and microvascular occlusion [93,94,95]. Multiple mechanisms causing immunosuppression driven by platelets have been identified: pro-inflammatory/pro-thrombotic, cytosolic group box 1 (HMGB1) protein release, formation of intravascular aggregates of neutrophils, T cells, and monocytes with platelets causing inappropriate activation of leukocytes, polarization of immunosuppressive macrophages and myeloid-derived suppressor cells (MDSCs) [96,97] mediated by transforming growth factor-β (TGF-β) [96,97,98,99,100,101,102,103,104,105]. These mechanisms and others are likely to contribute to the massive immunosuppression associated with severe COVID-19.

11. Tocilizumab: A Potential Risk Factor?

Interleukin (IL) 6 is a pro-inflammatory cytokine that functions in immunologic responses during host infection, inflammatory disease, and hematopoiesis. It can also exhibit anti-inflammatory effects. Tocilizumab is a monoclonal antibody that competitively inhibits the binding of IL-6 to receptors and membrane [106,107,108]. It is used as a treatment for giant cell arteritis, systemic juvenile idiopathic arthritis, rheumatoid arthritis, and life-threatening cytokine release syndrome [109,110,111,112]. In COVID-19 patients, an increase in IL-6 levels is observed [113,114,115]. Tocilizumab has been given to COVID-19 patients, and improved mortality and other clinical outcomes in hospitalized COVID-19 patients who required oxygen or invasive or non-invasive life-support, demonstrated by the RECOVERY, EMPACTA, REMDACTA and CONVACTA trials [116,117,118,119]. After the emergent approval of the use of tocilizumab in the treatment of COVID-19 patients, higher rates of secondary bacterial infections were seen in some studies after the use of IL-6 inhibitors [120,121,122,123]. Other researchers reported lower risk of superinfection following the use of tocilizumab, attributing the lower risk on one hand to IL-6 inhibitor’s ability to suppress sepsis-related immune exhaustion, and on the other hand to the lower risk of clinical deterioration and the need for mechanical ventilation in the tocilizumab group [124,125,126]. Other papers indicated no statistical significance in the emergence of secondary infection after the use of tocilizumab [127]. Difference of outcomes might be attributed to the lack of a standard definition of superinfection, and the unadjusted analysis in some studies.

12. Inadequate Infection Control Strategies

Infection-control practices can control and prevent hospital-acquired infections. During the COVID-19 pandemic and due to the lack of prior forecasting, hospitals faced a severe shortage of disinfectants, leading to inadequate infection-control strategies especially in ICUs. Heavy workloads, a higher nurse-to-patient ratio (due to the large influx of patients and the high infection rate among healthcare staff), shorter rest periods between shifts, as well as unsuitable design of physical hospital spaces (COVID-19 ICUs located in departments without proper facilities), lack of equipment, and poor-quality equipment were all identified as causes of increased incidence of secondary infections [128]. In addition, fear was a significant factor among healthcare workers during the SARS pandemic [129]. Increasing risk of transmission of SARS-CoV-2 and other events including secondary bacterial infection resulted due to the disregarding of evidence-based practices. Meanwhile, there was an increase of unnecessary prevention practices and personal protective equipment, which worsened the personal equipment shortages [130]. All of the above important parameters contributed to the high incidence of secondary infections.

13. Incidence and Common Pathogens

The prevalence of superinfections in COVID-19 patients is heterogeneous according to different studies, with differences over 50% depending on site infected, comorbidities, and immunosuppressant therapies. Studies found an occurrence rate of 4% to 41% of bacterial superinfection following influenza. Unfortunately, with COVID-19, the rate of infections might exceed other viral syndromes [131,132,133,134]. A large meta-analysis which screened over 6639 articles on COVID-19 superinfections and co-infections revealed that the incidence of superinfection was 24%, with bacterial pathogens the leading cause accounting for around 20%, followed by fungal superinfection at 8% and viral superinfection at 4% [135]. Several studies have tried to quantify the risk of superinfection in COVID-19 patients. Garcia-Vidal et al. investigated rates of these infections among 989 hospitalized patients with COVID-19 in Spain and found 4.7% with pseudomonas and E. coli, the main causative agents. The majority of the superinfections detected were bacterial, with the respiratory tract the most frequent site, and the infections were mostly acquired in the ICU department [136]. Reports from China of COVID-19 patients describe rates as high as 42% developing superinfection [137]. Superinfection rates in the UK were 9.3%, with coagulase-negative staphylococci as the main pathogen [138]. VAP is one of the most frequent ICU-acquired infections. Its incidence ranges from 5% to 40% depending on the setting and diagnostic criteria, and it is associated with prolonged stay and ventilation. A study from Italy documented several pathogens causing superinfection in hospitalized patients and reported ventilator-related superinfection in 319 patients (Table 2) [139]. Staphylococcus aureus, Streptococcus pneumonia, and Haemophilus influenza were among the most common pathogens in bacterial superinfections. Up to 90% of superinfected patients required invasive mechanical ventilation, and their course was very severe with mortality reaching 57% in the ICU [133,134,135]. The presence of secondary bacterial infections in patients with COVID-19 prolongs hospitalization, complicates the treatment, and worsen prognosis [140,141,142,143].

14. CRP and Procalcitonin as Biomarkers of Superinfections

Procalcitonin (PCT), the precursor of calcitonin, is used in clinical practice as a biomarker to diagnose sepsis of bacterial infection and to differentiate viral from bacterial pneumonia [144,145,146]. Its production is mediated by proinflammatory proteins, in particular IL-6, IL-1β, and TNF-α, activated by bacterial infection [147]. However, viral infections inhibit the production of procalcitonin due to the secretion of interferon-γ by lymphocytes [148]. C-reactive protein (CRP) is an acute phase protein that increases with inflammation or infection. It is synthesized primarily by liver hepatocytes in response to pro-inflammatory cytokines, especially IL-6 [149]. Procalcitonin is more sensitive and specific than CRP for the prognosis and diagnosis of sepsis [150,151]. During the H1N1 pandemic, procalcitonin and CRP were utilized to assess viral and bacterial pneumonia, with a combined sensitivity and specificity of 50% and 93%, respectively. A combination of low CRP and procalcitonin suggests that the pneumonia is not caused by a bacterial infection [152]. In COVID-19 patients, CRP is usually increased on presentation, while low levels of procalcitonin are seen in many cases [153]. Secondary infections and severe disease cause procalcitonin levels to rise [154]. In one study, significant increases in levels of procalcitonin and CRP were observed in patients who had secondary infections [155]. Another study compared CRP and procalcitonin after dexamethasone and tocilizumab treatment, also showing lower levels of CRP and procalcitonin especially in the dexamethasone group. The study also reported higher false positive results in the detection of secondary infections, explained by limited increase in values after normalization of both proteins may indicate limited diagnostic ability, especially in patients treated with dexamethasone [156].

15. Antimicrobials

Because it is difficult to rule out bacterial or fungal infection based on signs and symptoms alone, empirical antimicrobial antibiotics have been used rather than infection-guided treatment. In one review from China, 70% of COVID-19 patients received antimicrobial therapy, but only 10% had bacterial coinfections or super-infections [22]. Early administration of antibiotics in an empirical study did not show any effect on mortality or length of stay, according to retrospective comparison of ICU patients who received antibiotics and patients who did not [157]. Azithromycin, carbapenems, vancomycin, cephalosporin, and fluoroquinolones were among the most common antibiotics empirically used in China, with voriconazole being applied to treat fungal infection [157,158,159,160,161,162]. Since there were no clear guidelines for the use of antibiotics, one new method to help decisions on using antibiotics was metagenomics sequencing, which demonstrated a clinical benefit especially in severely ill patients [60]. Another problem of using antibiotics empirically is that this type of use promotes bacterial resistance, helping bacteria gain resistant properties [163,164]. An algorithm suggesting the use of antimicrobials was published by Duendas et al. [165].

16. Superinfection, Worsen Prognosis?

As observed for influenza virus with a lethal synergism, superinfection may affect outcomes in COVID-19 [166,167]. A large retrospective study in ICU patients with COVID-19 with 17,534 admitted patients mortality rates almost five times more with secondary infected patients versus no secondary infection reported [168].

17. Metabolomics and Metagenomics Sequencing

In sepsis, early administration of antibiotics reduces mortality [169]. However, unnecessary use of antibiotics could promote antimicrobial resistance and side effects. It is therefore important for optimal management to diagnose early secondary infections and to try to identify the causative pathogen. Cultures are time consuming, with low sensitivity [170]. A potential diagnostic tool that has recently gained attention uses liquid chromatography-mass spectrometry (LCMS) to provide metabolic profiles of patients according to environmental and genetic factors [171]. It identifies multiple metabolites (derangement of fatty acid, lysophosphatidylcholines, tricarboxylic acid cycle intermediates, citrate, malate in response to Gram + pathogens etc.) [172,173,174,175,176]. In our opinion, using these metabolites could help the increase the speed of diagnosis for septic patients and improve their treatment accordingly.
Metagenomics sequencing is a fast-evolving technology, which can currently identify pathogens within one day. It may also provide information on the microbiome, and uncover coinfection and secondary infections in addition to SARS-CoV-2. It is more sensitive and specific than conventional methods of bacterial detection, thus it is advantageous in the present pandemic [177]. One study using metagenomics sequencing revealed that enterobacterales in respiratory samples increase the risk of ARDS [178]. In another study, this technique identified non-negligible rates of secondary infection [60]. Therefore, this method is an effective tool for deciding on antimicrobials, especially in critically ill patients.

18. Conclusions

Superinfection is a major risk factor of poor outcomes in COVID-19 patients; conversely, patients with severe disease are more prone to secondary infections. Rapid detection and treatment may improve outcomes, especially in severely ill patients. It is therefore important to acquire new methods to detect secondary infections, and to adjust antimicrobials based on the results.

Author Contributions

M.B. (Mariana Boulos) and N.A. conceived the idea; M.B. (Mariana Boulos), T.B., M.B. (Maamoun Basheer), A.L. performed the literature search; M.B. (Mariana Boulos) wrote the manuscript; M.B. (Mariana Boulos) and N.A. critically corrected the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef]
  2. Guan, W.; Ni, Z.; Hu, Y.; Liang, W.; Ou, C.; He, J. Clinical characteristics of coronavirus disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
  3. Lupia, T.; Scabini, S.; Pinna, S.M.; Di Perri, G.; De Rosa, F.G.; Corcione, S. 2019 novel coronavirus (2019-nCoV) outbreak: A new challenge. J. Glob. Antimicrob. Resist. 2020, 21, 22–27. [Google Scholar] [CrossRef] [PubMed]
  4. Wu, Z.; McGoogan, J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020, 323, 1239–1242. [Google Scholar] [CrossRef] [PubMed]
  5. Zu, Z.; Jiang, M.; Xu, P.; Chen, W.; Ni, Q.; Lu, G.; Zhang, L. Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology 2020, 296, E15–E25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Möhlenkamp, S.; Thiele, H. Ventilation of COVID-19 patients in intensive care units. Herz 2020, 45, 329–331. [Google Scholar] [CrossRef] [PubMed]
  7. Velavan, T.; Meyer, C. The COVID-19 epidemic. Trop. Med. Int. Health 2020, 25, 278–280. [Google Scholar] [CrossRef] [Green Version]
  8. Del Pozo, J.L. Respiratory co-and superinfections in COVID-19. Rev. Esp. Quimioter. 2021, 34 (Suppl. 1), 69–71. [Google Scholar] [CrossRef]
  9. Arnold, F.W.; Fuqua, J.L. Viral respiratory infections: A cause of communityacquired pneumonia or a predisposing factor? Curr. Opin. Pulm. Med. 2020, 26, 208–214. [Google Scholar] [CrossRef]
  10. Brundage, J.F.; Shanks, G.D. Deaths from Bacterial Pneumonia during 1918–19 Influenza Pandemic. Emerg. Infect. Dis. 2008, 14, 1193–1199. [Google Scholar] [CrossRef]
  11. Morris, D.E.; Cleary, D.W.; Clarke, S.C. Secondary Bacterial Infections Associated with Influenza Pandemics. Front. Microbiol. 2017, 8, 1041. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Morens, D.M.; Taubenberger, J.K.; Fauci, A.S. Predominant Role of Bacterial Pneumonia as a Cause of Death in Pandemic Influenza: Implications for Pandemic Influenza Preparedness. J. Infect. Dis. 2008, 198, 962–970. [Google Scholar] [CrossRef] [PubMed]
  13. Burrell, A.; Huckson, S.; Pilcher, D.V. ICU Admissions for Sepsis or Pneumonia in Australia and New Zealand in 2017. N. Engl. J. Med. 2018, 378, 2138–2139. [Google Scholar] [CrossRef] [PubMed]
  14. Abelenda-Alonso, G.; Rombauts, A.; Gudiol, C.; Meije, Y.; Ortega, L.; Clemente, M.; Ardanuy, C.; Niubó, J.; Carratala, J. Influenza and Bacterial Coinfection in Adults With Community-Acquired Pneumonia Admitted to Conventional Wards: Risk Factors, Clinical Features, and Outcomes. Open Forum Infect. Dis. 2020, 7, ofaa066. [Google Scholar] [CrossRef] [Green Version]
  15. Voiriot, G.; Visseaux, B.; Cohen, J.; Nguyen, L.B.L.; Neuville, M.; Morbieu, C.; Burdet, C.; Radjou, A.; Lescure, F.-X.; Smonig, R.; et al. Viral-bacterial coinfection affects the presentation and alters the prognosis of severe community-acquired pneumonia. Crit. Care 2016, 20, 375. [Google Scholar] [CrossRef] [Green Version]
  16. Martin-Loeches, I.; Schultz, M.J.; Vincent, J.L.; Alvarez-Lerma, F.; Bos, L.D.; Solé-Violán, J.; Torres, A.; Rodriguez, A. Increased incidence of co-infection in critically ill patients with influenza. Intensive Care Med. 2017, 43, 48–58. [Google Scholar] [CrossRef]
  17. Bengoechea, J.A.; Bamford, C.G.G. SARS-CoV-2, bacterial co-infections, and AMR: The deadly trio in COVID-19? EMBO Mol. Med. 2020, 12, e12560. [Google Scholar] [CrossRef]
  18. Chen, J. COVID-19 Scientific Advisory Group Rapid Response Report 2020. Available online: https://www.albertahealthservices.ca/assets/info/ppih/if-ppih-Covid-19-sag-anti-microbial-use-forsecondary-infections-rapid-review.pdf (accessed on 6 May 2020).
  19. Zhu, N.; Zhang, D.; Wang, W.; Li, X.; Yang, B.; Song, J.; Zhao, X.; Huang, B.; Shi, W.; Lu, R.; et al. China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumoniain China, 2019. N. Engl. J. Med. 2020, 382, 727–733. [Google Scholar] [CrossRef]
  20. Wu, C.; Chen, X.; Cai, Y.; Xia, J.; Zhou, X.; Xu, S.; Huang, H.; Zhang, L.; Zhou, X.; Du, C.; et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern. Med. 2020, 180, 934–943. [Google Scholar] [CrossRef] [Green Version]
  21. Bendala Estrada, A.D.; Calderon Parra, J.; Fernandez Carracedo, E.; Muino Miguez, A.; Ramos Martinez, A.; Munez Rubio, E.; Rubio-Rivas, M.; Agudo, P.; Arnalich Fernandez, F.; Estrada Perez, V.; et al. Inadequate use of antibiotics in the COVID-19 era: Effectiveness of antibiotic therapy. BMC Infect. Dis. 2021, 21, 1144. [Google Scholar] [CrossRef]
  22. Schouten, J.; De Waele, J.; Lanckohr, C.; Koulenti, D.; Haddad, N.; Rizk, N.; Sjövall, F.; Kanj, S.S.; Alliance for the Prudent Use of Antibiotics. Antimicrobial stewardship in the ICU in COVID-19 times: The known unknowns. Int. J. Antimicrob. Agents 2021, 58, 106409. [Google Scholar] [CrossRef] [PubMed]
  23. Kreitmann, L.; Monard, C.; Dauwalder, O.; Simon, M.; Argaud, L. Early bacterial co-infection in ARDS related to COVID-19. Intensive Care Med. 2020, 46, 1787–1789. [Google Scholar] [CrossRef] [PubMed]
  24. Rawson, T.M.; Moore, L.S.P.; Zhu, N.; Ranganathan, N.; Skolimowska, K.; Gilchrist, M.; Satta, G.; Cooke, G.; Holmes, A.H. Bacterial and Fungal Coinfection in Individuals With Coronavirus: A Rapid Review To Support COVID-19 Antimicrobial Prescribing. Clin. Infect. Dis. 2020, 71, 2459–2468. [Google Scholar] [CrossRef] [PubMed]
  25. Toloui, A.; Moshrefiaraghi, D.; Madani Neishaboori, A.; Yousefifard, M.; Haji Aghajani, M. Cardiac Complications and Pertaining Mortality Rate in COVID-19 Patients; a Systematic Review and Meta-Analysis. Arch. Acad. Emerg. Med. 2021, 9, e18. [Google Scholar] [CrossRef]
  26. Lalani, K.; Seshadri, S.; Samanth, J.; Thomas, J.J.; Rao, M.S.; Kotian, N.; Satheesh, J.; Nayak, K. Cardiovascular complications and predictors of mortality in hospitalized patients with COVID-19: A cross-sectional study from the Indian subcontinent. Trop. Med. Health 2022, 50, 55. [Google Scholar] [CrossRef]
  27. Yang, X.; Yu, Y.; Xu, J.; Shu, H.; Xia, J.; Liu, H.; Wu, Y.; Zhang, L.; Yu, Z.; Fang, M.; et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir. Med. 2020, 8, 475–481. [Google Scholar] [CrossRef] [Green Version]
  28. Chen, N.; Zhou, M.; Dong, X.; Qu, J.; Gong, F.; Han, Y.; Qiu, Y.; Wang, J.; Liu, Y.; Wei, Y.; et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020, 395, 507–513. [Google Scholar] [CrossRef] [Green Version]
  29. Zhang, J.; Zhou, L.; Yang, Y.; Peng, W.; Wang, W.; Chen, X. Therapeutic and triage strategies for 2019 novel coronavirus disease in fever clinics. Lancet Respir. Med. 2020, 8, e11–e12. [Google Scholar] [CrossRef] [Green Version]
  30. Ruan, Q.; Yang, K.; Wang, W.; Jiang, L.; Song, J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 2020, 46, 846–848. [Google Scholar] [CrossRef] [Green Version]
  31. He, Y.; Li, W.; Wang, Z.; Chen, H.; Tian, L.; Liu, D. Nosocomial infection among patients with COVID-19: A retrospective data analysis of 918 cases from a single center in Wuhan, China. Infect. Control Hosp. Epidemiol. 2020, 41, 982–983. [Google Scholar] [CrossRef]
  32. Cox, M.J.; Loman, N.; Bogaert, D.; O’Grady, J. Co-infections: Potentially lethal and unexplored in COVID-19. Lancet Microbe 2020, 1, e11. [Google Scholar] [CrossRef]
  33. Daria, S.; Islam, M.R. Indiscriminate Use of Antibiotics for COVID-19 Treatment in South Asian Countries is a Threat for Future Pandemics Due to Antibiotic Resistance. Clin. Pathol. 2022, 15, 2632010X221099889. [Google Scholar] [CrossRef] [PubMed]
  34. Lu, R.; Zhao, X.; Li, J.; Niu, P.; Yang, B.; Wu, H.; Wang, W.; Song, H.; Huang, B.; Zhu, N.; et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins andreceptor binding. Lancet 2020, 395, 565–574. [Google Scholar] [CrossRef] [Green Version]
  35. Guo, Y.R.; Cao, Q.D.; Hong, Z.S.; Tan, Y.Y.; Chen, S.D.; Jin, H.J.; Tan, K.S.; Wang, D.Y.; Yan, Y. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak—An update on the status. Mil. Med. Res. 2020, 7, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Letko, M.; Marzi, A.; Munster, V. Functional assessment of cell entry and receptor usage for SARS-CoV-2 and other lineage B betacoronaviruses. Nat. Microbiol. 2020, 5, 562–569. [Google Scholar] [CrossRef] [Green Version]
  37. Shang, J.; Wan, Y.; Luo, C.; Ye, G.; Geng, Q.; Auerbach, A.; Li, F. Cell entry mechanisms of SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2020, 117, 11727–11734. [Google Scholar] [CrossRef]
  38. Vaninov, N. In the eye of the COVID-19 cytokine storm. Nat. Rev. Immunol. 2020, 20, 277. [Google Scholar] [CrossRef]
  39. Chousterman, B.G.; Swirski, F.K.; Weber, G.F. Cytokine storm and sepsis disease pathogenesis. Semin. Immunopathol. 2017, 39, 517–528. [Google Scholar] [CrossRef]
  40. Chen, J.; Lau, Y.F.; Lamirande, E.W.; Paddock, C.D.; Bartlett, J.H.; Zaki, S.R.; Subbarao, K. Cellular Immune Responses to Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) Infection in Senescent BALB/c Mice: CD4+ T Cells Are Important in Control of SARS-CoV Infection. J. Virol. 2010, 84, 1289–1301. [Google Scholar] [CrossRef] [Green Version]
  41. Clay, C.; Donart, N.; Fomukong, N.; Knight, J.B.; Lei, W.; Price, L.; Hahn, F.; Van Westrienen, J.; Harrod, K.S. Primary Severe Acute Respiratory Syndrome Coronavirus Infection Limits Replication but Not Lung Inflammation upon Homologous Rechallenge. J. Virol. 2012, 86, 4234–4244. [Google Scholar] [CrossRef]
  42. WHO. Global Surveillance for Human Infection with Novel Coronavirus (2019-nCoV); World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
  43. Wu, Y.; Kang, L.; Guo, Z.; Liu, J.; Liu, M.; Liang, W. Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis. JAMA Netw. Open 2022, 5, e2228008. [Google Scholar] [CrossRef] [PubMed]
  44. Fu, L.; Wang, B.; Yuan, T.; Chen, X.; Ao, Y.; Fitzpatrick, T.; Li, P.; Zhou, Y.; Lin, Y.-F.; Duan, Q.; et al. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. J. Infect. 2020, 80, 656–665. [Google Scholar] [CrossRef] [PubMed]
  45. Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus—Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061–1069. [Google Scholar] [CrossRef] [PubMed]
  46. Basheer, M.; Saad, E.; Shlezinger, D.; Assy, N. Convalescent Plasma Reduces Mortality and Decreases Hospitalization Stay in Patients with Moderate COVID-19 Pneumonia. Metabolites 2021, 11, 761. [Google Scholar] [CrossRef] [PubMed]
  47. Duan, K.; Liu, B.; Li, C.; Zhang, H.; Yu, T.; Qu, J.; Zhou, M.; Chen, L.; Meng, S.; Hu, Y.; et al. Effectiveness of convalescent plasma therapy in severe COVID-19 patients. Proc. Natl. Acad. Sci. USA 2020, 117, 9490–9496. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Panel, C.T.G. Coronavirus disease 2019 (COVID-19) Treatment Guidelines. Available online: https://Covid-19treatmentguidelines.nih.gov/introduction/ (accessed on 18 August 2021).
  49. Wong, J.P.; Viswanathan, S.; Wang, M.; Sun, L.-Q.; Clark, G.C.; D’Elia, R.V. Current and future developments in the treatment of virus-induced hypercytokinemia. Future Med. Chem. 2017, 9, 169–178. [Google Scholar] [CrossRef] [PubMed]
  50. Mustafa, M.I.; Abdelmoneim, A.H.; Mahmoud, E.M.; Makhawi, A.M. Cytokine Storm in COVID-19 Patients, Its Impact on Organs and Potential Treatment by QTY Code-Designed Detergent-Free Chemokine Receptors. Mediat. Inflamm. 2020, 2020, 8198963. [Google Scholar] [CrossRef]
  51. Tisoncik, J.R.; Korth, M.J.; Simmons, C.P.; Farrar, J.; Martin, T.R.; Katze, M.G. Into the eye of the cytokine storm. Microbiol. Mol. Biol. Rev. 2012, 76, 16–32. [Google Scholar] [CrossRef] [Green Version]
  52. Xu, Z.; Shi, L.; Wang, Y.; Zhang, J.; Huang, L.; Zhang, C.; Liu, S.; Zhao, P.; Liu, H.; Zhu, L.; et al. Pathological findings of COVID-19 associated with acute respiratory distress syndrome. Lancet Respir. Med. 2020, 8, 420–422. [Google Scholar] [CrossRef]
  53. Du, F.; Liu, B.; Zhang, S. COVID-19: The role of excessive cytokine release and potential ACE2 down-regulation in promoting hypercoagulable state associated with severe illness. J. Thromb. Thrombolysis 2021, 51, 313–329. [Google Scholar] [CrossRef]
  54. Azer, S. COVID-19: Pathophysiology, diagnosis, complications and investigational therapeutics. New Microbes New Infect. 2020, 37, 100738. [Google Scholar] [CrossRef] [PubMed]
  55. Duployez, C.; Le Guern, R.; Tinez, C.; Lejeune, A.-L.; Robriquet, L.; Six, S.; Loïez, C.; Wallet, F. Panton-Valentine Leukocidin-Secreting Staphylococcus aureus Pneumonia Complicating COVID-19. Emerg. Infect. Dis. 2020, 26, 1939–1941. [Google Scholar] [CrossRef]
  56. Kluge, S.; Janssens, U.; Welte, T.; Weber-Carstens, S.; Marx, G.; Karagiannidis, C. German recommendations for critically ill patients with COVID-19. Med. Klin.-Intensiv. Notfallmedizin 2020, 115, 111–114. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Ullah, W.; Saeed, R.; Sarwar, U.; Patel, R.; Fischman, D.L. COVID-19 Complicated by Acute Pulmonary Embolism and Right-Sided Heart Failure. JACC Case Rep. 2020, 2, 1379–1382. [Google Scholar] [CrossRef]
  58. Li, X.; Xu, S.; Yu, M.; Wang, K.; Tao, Y.; Zhou, Y. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J. Allergy Clin. Immunol. 2020, 146, 110–118. [Google Scholar] [CrossRef] [PubMed]
  59. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [Google Scholar] [CrossRef] [Green Version]
  60. Miao, Q.; Ma, Y.; Ling, Y.; Jin, W.; Su, Y.; Wang, Q.; Pan, J.; Zhang, Y.; Chen, H.; Yuan, J.; et al. Evaluation of superinfection, antimicrobial usage, and airway microbiome with metagenomic sequencing in COVID-19 patients: A cohort study in Shanghai. J. Microbiol. Immunol. Infect. 2021, 54, 808–815. [Google Scholar] [CrossRef] [PubMed]
  61. Kalanuria, A.A.; Zai, W.; Mirski, M. Ventilator-associated pneumonia in the ICU. Crit. Care 2014, 18, 208. [Google Scholar] [CrossRef] [Green Version]
  62. Singer, M.; Deutschman, C.S.; Seymour, C.W.; Shankar-Hari, M.; Annane, D.; Bauer, M.; Bellomo, R.; Bernard, G.R.; Chiche, J.-D.; Coopersmith, C.M.; et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016, 315, 801–810. [Google Scholar] [CrossRef]
  63. Schultz-Cherry, S. Viral Interference: The Case of Influenza Viruses. J. Infect. Dis. 2015, 212, 1690–1691. [Google Scholar] [CrossRef]
  64. Hendaus, M.A.; Jomha, F.A. COVID-19 induced superimposed bacterial infection. J. Biomol. Struct. Dyn. 2020, 39, 4185–4191. [Google Scholar] [CrossRef] [PubMed]
  65. Pedersen, S.F.; Ho, Y.C. SARS-CoV-2: A storm is raging. J. Clin. Investig. 2020, 130, 2202–2205. [Google Scholar] [CrossRef] [PubMed]
  66. LeMessurier, K.S.; Tiwary, M.; Morin, N.P.; Samarasinghe, A.E. Respiratory Barrier as a Safeguard and Regulator of Defense Against Influenza A Virus and Streptococcus pneumoniae. Front. Immunol. 2020, 11, 3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Goel, V.; Gupta, S.; Goel, T. Ventilator-associated pneumonia: A review of the clinically relevant challenges in diagnosis and prevention. Br. J. Med. Pract. 2016, 9, a910. [Google Scholar]
  68. Søvik, S.; Barratt-Due, A.; Kåsine, T.; Olasveengen, T.; Strand, M.W.; Tveita, A.A.; Berdal, J.E.; Lehre, M.A.; Lorentsen, T.; Heggelund, L.; et al. Corticosteroids and superinfections in COVID-19 patients on invasive mechanical ventilation. J. Infect. 2022, 85, 57–63. [Google Scholar] [CrossRef]
  69. McCall, C.E.; El Gazzar, M.; Liu, T.; Vachharajani, V.; Yoza, B. Epigenetics, bioenergetics, and microRNA coordinate gene-specific reprogramming during acute systemic inflammation. J. Leukoc. Biol. 2011, 90, 439–446. [Google Scholar] [CrossRef] [Green Version]
  70. McCall, C.E.; Yoza, B.K. Gene Silencing in Severe Systemic Inflammation. Am. J. Respir. Crit. Care Med. 2007, 175, 763–767. [Google Scholar] [CrossRef] [Green Version]
  71. Said, E.; Dupuy, F.P.; Trautmann, L.; Zhang, Y.; Shi, Y.; El-Far, M.; Hill, B.J.; Noto, A.; Ancuta, P.; Peretz, Y.; et al. Programmed death-1–induced interleukin-10 production by monocytes impairs CD4+ T cell activation during HIV infection. Nat. Med. 2010, 16, 452–459. [Google Scholar] [CrossRef] [Green Version]
  72. Hotchkiss, R.S.; Karl, I.E. The Pathophysiology and Treatment of Sepsis. N. Engl. J. Med. 2003, 348, 138–150. [Google Scholar] [CrossRef] [Green Version]
  73. De Gaudio, A.R.; Rinaldi, S.; Chelazzi, C.; Borracci, T. Pathophysiology of sepsis in the elderly: Clinical impact and therapeutic considerations. Curr. Drug Targets 2009, 10, 60–70. [Google Scholar] [CrossRef]
  74. Rosolem, M.M.; Rabello, L.S.; Lisboa, T.; Caruso, P.; Costa, R.T.; Leal, J.V.; Salluh, J.I.; Soares, M. Critically ill patients with cancer and sepsis: Clinical course and prognostic factors. J. Crit. Care 2012, 27, 301–307. [Google Scholar] [CrossRef] [PubMed]
  75. Gogos, C.; Kotsaki, A.; Pelekanou, A.; Giannikopoulos, G.; Vaki, I.; Maravitsa, P.; Adamis, S.; Alexiou, Z.; Andrianopoulos, G.; Antonopoulou, A.; et al. Early alterations of the innate and adaptive immune statuses in sepsis according to the type of underlying infection. Crit. Care 2010, 14, R96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Rasmussen, L.J.H.; Petersen, J.E.V.; Eugen-Olsen, J. Soluble Urokinase Plasminogen Activator Receptor (suPAR) as a Biomarker of Systemic Chronic Inflammation. Front. Immunol. 2021, 12, 780641. [Google Scholar] [CrossRef] [PubMed]
  77. Kyriazopoulou, E.; Poulakou, G.; Milionis, H.; Metallidis, S.; Adamis, G.; Tsiakos, K.; Fragkou, A.; Rapti, A.; Damoulari, C.; Fantoni, M.; et al. Early treatment of COVID-19 with anakinra guided by soluble urokinase plasminogen receptor plasma levels: A double-blind, randomized controlled phase 3 trial. Nat. Med. 2021, 27, 1752–1760. [Google Scholar] [CrossRef] [PubMed]
  78. Collaborative Group RECOVERY. Dexamethasone in hospitalized patients with COVID-19—Preliminary report. N. Engl. J. Med. 2020, 384, 693–704. [Google Scholar]
  79. WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group. Association between administration of systemic corticosteroids and mortality among critically ill patients with COVID-19: A meta-analysis. JAMA 2020, 324, 1330–1341. [Google Scholar] [CrossRef]
  80. Villar, J.; Ferrando, C.; Martínez, D.; Ambrós, A.; Muñoz, T.; Soler, J.A.; Aguilar, G.; Alba, F.; González-Higueras, E.; Conesa, L.A.; et al. Dexamethasone treatment for the acute respiratory distress syndrome: A multicentre, randomised controlled trial. Lancet Respir. Med. 2020, 8, 267–276. [Google Scholar] [CrossRef]
  81. Dagens, A.; Sigfrid, L.; Cai, E.; Lipworth, S.; Cheung, V.; Harris, E.; Bannister, P.; Rigby, I.; Horby, P. Scope, quality, and inclusivity of clinical guidelines produced early in the COVID-19 pandemic: Rapid review. BMJ 2020, 369, m1936. [Google Scholar] [CrossRef]
  82. Arabi, Y.M.; Mandourah, Y.; Al-Hameed, F.; Sindi, A.A.; Almekhlafi, G.A.; Hussein, M.A.; Jose, J.; Pinto, R.; Al-Omari, A.; Kharaba, A.; et al. Corticosteroid Therapy for Critically Ill Patients with Middle East Respiratory Syndrome. Am. J. Respir. Crit. Care Med. 2018, 197, 757–767. [Google Scholar] [CrossRef]
  83. Lee, N.; Chan, K.C.A.; Hui, D.S.; Ng, E.K.O.; Wu, A.; Chiu, R.W.K.; Wong, V.W.S.; Chan, P.K.S.; Wong, K.T.; Wong, E.; et al. Effects of early corticosteroid treatment on plasma SARS-associated Coronavirus RNA concentrations in adult patients. J. Clin. Virol. 2004, 31, 304–309. [Google Scholar] [CrossRef]
  84. Lee, N.; Chan, P.K.S.; Hui, D.; Rainer, T.; Wong, E.; Choi, K.; Lui, C.Y.G.; Wong, B.C.K.; Wong, R.Y.K.; Lam, W.; et al. Viral Loads and Duration of Viral Shedding in Adult Patients Hospitalized with Influenza. J. Infect. Dis. 2009, 200, 492–500. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Rodrigo, C.; Leonardi-Bee, J.; Nguyen-Van-Tam, J.; Lim, W.S. Corticosteroids as adjunctive therapy in the treatment of influenza. Cochrane Database Syst. Rev. 2016, 3, CD010406. [Google Scholar] [CrossRef] [PubMed]
  86. Sinha, S.; Rosin, N.L.; Arora, R.; Labit, E.; Jaffer, A.; Cao, L.; Farias, R.; Nguyen, A.P.; de Almeida, L.G.N.; Dufour, A.; et al. Dexamethasone modulates immature neutrophils and interferon programming in severe COVID-19. Nat. Med. 2022, 28, 201–211. [Google Scholar] [CrossRef] [PubMed]
  87. Perretti, M.; D’Acquisto, F. Annexin A1 and glucocorticoids as effectors of the resolution of inflammation. Nat. Rev. Immunol. 2009, 9, 62–70. [Google Scholar] [CrossRef]
  88. Goubran, H.; Seghatchian, J.; Sabry, W.; Ragab, G.; Burnouf, T. Platelet and extracellular vesicles in COVID-19 infection and its vaccines. Transfus. Apher. Sci. 2022, 61, 103459. [Google Scholar] [CrossRef]
  89. Barrett, T.J.; Bilaloglu, S.; Cornwell, M.; Burgess, H.M.; Virginio, V.W.; Drenkova, K.; Ibrahim, H.; Yuriditsky, E.; Aphinyanaphongs, Y.; Lifshitz, M.; et al. Platelets contribute to disease severity in COVID-19. J. Thromb. Haemost. 2021, 19, 3139–3153. [Google Scholar] [CrossRef]
  90. Zaid, Y.; Puhm, F.; Allaeys, I.; Naya, A.; Oudghiri, M.; Khalki, L.; Limami, Y.; Zaid, N.; Sadki, K.; Haj, R.B.E.; et al. Platelets can associate with SARS-Cov-2 RNA and are hyperactivated in COVID-19. Circ. Res. 2020, 127, 1404–1418. [Google Scholar] [CrossRef]
  91. Feldman, C.; Anderson, R. Brief review: Cardiac complications and platelet activation in COVID-19 infection. Afr. J. Thorac. Crit. Care Med. 2020, 26, 90–94. [Google Scholar] [CrossRef]
  92. Zhang, S.; Liu, Y.; Wang, X.; Yang, L.; Li, H.; Wang, Y.; Liu, M.; Zhao, X.; Xie, Y.; Yang, Y.; et al. SARS-CoV-2 binds platelet ACE2 to enhance thrombosis in COVID-19. J. Hematol. Oncol. 2020, 13, 120. [Google Scholar] [CrossRef]
  93. Koupenova, M.; Vitseva, O.; Mackay, C.R.; Beaulieu, L.M.; Benjamin, E.J.; Mick, E.; Kurt-Jones, E.A.; Ravid, K.; Freedman, J.E. Platelet-TLR7 mediates host survival and platelet count during viral infection in the absence of platelet-dependent thrombosis. Blood 2014, 124, 791–802. [Google Scholar] [CrossRef] [Green Version]
  94. Koupenova, M.; Corkrey, H.A.; Vitseva, O.; Manni, G.; Pang, C.J.; Clancy, L.; Yao, C.; Rade, J.; Levy, D.; Wang, J.P.; et al. The role of platelets in mediating a response to human influenza infection. Nat. Commun. 2019, 10, 1780. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Said, E.A.; Tremblay, N.; Al-Balushi, M.S.; Al-Jabri, A.A.; Lamarre, D. Viruses Seen by Our Cells: The Role of Viral RNA Sensors. J. Immunol. Res. 2018, 2018, 9480497. [Google Scholar] [CrossRef] [PubMed]
  96. Ferreira-Gomes, M.; Kruglov, A.; Durek, P.; Heinrich, F.; Tizian, C.; Heinz, G.A.; Pascual-Reguant, A.; Du, W.; Mothes, R.; Fan, C.; et al. SARS-CoV-2 in severe COVID-19 induces a TGF-β-dominated chronic immune response that does not target itself. Nat. Commun. 2021, 12, 1961. [Google Scholar] [CrossRef] [PubMed]
  97. Jeannet, R.; Daix, T.; Formento, R.; Feuillard, J.; Fançois, B. Severe COVID-19 is associated with deep and sustained multifaceted cellular immunosuppression. Intensive Care Med. 2020, 46, 1769–1771. [Google Scholar] [CrossRef]
  98. Kim, S.-W.; Lee, J.-K. Role of HMGB1 in the Interplay between NETosis and Thrombosis in Ischemic Stroke: A Review. Cells 2020, 9, 1794. [Google Scholar] [CrossRef]
  99. Manne, B.K.; Denorme, F.; Middleton, E.A.; Portier, I.; Rowley, J.W.; Stubben, C.; Petrey, A.C.; Tolley, N.D.; Guo, L.; Cody, M.; et al. Platelet gene expression and function in patients with COVID-19. Blood 2020, 136, 1317–1329. [Google Scholar] [CrossRef]
  100. Zhang, F.; Wang, H.; Wang, X.; Jiang, G.; Liu, H.; Zhang, G.; Wang, H.; Fang, R.; Bu, X.; Cai, S.; et al. TGF-β induces M2-like macrophage polarization via SNAIL-mediated suppression of a pro-inflammatory phenotype. Oncotarget 2016, 7, 52294–52306. [Google Scholar] [CrossRef] [Green Version]
  101. He, J.; Cai, S.; Feng, H.; Cai, B.; Lin, L.; Mai, Y.; Fan, Y.; Zhu, A.; Huang, H.; Shi, J.; et al. Single-cell analysis reveals bronchoalveolar epithelial dysfunction in COVID-19 patients. Protein Cell 2020, 11, 680–687. [Google Scholar] [CrossRef]
  102. Hottz, E.D.; Azevedo-Quintanilha, I.G.; Palhinha, L.; Teixeira, L.; Barreto, E.A.; Pão, C.R.R.; Righy, C.; Franco, S.; Souza, T.M.L.; Kurtz, P.; et al. Platelet activation and platelet-monocyte aggregate formation trigger tissue factor expression in patients with severe COVID-19. Blood 2020, 136, 1330–1341. [Google Scholar] [CrossRef]
  103. Lee, C.-R.; Lee, W.; Cho, S.K.; Park, S.-G. Characterization of Multiple Cytokine Combinations and TGF-β on Differentiation and Functions of Myeloid-Derived Suppressor Cells. Int. J. Mol. Sci. 2018, 19, 869. [Google Scholar] [CrossRef] [Green Version]
  104. Schulte-Schrepping, J.; Reusch, N.; Paclik, D.; Baßler, K.; Schlickeiser, S.; Zhang, B.; Krämer, B.; Krammer, T.; Brumhard, S.; Bonaguro, L.; et al. Severe COVID-19 Is Marked by a Dysregulated Myeloid Cell Compartment. Cell 2020, 182, 1419–1440.e23. [Google Scholar] [CrossRef] [PubMed]
  105. Fava, R.; Casey, T.; Wilcox, J.; Pelton, R.; Moses, H.; Nanney, L. Synthesis of transforming growth factor-beta 1 by megakaryocytes and its localization to megakaryocyte and platelet alpha-granules. Blood 1990, 76, 1946–1955. [Google Scholar] [CrossRef] [PubMed]
  106. Avci, A.B.; Feist, E.; Burmester, G.R. Targeting IL-6 or IL-6 Receptor in Rheumatoid Arthritis: What’s the Difference? BioDrugs 2018, 32, 531–546. [Google Scholar] [CrossRef] [PubMed]
  107. Choy, E.H.; De Benedetti, F.; Takeuchi, T.; Hashizume, M.; John, M.R.; Kishimoto, T. Translating IL-6 biology into effective treatments. Nat. Rev. Rheumatol. 2020, 16, 335–345. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Sebba, A. Tocilizumab: The first interleukin-6-receptor inhibitor. Am. J. Health Syst. Pharm. 2008, 65, 1413–1418. [Google Scholar] [CrossRef]
  109. Le, R.Q.; Li, L.; Yuan, W.; Shord, S.S.; Nie, L.; Habtemariam, B.A.; Przepiorka, D.; Farrell, A.T.; Pazdur, R. FDA Approval Summary: Tocilizumab for Treatment of Chimeric Antigen Receptor T Cell-Induced Severe or Life-Threatening Cytokine Release Syndrome. Oncologist 2018, 23, 943–947. [Google Scholar] [CrossRef] [Green Version]
  110. Tanaka, T.; Ogata, A.; Narazaki, M. Tocilizumab for the treatment of rheumatoid arthritis. Expert Rev. Clin. Immunol. 2010, 6, 843–854. [Google Scholar] [CrossRef]
  111. Schirmer, M.; Muratore, F.; Salvarani, C. Tocilizumab for the treatment of giant cell arteritis. Expert Rev. Clin. Immunol. 2018, 14, 339–349. [Google Scholar] [CrossRef] [Green Version]
  112. Brunner, H.I.; Ruperto, N.; Zuber, Z.; Keane, C.; Harari, O.; Kenwright, A.; Lu, P.; Cuttica, R.; Keltsev, V.; Xavier, R.M.; et al. Efficacy and safety of tocilizumab in patients with polyarticular-course juvenile idiopathic arthritis: Results from a phase 3, randomised, double-blind withdrawal trial. Ann. Rheum. Dis. 2015, 74, 1110–1117. [Google Scholar] [CrossRef] [Green Version]
  113. Talwar, D.; Kumar, S.; Acharya, S.; Raisinghani, N.; Madaan, S.; Hulkoti, V.; Akhilesh, A.; Khanna, S.; Shah, D.; Nimkar, S. Interleukin 6 and Its Correlation with COVID-19 in Terms of Outcomes in an Intensive Care Unit of a Rural Hospital:A Cross-sectional Study. Indian J. Crit Care Med. 2022, 26, 39–42. [Google Scholar] [CrossRef]
  114. Malekzadeh, R.; Abedini, A.; Mohsenpour, B.; Sharifipour, E.; Ghasemian, R.; Javad-Mousavi, S.A.; Khodashahi, R.; Darban, M.; Kalantari, S.; Abdollahi, N.; et al. Subcutaneous tocilizumab in adults with severe and critical COVID-19: A prospective open-label uncontrolled multicenter trial. Int. Immunopharmacol. 2020, 89, 107102. [Google Scholar] [CrossRef] [PubMed]
  115. Alam, W.; Bizri, A.R. Efficacy of tocilizumab in COVID-19: A review of the current evidence. Sci. Prog. 2021, 104, 368504211030372. [Google Scholar] [CrossRef] [PubMed]
  116. Abani, O.; Abbas, A.; Abbas, F.; Abbas, M.; Abbasi, S.; Abbass, H.; Abbott, A.; Abdallah, N.; Abdelaziz, A.; Abdelfattah, M.; et al. Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): A randomised, controlled, open-label, platform trial. Lancet 2021, 397, 1637–1645. [Google Scholar]
  117. Salama, C.; Han, J.; Yau, L.; Reiss, W.G.; Kramer, B.; Neidhart, J.D.; Criner, G.J.; Kaplan-Lewis, E.; Baden, R.; Pandit, L.; et al. Tocilizumab in Patients Hospitalized with COVID-19 Pneumonia. N. Engl. J. Med. 2021, 384, 20–30. [Google Scholar] [CrossRef]
  118. Roche. Roche Provides Update on the Phase III REMDACTA Trial of Actemra/RoActemra Plus Veklury in Patients with Severe COVID-19 Pneumonia 2021. Available online: https://www.roche.com/media/releases/med-cor-2021-03-11.htm (accessed on 22 February 2022).
  119. Rosas, I.O.; Bräu, N.; Waters, M.; Go, R.C.; Hunter, B.D.; Bhagani, S.; Skiest, D.; Aziz, M.S.; Cooper, N.; Douglas, I.S.; et al. Tocilizumab in Hospitalized Patients with Severe COVID-19 Pneumonia. N. Engl. J. Med. 2021, 384, 1503–1516. [Google Scholar] [CrossRef]
  120. Kimmig, L.M.; Wu, D.; Gold, M.; Pettit, N.N.; Pitrak, D.; Mueller, J.; Husain, A.N.; Mutlu, E.A.; Mutlu, G.M. IL-6 Inhibition in Critically Ill COVID-19 Patients Is Associated With Increased Secondary Infections. Front. Med. 2020, 7, 583897. [Google Scholar] [CrossRef]
  121. Warner, A.; Abbas, M.; Avner, B.S.; Flynn, T.E.; Walroth, T.A.; Jagger, B.W. 342. Secondary Infections following Tocilizumab for Treatment of COVID-19 Pneumonia. Open Forum Infect. Dis. 2021, 8 (Suppl. 1), S275. [Google Scholar] [CrossRef]
  122. Sandhu, G.; Piraino, S.T.; Piticaru, J. Secondary Infection Risk in Patients With Severe COVID-19 Pneumonia Treated With Tocilizumab. Am. J. Ther. 2022, 29, e275–e278. [Google Scholar] [CrossRef]
  123. Guillamet, M.C.; Kulkarni, H.S.; Montes, K.; Samant, M.; Shaikh, P.A.; Betthauser, K.; Mudd, P.A.; Reynolds, D.; O’Halloran, J.; Lyons, P.; et al. Interleukin-6 Trajectory and Secondary Infections in Mechanically Ventilated Patients With Coronavirus Disease 2019 Acute Respiratory Distress Syndrome Treated With Interleukin-6 Receptor Blocker. Crit. Care Explor. 2021, 3, e0343. [Google Scholar] [CrossRef]
  124. Tleyjeh, I.M.; Kashour, Z.; Riaz, M.; Hassett, L.; Veiga, V.C.; Kashour, T. Efficacy and safety of tocilizumab in COVID-19 patients: A living systematic review and meta-analysis, first update. Clin. Microbiol. Infect. 2021, 27, 1076–1082. [Google Scholar] [CrossRef]
  125. Aljuhani, O.; Al Sulaiman, K.; Alshabasy, A.; Eljaaly, K.; Al Shaya, A.I.; Noureldeen, H.; Aboudeif, M.; Al Dosari, B.; Alkhalaf, A.; Korayem, G.B.; et al. Association between tocilizumab and emerging multidrug-resistant organisms in critically ill patients with COVID-19: A multicenter, retrospective cohort study. BMC Infect. Dis. 2021, 21, 1127. [Google Scholar] [CrossRef]
  126. Stone, J.H.; Frigault, M.J.; Serling-Boyd, N.J.; Fernandes, A.D.; Harvey, L.; Foulkes, A.S.; Horick, N.K.; Healy, B.C.; Shah, R.; Bensaci, A.M.; et al. Efficacy of Tocilizumab in Patients Hospitalized with COVID-19. N. Engl. J. Med. 2020, 383, 2333–2344. [Google Scholar] [CrossRef] [PubMed]
  127. Chen, C.-X.; Hu, F.; Wei, J.; Yuan, L.-T.; Wen, T.-M.; Gale, R.P.; Liang, Y. Systematic review and meta-analysis of tocilizumab in persons with coronavirus disease-2019 (COVID-19). Leukemia 2021, 35, 1661–1670. [Google Scholar] [CrossRef] [PubMed]
  128. Ahmadipour, M.; Dehghan, M.; Ahmadinejad, M.; Jabarpour, M.; Shahrbabaki, P.M.; Rigi, Z.E. Barriers to hand hygiene compliance in intensive care units during the COVID-19 pandemic: A qualitative study. Front. Public Health 2022, 10, 968231. [Google Scholar] [CrossRef] [PubMed]
  129. Ho, S.M.Y.; Kwong-Lo, R.S.Y.; Mak, C.W.Y.; Wong, J.S. Fear of Severe Acute Respiratory Syndrome (SARS) Among Health Care Workers. J. Consult. Clin. Psychol. 2005, 73, 344–349. [Google Scholar] [CrossRef]
  130. Cawcutt, K.A.; Starlin, R.; Rupp, M.E. Fighting fear in healthcare workers during the COVID-19 pandemic. Infect. Control Hosp. Epidemiol. 2020, 41, 1192–1193. [Google Scholar] [CrossRef]
  131. Leggiadro, R.J. Bacterial coinfections in lung tissue specimens from fatal cases of 2009 pandemic influenza A (H1N1)—United States, May–August 2009. Pediatr. Infect. Dis. J. 2010, 29, 22. [Google Scholar] [CrossRef]
  132. Louie, J.K. Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA 2009, 302, 1896–1902. [Google Scholar] [CrossRef] [Green Version]
  133. The ANZIC Influenza Investigators. Critical care services and the H1N1 (2009) influenza epidemic in Australia and New Zealand in 2010: The impact of the second winter epidemic. Crit. Care 2011, 15, R143. [Google Scholar] [CrossRef] [Green Version]
  134. Li, R.; Pei, S.; Chen, B.; Song, Y.; Zhang, T.; Yang, W.; Shaman, J. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020, 368, 489–493. [Google Scholar] [CrossRef] [Green Version]
  135. Musuuza, J.S.; Watson, L.; Parmasad, V.; Putman-Buehler, N.; Christensen, L.; Safdar, N. Prevalence and outcomes of co-infection and superinfection with SARS-CoV-2 and other pathogens: A systematic review and meta-analysis. PLoS ONE 2021, 16, e0251170. [Google Scholar] [CrossRef] [PubMed]
  136. Garcia-Vidal, C.; Sanjuan, G.; Moreno-García, E.; Puerta-Alcalde, P.; Garcia-Pouton, N.; Chumbita, M.; Fernandez-Pittol, M.; Pitart, C.; Inciarte, A.; Bodro, M.; et al. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: A retrospective cohort study. Clin. Microbiol. Infect. 2020, 27, 83–88. [Google Scholar] [CrossRef] [PubMed]
  137. Wang, L.; He, W.; Yu, X.; Hu, D.; Bao, M.; Liu, H.; Zhou, J.; Jiang, H. Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up. J. Infect. 2020, 80, 639–645. [Google Scholar] [CrossRef] [PubMed]
  138. Hughes, S.; Troise, O.; Donaldson, H.; Mughal, N.; Moore, L.S.P. Bacterial and fungal coinfection among hospitalized patients with COVID-19: A retrospective cohort study in a UK secondary-care setting. Clin. Microbiol. Infect. 2020, 26, 1395–1399. [Google Scholar] [CrossRef] [PubMed]
  139. Falcone, M.; Tiseo, G.; Giordano, C.; Leonildi, A.; Menichini, M.; Vecchione, A.; Pistello, M.; Guarracino, F.; Ghiadoni, L.; Forfori, F.; et al. Predictors of hospital-acquired bacterial and fungal superinfections in COVID-19: A prospective observational study. J. Antimicrob. Chemother. 2021, 76, 1078–1084. [Google Scholar] [CrossRef]
  140. Vanderbeke, L.; Spriet, I.; Breynaert, C.; Rijnders, B.J.A.; Verweij, P.E.; Wauters, J. Invasive pulmonary aspergillosis complicating severe influenza: Epidemiology, diagnosis and treatment. Curr. Opin. Infect. Dis. 2018, 31, 471–480. [Google Scholar] [CrossRef]
  141. Schauwvlieghe, A.F.A.D.; Rijnders, B.J.A.; Philips, N.; Verwijs, R.; Vanderbeke, L.; Van Tienen, C.; Lagrou, K.; Verweij, P.E.; Van De Veerdonk, F.L.; Gommers, D.; et al. Invasive aspergillosis in patients admitted to the intensive care unit with severe influenza: A retrospective cohort study. Lancet Respir. Med. 2018, 6, 782–792. [Google Scholar] [CrossRef]
  142. Buehler, P.K.; Zinkernagel, A.S.; Hofmaenner, D.A.; Wendel Garcia, P.D.; Acevedo, C.T.; Gómez-Mejia, A.; Mairpady Shambat, S.; Andreoni, F.; Maibach, M.A.; Bartussek, J.; et al. Bacterial pulmonary superinfections are associated with longer duration of ventilation in critically ill COVID-19 patients. Cell Rep. Med. 2021, 2, 100229. [Google Scholar] [CrossRef]
  143. Grasselli, G.; Cattaneo, E.; Florio, G. Secondary infections in critically ill patients with COVID-19. Crit. Care 2021, 25, 317. [Google Scholar] [CrossRef]
  144. Stolz, D.; Christ-Crain, M.; Bingisser, R.; Leuppi, J.; Miedinger, D.; Müller, C.; Huber, P.; Müller, B.; Tamm, M. Antibiotic Treatment of Exacerbations of COPD: A Randomized, Controlled Trial Comparing Procalcitonin-Guidance With Standard Therapy. Chest 2007, 131, 9–19. [Google Scholar] [CrossRef] [Green Version]
  145. Meynaar, I.A.; Droog, W.; Batstra, M.; Vreede, R.; Herbrink, P. In Critically Ill Patients, Serum Procalcitonin Is More Useful in Differentiating between Sepsis and SIRS than CRP, Il-6, or LBP. Crit. Care Res. Pract. 2011, 2011, 594645. [Google Scholar] [CrossRef] [PubMed]
  146. Briel, M.; Schuetz, P.; Mueller, B.; Young, J.; Schild, U.; Nusbaumer, C.; Périat, P.; Bucher, H.C.; Christ-Crain, M. Procalcitonin-Guided Antibiotic Use vs. a Standard Approach for Acute Respiratory Tract Infections in Primary Care. Arch. Intern. Med. 2008, 168, 2000–2007; Discussion 2007–2008. [Google Scholar] [CrossRef] [PubMed]
  147. Becker, K.L.; Snider, R.; Nylen, E.S. Procalcitonin in sepsis and systemic inflammation: A harmful biomarker and a therapeutic target. Br. J. Pharmacol. 2010, 159, 253–264. [Google Scholar] [CrossRef] [PubMed]
  148. Linscheid, P.; Seboek, D.; Nylen, E.S.; Langer, I.; Schlatter, M.; Becker, K.L.; Keller, U.; Muller, B. In Vitro and in Vivo Calcitonin I Gene Expression in Parenchymal Cells: A Novel Product of Human Adipose Tissue. Endocrinology 2003, 144, 5578–5584. [Google Scholar] [CrossRef]
  149. Nehring, S.M.; Goyal, A.; Patel, B.C. C Reactive Protein; StatPearls Publishing: Treasure Island, FL, USA, 2022. [Google Scholar]
  150. Meisner, M.; Tschaikowsky, K.; Palmaers, T.; Schmidt, J. Comparison of procalcitonin (PCT) and C-reactive protein (CRP) plasma concentrations at different SOFA scores during the course of sepsis and MODS. Crit. Care 1999, 3, 45–50. [Google Scholar] [CrossRef] [Green Version]
  151. Luzzani, A.; Polati, E.; Dorizzi, R.; Rungatscher, A.; Pavan, R.; Merlini, A. Comparison of procalcitonin and C-reactive protein as markers of sepsis. Crit. Care Med. 2003, 31, 1737–1741. [Google Scholar] [CrossRef]
  152. Ahn, S.; Kim, W.Y.; Kim, S.-H.; Hong, S.; Lim, C.-M.; Koh, Y.; Lim, K.S. Role of procalcitonin and C-reactive protein in differentiation of mixed bacterial infection from 2009 H1N1 viral pneumonia. Influ. Other Respir. Viruses 2011, 5, 398–403. [Google Scholar] [CrossRef] [Green Version]
  153. Lippi, G.; Plebani, M. Laboratory abnormalities in patients with COVID-2019 infection. Clin. Chem. Lab. Med. CCLM 2020, 58, 1131–1134. [Google Scholar] [CrossRef] [Green Version]
  154. Tong-Minh, K.; van der Does, Y.; Engelen, S.; de Jong, E.; Ramakers, C.; Gommers, D.; van Gorp, E.; Endeman, H. High procalcitonin levels associated with increased intensive care unit admission and mortality in patients with a COVID-19 infection in the emergency department. BMC Infect. Dis. 2022, 22, 165. [Google Scholar] [CrossRef]
  155. Van Berkel, M.; Kox, M.; Frenzel, T.; Pickkers, P.; Schouten, J.; Waanders, D.; Bruse, N.; Kooistra, E.; Touw, H.; Hemelaar, P.; et al. Biomarkers for antimicrobial stewardship: A reappraisal in COVID-19 times? Crit. Care 2020, 24, 600. [Google Scholar] [CrossRef]
  156. Kooistra, E.J.; van Berkel, M.; van Kempen, N.F.; van Latum, C.R.; Bruse, N.; Frenzel, T.; van den Berg, M.J.W.; Schouten, J.A.; Kox, M.; Pickkers, P. Dexamethasone and tocilizumab treatment considerably reduces the value of C-reactive protein and procalcitonin to detect secondary bacterial infections in COVID-19 patients. Crit. Care 2021, 25, 281. [Google Scholar] [CrossRef] [PubMed]
  157. Buetti, N.; Mazzuchelli, T.; Priore, E.L.; Balmelli, C.; Llamas, M.; Pallanza, M.; Elzi, L.; Consonni, V.; Trimboli, P.; Forni-Ogna, V.; et al. Early administered antibiotics do not impact mortality in critically ill patients with COVID-19. J. Infect. 2020, 81, e148–e149. [Google Scholar] [CrossRef] [PubMed]
  158. Cao, J.; Tu, W.J.; Cheng, W.; Yu, L.; Liu, Y.K.; Hu, X.; Liu, Q. Clinical features and short-term outcomes of 102 patients with corona virus disease 2019 in Wuhan, China. Clin. Infect. Dis. 2020, 71, 748–755.ciaa243. [Google Scholar] [CrossRef] [PubMed]
  159. Chen, T.; Wu, D.; Chen, H.; Yan, W.; Yang, D.; Chen, G.; Ma, K.; Xu, D.; Yu, H.; Wang, H.; et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: Retrospective study. BMJ 2020, 368, m1091. [Google Scholar] [CrossRef] [Green Version]
  160. Du, Y.; Tu, L.; Zhu, P.; Mu, M.; Wang, R.; Yang, P.; Wang, X.; Hu, C.; Ping, R.; Hu, P.; et al. Clinical Features of 85 Fatal Cases of COVID-19 from Wuhan. A Retrospective Observational Study. Am. J. Respir. Crit. Care Med. 2020, 201, 1372–1379. [Google Scholar] [CrossRef] [Green Version]
  161. Prattes, J.; Valentin, T.; Hoenigl, M.; Talakic, E.; Reisinger, A.C.; Eller, P. Invasive pulmonary aspergillosis complicating COVID-19 in the ICU—A case report. Med. Mycol. Case Rep. 2020, 31, 2–5. [Google Scholar] [CrossRef]
  162. Wang, Z.; Yang, B.; Li, Q.; Wen, L.; Zhang, R. Clinical Features of 69 Cases With Coronavirus Disease 2019 in Wuhan, China. Clin. Infect. Dis. 2020, 71, 769–777. [Google Scholar] [CrossRef] [Green Version]
  163. Subramaniam, G.; Girish, M. Antibiotic Resistance—A Cause for Reemergence of Infections. Indian J. Pediatr. 2020, 87, 937–944. [Google Scholar] [CrossRef]
  164. Tiri, B.; Sensi, E.; Marsiliani, V.; Cantarini, M.; Priante, G.; Vernelli, C.; Martella, L.A.; Costantini, M.; Mariottini, A.; Andreani, P.; et al. Antimicrobial Stewardship Program, COVID-19, and Infection Control: Spread of Carbapenem-Resistant Klebsiella Pneumoniae Colonization in ICU COVID-19 Patients. What Did Not Work? J. Clin. Med. 2020, 9, 2744. [Google Scholar] [CrossRef]
  165. Dueñas-Castell, C.; Polanco-Guerra, C.J.; Martinez-Ávila, M.C.; Hurtado, A.J.A.; Yanez, T.R.; Gutierrez-Ariza, J.C.; Rico-Fontalvo, J. When to Use Antibiotics in COVID-19: A Proposal Based on Questions. Cureus 2022, 14, e27398. [Google Scholar] [CrossRef]
  166. Klugman, K.P.; Chien, Y.-W.; Madhi, S.A. Pneumococcal pneumonia and influenza: A deadly combination. Vaccine 2009, 27, C9–C14. [Google Scholar] [CrossRef] [PubMed]
  167. De Bruyn, A.; Verellen, S.; Bruckers, L.; Geebelen, L.; Callebaut, I.; De Pauw, I.; Stessel, B.; Dubois, J. Secondary infection in COVID-19 critically ill patients: A retrospective single-center evaluation. BMC Infect. Dis. 2022, 22, 207. [Google Scholar] [CrossRef] [PubMed]
  168. Vijay, S.; Bansal, N.; Rao, B.K.; Veeraraghavan, B.; Rodrigues, C.; Wattal, C.; Goyal, J.P.; Tadepalli, K.; Mathur, P.; Venkateswaran, R.; et al. Secondary Infections in Hospitalized COVID-19 Patients: Indian Experience. Infect. Drug Resist. 2021, 14, 1893–1903. [Google Scholar] [CrossRef] [PubMed]
  169. Seymour, C.W.; Gesten, F.; Prescott, H.C.; Friedrich, M.E.; Iwashyna, T.J.; Phillips, G.S.; Lemeshow, S.; Osborn, T.; Terry, K.M.; Levy, M.M. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. N. Engl. J. Med. 2017, 376, 2235–2244. [Google Scholar] [CrossRef]
  170. Cheng, M.P.; Stenstrom, R.; Paquette, K.; Stabler, S.N.; Akhter, M.; Davidson, A.C.; Gavric, M.; Lawandi, A.; Jinah, R.; Saeed, Z.; et al. Blood culture results before and after antimicrobial administration in patients with severe manifestations of sepsis: A diagnostic study. Ann. Intern. Med. 2019, 171, 547–554. [Google Scholar] [CrossRef]
  171. Holmes, E.; Wilson, I.D.; Nicholson, J.K. Metabolic Phenotyping in Health and Disease. Cell 2008, 134, 714–717. [Google Scholar] [CrossRef] [Green Version]
  172. Cambiaghi, A.; Pinto, B.B.; Brunelli, L.; Falcetta, F.; Aletti, F.; Bendjelid, K.; Pastorelli, R.; Ferrario, M. Characterization of a metabolomic profile associated with responsiveness to therapy in the acute phase of septic shock. Sci. Rep. 2017, 7, 9748. [Google Scholar] [CrossRef] [Green Version]
  173. Schmerler, D.; Neugebauer, S.; Ludewig, K.; Bremer-Streck, S.; Brunkhorst, F.M.; Kiehntopf, M. Targeted metabolomics for discrimination of systemic inflammatory disorders in critically ill patients. J. Lipid Res. 2012, 53, 1369–1375. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  174. Jaurila, H.; Koivukangas, V.; Koskela, M.; Gäddnäs, F.; Myllymaa, S.; Kullaa, A.; Salo, T.; Ala-Kokko, T.I. 1H NMR Based Metabolomics in Human Sepsis and Healthy Serum. Metabolites 2020, 10, 70. [Google Scholar] [CrossRef] [Green Version]
  175. Slupsky, C.M.; Cheypesh, A.; Chao, D.V.; Fu, H.; Rankin, K.N.; Marrie, T.J.; Lacy, P. Streptococcus pneumoniae and Staphylococcus aureus Pneumonia Induce Distinct Metabolic Responses. J. Proteome Res. 2009, 8, 3029–3036. [Google Scholar] [CrossRef]
  176. Ping, F.; Li, Y.; Cao, Y.; Shang, J.; Zhang, Z.; Yuan, Z.; Wang, W.; Guo, Y. Metabolomics Analysis of the Development of Sepsis and Potential Biomarkers of Sepsis-Induced Acute Kidney Injury. Oxidative Med. Cell. Longev. 2021, 2021, 6628847. [Google Scholar] [CrossRef] [PubMed]
  177. Miao, Q.; Ma, Y.; Wang, Q.; Pan, J.; Zhang, Y.; Jin, W.; Yao, Y.; Su, Y.; Huang, Y.; Wang, M.; et al. Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. Clin. Infect. Dis. 2018, 67, S231–S240. [Google Scholar] [CrossRef] [PubMed]
  178. Mostafa, H.H.; Fissel, J.A.; Fanelli, B.; Bergman, Y.; Gniazdowski, V.; Dadlani, M.; Carroll, K.C.; Colwell, R.R.; Simner, P.J. Metagenomic Next-Generation Sequencing of Nasopharyngeal Specimens Collected from Confirmed and Suspect COVID-19 Patients. mBio 2020, 11, e01969-20. [Google Scholar] [CrossRef] [PubMed]
Table 1. Demographics, clinical characteristics, use of immunomodulatory drugs, and outcomes in 155 COVID-19 ARDS patients with or without superinfection.
Table 1. Demographics, clinical characteristics, use of immunomodulatory drugs, and outcomes in 155 COVID-19 ARDS patients with or without superinfection.
Full Cohort (N = 155) Superifection (N = 67)No Superinfection (N = 88)p-Value
Male gender11550650.914
Age (years)6262610.257
BMI27.827.728.10.987
Hypertension6932370.517
Diabetes3815230.519
Chronic lung disease3617190.581
Chronic kidney disease12750.271
Autoimmune disease171250.016
Prior immunosuppressive therapy17980.392
Dexamethasone given724428<0.0001
Duran (days)1111120.705
Anakinra2911180.589
CRP admission highest2802732800.261
PaO2/PiO2 ratio (worst)1412150.07
Hemodialysis in ICU278190.117
Hospital LOS (days)273223<0.0001
ICU LOS192617<0.0001
Time on ventilator162113<0.0001
Values are N (percentage) and medians (25th–75th centile).
Table 2. Etiology of superinfection in hospitalized and ventilated patients [139].
Table 2. Etiology of superinfection in hospitalized and ventilated patients [139].
Infection Type and Organismn
Bacteraemia/fungaemia44 (40.3%)
CoNS4
S. aureus4
Enterococcus spp.2
K. pneumoniae11
E. coli2
Enterobacter aerogenes2
P. aeruginosa4
S. maltophilia1
A. baumannii1
C. striatum2
Candida parapsilosis/orthopsilosis5
Mixed6
Ventilator acquired pneumonia26 (23.8)
S. aureus1
K. pneumoniae8
E. coli2
C. freundii2
P. aeruginosa1
A. baumannii2
A. fumigatus1
Mixed9
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Boulos, M.; Bassal, T.; Layyous, A.; Basheer, M.; Assy, N. Inflammation in COVID-19: A Risk for Superinfections. COVID 2022, 2, 1609-1624. https://doi.org/10.3390/covid2110116

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Boulos M, Bassal T, Layyous A, Basheer M, Assy N. Inflammation in COVID-19: A Risk for Superinfections. COVID. 2022; 2(11):1609-1624. https://doi.org/10.3390/covid2110116

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Boulos, Mariana, Tamara Bassal, Asad Layyous, Maamoun Basheer, and Nimer Assy. 2022. "Inflammation in COVID-19: A Risk for Superinfections" COVID 2, no. 11: 1609-1624. https://doi.org/10.3390/covid2110116

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