4 Discussion
Owing to the important implication of SARS-CoV-2 co-infection for
COVID-19 management, we found a large proportion of co-infection with
other respiratory pathogens among COVID-19 patients in Qingdao, China.
Meanwhile, we determined independent factors associated with
co-infection by univariate and multivariate analysis. Besides, negative
conversion of SARS-CoV-2 RNA was considered as associated variable of
prognosis to evaluate the impact of co-infection on COVID-19 patients.
Our findings suggested the distribution of co-infection in COVID-19, and
provided the evidence that co-infection of only bacteria, only viruses
and mixed of them could variously affect the COVID-19. A reported rate
of COVID-19 co-infection with 39 pathogen detection was 94.2% (virus
31.5%, bacteria 91.8%) from Zhu (Zhu et al., 2020), as well as other
reported rates of co-infected pathogens from 13.5% to 20.7% (Kim et
al., 2020; Wang et al.). In our study, the co-infection rate of COVID-19
patients was 76.4% (virus 23.6%, bacteria 63.6%), which stayed
similar level in comparison with other studies. In order to further
verify whether the high rate of COVID-19 co-infection related to
SARS-CoV-2 infection, we collected pneumonia cases in fever clinics
considered as suspected cases of COVID-19, including 178 febrile
outpatients with pneumonia who admitted to the local hospitals in
Qingdao at the same time. As shown in Figure 3 , the common
pathogens in COVID-19 patients and pneumonia cases were almost the same,
including SP, HI, MC and IFV-B, IFV-A. However, there was a significant
difference for rates of co-infection between COVID-19 patients and
pneumonia cases (P<0.05), and the rate of co-infection in
COVID-19 patients was four times of the co-infection rate of pneumonia
cases (19.1%).
To our best knowledge, this has been the first study focused on
independent factors associated with SARS-CoV-2 co-infection. Based on
co-infection was no associated with disease severity, we further
analyzed in terms of separately bacteria and viruses to determine
characteristics of co-infection in COVID-19 patients. Among all
co-infection patients, 83.3% cases were detected for bacterial
co-infection, which were more than twice for viral co-infection
(31.0%). For co-infection of bacteria, the most common bacterial
pathogens were SP and HI. Results from multivariate Logistic model
revealed that over 70% of neutrophils proportion was an independently
factor of co-infection of bacteria, which positively associated with
bacterial co-infection (OR: 4.563; 95%CI: 1.116-18.648). Moreover, for
co-infection of viruses, the most common viral pathogen is INF-B. After
multivariate Logistic regression analysis, fever and chest tightness
were independently factors of co-infection of viruses. Fever (OR: 4.506;
95%CI: 1.044-19.441) was positively associated with co-infection of
viruses, whereas chest tightness (OR: 0.106; 95%CI: 0.015-0.743) was
negatively associated. These findings suggest the need to conduct
comprehensive microbiologic surveys and clinical evaluation for other
respiratory pathogens in COVID-19 patients, and clinicians should pay
more attention on the co-infection for confirmed COVID-19 cases, which
have great implications for COVID-19 treatment. Additionally, these
independent factors may help clinicians to identify keys for
co-infection prevention in COVID-19 patients.
At present, there has been limit study reporting the impact of
co-infection on COVID-19 patients, which mostly focused on the
descriptive characteristics of co-infection. However, this study has
firstly provided evidence that co-infection could impact on COVID-19,
which presents the association with negative conversion of SARS-CoV-2
RNA. Results suggested that co-infection was associated with a promoted
shedding of SARS-CoV-2 in COVID-19 patients. Compared with COVID-19
patients without co-infection, patients with co-infection could promote
the duration of negative conversion of SARS-CoV-2 RNA, and the effect of
promotion varies from different types of co-infection pathogens. Results
from multivariate Cox regression revealed that among all types of
co-infection, The strongest promotion for negative conversion was
detected with co-infection of only viruses (HR: 4.039; 95%CI:
1.238-13.177), and the weakest was found for co-infection of only
bacteria (HR: 2.909; 95%CI: 1.308-6.471). Interestingly, the promotion
in co-infection of mixed bacteria and viruses was between co-infection
of only bacteria and only viruses, and its HR was 3.242 with 95%CI
ranging from 1.171 to 8.977. However, there is no clear explanation for
these findings. One of the potential explanation for this phenomenon may
attribute to combination therapy. Although there has been no treatment
guideline for co-infection in COVID-19, and the recommendations from
different organizations are also inconsistent, combination therapy with
non-anti-SARS-CoV-2 agents in co-infected COVID-19 patients has been
seriously considered. In China, antibiotic therapy was recommended under
different situations for COVID-19 patients in whom co-bacterial
infection cannot be ruled out. Empirical antibiotic, such as
amoxicillin, azithromycin, or fluoroquinolones, was recommended for mild
cases, but broad-spectrum antibiotic covering all possible pathogens was
suggested for severe cases (Jin et al., 2020). Based on the limited data
of the present work, it remains unclear which antimicrobial agents
should be empirically prescribed in patients with COVID-19. In addition,
antimicrobial stewardship program should be implemented to prevent the
rising rates of antimicrobial resistance could be caused by an increase
in inappropriate antibiotic use for viral pneumonia (Huttner et al.,
2020). Besides combination therapy, another potential explanation may
attribute to much antagonistic effect of bacteria for SARS-CoV-2 than it
of other viruses. Our findings suggest that there may be interaction in
viral or bacterial replication and amplification in COVID-19
co-infection. As of now, there has been no evidence explain this
phenomenon. Wilks et al proposed that defence system of host, as a
supraorganism, contained commensal bacteria and immune system to against
bacterial and viral pathogens (Wilks et al., 2012). Several researchers
supported the view that the microbiota could inhibit viral replication,
and affect virally induced pathogenesis (Domínguez-Díaz C et al., 2019;
Khan R et al., 2019, Shi Z et al., 2018). Moreover, viruses in multiple
infections can interact with each other in different ways, with
different results such as antagonism (Mascia et al., 2016). These views
may be useful in explaining our findings.
It is notable that there are several limitations of this study. A
relatively small number of COVDI-19 cases were evaluated in comparison
with other studies. There were 55 discharged patients of COVID-19 in
Qingdao during the study period. Although we firstly assessed the
association between co-infection and negative conversion of SARS- CoV-2
RNA, further studies regarding the impact of co-infection on COVID-19
prognosis should be warranted. In our study, we try to identify the
interaction between SARS-CoV-2 and other respiratory pathogens by the
duration of negative conversion. The timeline of viral load of
SARS-CoV-2 and other respiratory pathogens during the disease period may
be also an effective variable for better understanding the interaction
with co-infected pathogens. Due to lack of continuous Ct values of all
pathogens, we are unable to analyze in this aspect, but future studies
should pay more attention doing this work.