Abstract
Objective
This study aims to investigate the prevalence, pathogen spectrum, clinical characteristics, and prognosis-related factors of other respiratory pathogens in COVID-19-infected patients, and to explore the application of molecular detection methods in the epidemiological investigation of multiple pathogen infections.
Methods
Respiratory samples and clinical data from 384 patients with outpatient and inpatient respiratory infections were collected and analyzed. Multiplex PCR and capillary electrophoresis were conducted to detect the distribution characteristics of 26 pathogen species, comprising 13 viruses, 13 bacteria. Statistical analysis explored the relationship between pathogen distribution with COVID-19 development.
Results
There was no statistical difference in prognosis between the 230 COVID-19-positive patients and the 154 COVID-19-positive patients. COVID-19 cases co-infected with other pathogens do not correlate with patient’s age and gender. The main distribution of pathogens was mainly mecA (n = 62, 26.96%), followed by SPN(n = 61; 26.52%) and KP(n = 22; 9.57%). Compared with non-COVID-19 cases, COVID-19 infected patients showed a significantly higher mecA carrying rate (26.96% vs. 15.58%, P < 0.01), while there was no statistical difference for other pathogens. Regression analysis found that mecA and KP were independent risk factors for severe illnesses (mechanical ventilation, endotracheal intubation, ECOMO, etc.) or death in COVID-19 patients had 2.391 times and 3.722 times risk of severe disease or death compared with COVID-19 patients without mecA and KP.
Conclusion
COVID-19 patients show a higher mecA carrier rate than non-COVID-19 patients, and mecA and KP may increase the risk of severe disease or death in COVID-19 patients, which requires close attention.
Keywords: COVID-19, Respiratory pathogens, mecA, Klebsiella pneumoniae (KP), Epidemiology
Introduction
Novel coronavirus (SARS-CoV-2) causes widespread global infection (COVID-19). It mainly causes a series of symptoms by targeting the angiotensin-converting enzyme 2 (ACE 2) receptor in the body, which can cause severe pneumonia and even death in severe cases [1]. Studies have found that SARS-CoV-2 infection is not the only factor causing severe pneumonia or even death, patients are often co-infected with other pathogens, and multiple pathogens’ coinfection may be associated with aggravation of symptoms [2, 3]. Literature shows that patients co-infected with COVID-19 have more severe dyspnea symptoms than patients with COVID-19 alone (48.1% vs. 37.3%), and the mortality rate increases to three times [4]. Patton et al. [5] also found that the mortality rate of COVID-19 patients with bacterial infections (24%) significantly exceeded the associated mortality rate of community-acquired bacteremia (5.9%) and was consistent among the different alpha, delta, and Omicron SARS-CoV-2 variants. The proportion of co-infections in patients infected with SARS-CoV-2 and the types of viruses involved vary in other parts of the world, depending on the sensitivity of the diagnostic test used, the population studied, the climate, the sampling period, and temporal variation in viral epidemiology [6]. At present, many studies believe that mixed infections of respiratory pathogens are uncommon. A pooled research indicates that the incidence of combined bacteria and other viruses is only 7% and 3%, respectively [7]. However, few reports on the changes in the pathogen spectrum of respiratory pathogens after SARS-CoV-2 infection. It has been suggested that antimicrobial treatment and diagnostic testing are not required on admission for most patients hospitalized with COVID-19, while our preliminary experimental results suggested the presence of other pathogens in the SARS-CoV-2 pandemic, which is quite different from previous reports [8]. In this study, we studied the distribution of respiratory pathogens (including viruses, bacteria, and resistance genes) in the subjects of SARS-CoV-2 infection and non-SARS-CoV-2 infection using highly sensitive and highly specific multiplex PCR combined with capillary electrophoresis, explored the pathogen spectrum types of multiple pathogen infections in COVID-19 patients and their relationship with the risk of severe illness and death, to provide laboratory data support for the rational use of antibiotics in clinical practice and reduce the incidence of severe illness and death caused by SARS-CoV-2 infection.
Materials and methods
Data collection
A total of 384 respiratory samples (oropharyngeal swabs, nasopharyngeal swabs, sputum, alveolar lavage fluid, etc.) from outpatient and inpatient pneumonia patients on the day of admission to the hospital during the domestic COVID-19 pandemic on 7th January and 31st March 2023 were collected. After sampling, put it into a special virus storage tube and send it immediately to the PCR laboratory of our hospital. The sample will be pre-processed and tested after being stored at 4 °C for less than 24 h. The patient’s electronic medical records are tracked and reviewed through the hospital’s CIS system, and the patient’s age, gender, basic diseases, condition changes, and outcomes are collected. Any patient with mechanical ventilation, endotracheal intubation, ECOMO, death, etc. was judged to have a poor prognosis. This study has been approved by the The First Affiliated Hospital of Ningbo University ethics committee (approval number: 2024-Research No.175RS). Waiver of informed consent was obtained because of the nature of the study.
Nucleic acid extraction and purification methods
Viral DNA and RNA were purified using a viral nucleic acid extraction reagent (Chongqing Zhongyuan Huiji) according to the manufacturer’s instructions.
Polymerase chain reaction (PCR)
SARS-CoV-2 was tested using a novel Coronavirus nucleic acid test kit (Wuhan Mingde). Multiplex PCR fluorescent probe technology combined with one-step RT-PCR technology is used to detect specific N and ORF1ab gene fragments, and the Ct ≤ 35 were judged as positive. The human housekeeping gene RNase P is used to monitor the sampling quality and experimental validity. The minimum detection limit is 200 copies/mL, the positive coincidence rate is 95.53%, the negative coincidence rate is 94.07%, and the total coincidence rate is 94.8%.Other respiratory pathogen, including influenza A virus (InfA), adenovirus (ADV), boca virus (Boca), rhinovirus (HRV), influenza A virus H1N1 (H1N1), parainfluenza virus (HPIV), Chlamydia (Ch), parneumovirus (HMPV), influenza B virus (InfB), Mycoplasma Pneumoniae (Mp), influenza A virus H3N2 (H3N2), coronavirus (HCOV), respiratory syncytial virus (HRSV), Streptococcus pyogenes (SPY), Streptococcus pneumoniae (SPN), Pseudomonas aeruginosa (PA), mecA gene(mecA), Klebsiella pneumoniae (KP), Staphylococcus aureus (SA), Acinetobacter baumannii (AB), Stenotrophomonas maltophilia (SM), Moraxella catarrhalis (MC), Haemophilus influenzae (HI), Enterobacter cloacae complex (ECC), Escherichia coli (Eco), Legionella pneumophila (LP), were detected using the respiratory pathogen multiple nucleic acid detection kit, which designed specific primers for each pathogen gene sequence (to ensure that each set of primers can only amplify one pathogen), and is composed of fluorescently labeled specific reactions. After the fluorescent-labeled specific reverse primer is combined with the corresponding template RNA for reverse transcription, the specific forward primer and reverse primer use DNA or the reverse transcription product cDNA as a template to perform multi-template amplification to produce a large number of PCR products of fragments of different lengths. All PCR processes were carried out according to the reaction procedures set up in the reagent instructions, and the Gentier96R real-time fluorescence quantitative PCR system (TianLong, Xi’an, China) was used for amplification.
Capillary electrophoresis
In addition to novel coronavirus, nucleic acid PCR products of other respiratory pathogens were separated using a CE2400 capillary electrophoresis analyzer (Ninbo HEALTH Gene Technologies Co.,Ltd). The pathogen type is determined by detecting the characteristic fluorescence signal values(i.e., peak height values) of the characteristic different length fragments. RT-PCR internal reference (RT-PCR Internal Control, IC) is used to monitor nucleic acid extraction, RT-PCR reaction, and capillary electrophoresis process of samples; human DNA internal reference (huDNA) and human RNA internal reference (huRNA) are used to monitor sample quality.
Statistical analysis
Classification data were compared using a chi-square test or Fisher exact test, and continuous variables were compared using the student t-test. Furthermore, Logistic regression analysis models evaluated the risk factors for adverse disease progression and outcomes in COVID-19 patients.
Results
A total of 230 SARS-CoV-2 positive cases among the 384 patients, including150 males (65.2%) and 80 females (34.8%), age ranging from 74 days to 100 years old, with an average age of 73.50 ± 16.15 years, and the median age was 76 years. 154 SARS-CoV-2 negative cases, including 86 (55.8%) males and 68 (44.2%) females ranging in age from 1 year and 5 months to 99 years old, with an average age of 66 years ± 22.95 years and a median age of 72.6 years. The average age of COVID-19 patients was higher than that of non-COVID-19 patients (P = 0.001), and the difference in gender composition was not significant (P = 0.064). The proportion of COVID-19 patients with poor prognosis was higher than that of non-COVID-19 patients, but the difference was not significant (P = 0.074), Table 1.
Table 1.
Prognostic comparison between COVID-19 patients and non-COVID-19
| Groups | favorable outcome | poor outcome | n = number; % |
|---|---|---|---|
| COVID-19 | n = 184; 80.0% | n = 46; 20.0% | n = 230; 100.0% |
| Non-COVID-19 | n = 134; 87.0% | n = 20; 13.0% | n = 154; 100.0% |
| Total | n = 318; 82.8% | n = 66; 17.2% | n = 384; 100.0% |
The percentage of co-infected pathogens in COVID-19 and non-COVID-19 groups is shown in Figs. 1 and 2, respectively. Additionally, a comparison of the percentages of COVID-19 coinfections and non-COVID-19 infection pathogens is shown in Fig. 3; Table 2. It was found that there was no significant difference between COVID-19 patients and non-COVID-19 patients in infection with other non-bacterial pathogens (1.7% VS 3.2%) and bacterial pathogens (56.1% VS 51.9%) (P > 0.05). However, a comparison of pathogen classification found that the mecA gene carrying rate of COVID-19 patients (Fig. 1) was significantly higher than that of the non-COVID-19 group (Fig. 2) (26.96% VS 15.58%, and P < 0.01). No significant difference was detected among the other pathogens. Table 2. HRV, H1N1, HPIV, InfB, H3N2, HCOV, HRSV, and SPY were not detected in either COVID-19 or non-COVID-19 patients.
Fig. 1.
Percentage of COVID-19 coinfected pathogens
Fig. 2.
Percentage of non-COVID-19 infected pathogens
Fig. 3.
Percentage of COVID-19 coinfected and non-COVID-19 infected pathogens. The abscissa represents the percentage, the ordinate represents pathogens
Table 2.
Comparison of co-infected pathogens in non-COVID-19 and COVID-19 patients
| pathogens | non-COVID-19 group (N = 154, %) |
COVID-19 group (N = 230, %) |
P |
|---|---|---|---|
| virus | n = 5, 3.2% | n = 4, 1.7% | 0.540 |
| bacteria | n = 80, 51.9% | n = 129, 56.1% | 0.425 |
| ADV | n = 0, 0.00% | n = 2, 0.87% | - |
| InfA | n = 0, 0.00% | n = 1, 0.43% | - |
| Mp | n = 2, 1.30% | n = 0, 0.00% | - |
| HMPV | n = 0, 0.00% | n = 1, 0.43% | - |
| Ch | n = 0, 0.65% | n = 0, 0.00% | - |
| Boca | n = 2, 1.30% | n = 0, 0.00% | - |
| SPN | n = 47, 30.52% | n = 61, 26.52% | 0.393 |
| PA | n = 25, 16.23% | n = 4, 1.74% | 0.000 |
| mecA | n = 24, 15.58% | n = 62, 26.96% | 0.009* |
| KP | n = 15, 9.74% | n = 22, 9.57% | 0.955 |
| SA | n = 6, 3.90% | n = 20, 8.70% | 0.067 |
| AB | n = 4, 2.60% | n = 10, 2.35% | 0.370 |
| SM | n = 3, 1.95% | n = 6, 2.61% | 0.94 |
| MC | n = 2, 1.30% | n = 2, 0.87% | 1.000 |
| HI | n = 3, 1.95% | n = 5, 2.17% | 1.000 |
| ECC | n = 2, 1.30% | n = 3, 1.30% | 1.000 |
| Eco | n = 0, 0.00% | n = 2, 0.87% | - |
| LP | n = 0, 0.00% | n = 1, 0.43% | - |
| MRSA | n = 6, 3.90% | n = 16, 7.0% | 0.206 |
Note: influenza A virus (InfA), adenovirus (ADV), boca virus (Boca), rhinovirus (HRV), influenza A virus H1N1 (H1N1), parainfluenza virus (HPIV), Chlamydia (Ch), parneumovirus (HMPV), influenza B virus (InfB), Mycoplasma (Mp), influenza A virus H3N2 (H3N2), coronavirus (HCOV), respiratory syncytial virus (HRSV), Streptococcus pyogenes (SPY), Streptococcus pneumoniae (SPN), Pseudomonas aeruginosa (PA), mecA gene(mecA), Klebsiella pneumoniae (KP), Staphylococcus aureus (SA), Acinetobacter baumannii (AB), Stenotrophomonas maltophilia (SM), Moraxella catarrhalis (MC), Haemophilus influenzae (HI), Enterobacter cloacae complex (ECC), Escherichia coli (Eco), Legionella pneumophila (LP)
Of the 230 COVID-19 patients, 133 were co-infected with other pathogens, aged 74.29 ± 16.69 years, and 97 were not co-infected with other pathogens, aged 72.47 ± 15.43 years, with no difference in mean age (P > 0.05). The adverse outcome rate of COVID-19 patients co-infected with other pathogens was significantly higher than that of those without co-infection (P = 0.001), and the difference was statistically significant, Table 3.
Table 3.
Analysis of COVID-19 coinfections and outcomes
| Groups | favorable outcome | poor outcome | Total |
|---|---|---|---|
| COVID-19 co-infection with other pathogens | n = 94; 72.3% | n = 36; 27.7% | n = 130; 100.0% |
| COVID-19 is not co-infection with other pathogens | n = 90; 90.0% | n = 10; 10.0% | n = 100; 100.0% |
| Total | n = 184; 80.0% | n = 46; 20.0% | n = 230; 100.0% |
Binary logistic regression was used to evaluate the correlation between the above factors and disease progression and outcome in COVID-19 patients. The linearity of the logit for the continuous independent variables in logistic regression analysis was assessed using the Box-Tidwell test. Ten observations with studentized residuals greater than 2.0 times the standard deviations were retained in the analysis. Ultimately, the logistic regression model was statistically significant, χ2 = 37.193, P = 0.008 < 0.05, Hosmer-Lemeshow test = 0.982 > 0.05, and the model was able to classify 81.7% of research objects correctly. Among the 19 independent variables included in the model, the mecA gene (P = 0.042), KP (P = 0.017) were statistically significant, but age (P = 0.323), gender (P = 0.328), MRSA (P = 0.801), ADV (P = 0.999), InfA (P = 1.000), the HMPV (P = 1.000), SPN (P = 0.498), PA (P = 0.895), AB (P = 0.835), SM (P = 0.072), MC (P = 0.999), HI (P = 0.999), ECC (P = 0.536), ECL(P = 0. 999), LP (P = 1.000) were not statistically significant. Severe disease (mechanical respiration, organ intubation, ECOMO, etc.) or death risk was increased by 1.391 and 2.722 times, respectively, in COVID-19 patients with the mecA gene and KP, Table 4; Fig. 4.
Table 4.
Prognostic risk factor analysis of COVID-19 patients
| B | S.E. | Wald | df | P | Exp(B) | 95% confidence interval for Exp(B) | ||
|---|---|---|---|---|---|---|---|---|
| lower limits | upper limits | |||||||
| Gender | 0.38 | 0.389 | 0.956 | 1 | 0.328 | 1.462 | 0.683 | 3.131 |
| Age | 0.015 | 0.015 | 0.976 | 1 | 0.323 | 1.015 | 0.985 | 1.046 |
| Combined with underlying diseases or postoperative | 19.364 | 9926.288 | 0 | 1 | 0.998 | 256881580.6 | 0 | |
| MRSA | 0.372 | 1.472 | 0.064 | 1 | 0.801 | 1.451 | 0.081 | 25.985 |
| ADV | -19.776 | 23949.914 | 0 | 1 | 0.999 | 0 | 0 | |
| InfA | -19.363 | 40192.97 | 0 | 1 | 1.000 | 0 | 0 | |
| HMPV | -0.256 | 41400.556 | 0 | 1 | 1.000 | 0.774 | 0 | |
| SPN | -0.303 | 0.447 | 0.459 | 1 | 0.498 | 0.739 | 0.308 | 1.773 |
| PA | -0.173 | 1.304 | 0.018 | 1 | 0.895 | 0.841 | 0.065 | 10.834 |
| mecA | 0.872 | 0.429 | 4.129 | 1 | 0.042* | 2.391* | 1.031 | 5.543 |
| KP | 1.314 | 0.552 | 5.676 | 1 | 0.017* | 3.722* | 1.262 | 10.974 |
| SA | 0.585 | 1.295 | 0.204 | 1 | 0.651 | 1.796 | 0.142 | 22.719 |
| AB | 0.168 | 0.809 | 0.043 | 1 | 0.835 | 1.183 | 0.242 | 5.774 |
| SM | 1.724 | 0.96 | 3.227 | 1 | 0.072 | 5.605 | 0.855 | 36.759 |
| MC | 18.694 | 14724.198 | 0 | 1 | 0.999 | 131419427.8 | 0 | |
| HI | -36.381 | 20823.16 | 0 | 1 | 0.999 | 0 | 0 | |
| ECC | 0.833 | 1.345 | 0.384 | 1 | 0.536 | 2.3 | 0.165 | 32.084 |
| ECL | -20.944 | 28275.524 | 0 | 1 | 0.999 | 0 | 0 | |
| LP | -18.837 | 40192.97 | 0 | 1 | 1.000 | 0 | 0 | |
| Constant | -22.446 | 9926.288 | 0 | 1 | 0.998 | 0 | ||
Fig. 4.
Prognostic risk factors for COVID-19 patients. The abscissa represents the odds ratio (OR) value and 95% confidence interval (CI), and the ordinate represents risk factors
Discussion
The spread of the COVID-19 pandemic may have affected antibiotic consumption patterns and the prevalence of colonization or infection with multidrug-resistant (MDR) bacteria and promoted the nosocomial spread of MDR [9]. In this study, we found that higher rates of co-bacterial infection and lower rates of combined virus infection existed in COVID-19 patients, with the most detected bacterial pathogens were bacteria carrying the mecA gene. And SARS-CoV-2 positive patients with a higher mecA carriage rate and KP positive were independent risk factors for severe illness (mechanical breathing, endotracheal intubation, ECOMO, etc.) or death. The high mecA carriage rate in COVID-19 patients may be related to the early irregular use of antibiotics. Previous study also suggested that specific effects of COVID-19 on the antibiotics resistance genes [10, 11]. One study reveled that the bacteremia caused by Staphylococcus aureus is linked with a significant death rate in individuals hospitalized with COVID-19 [12]. Another study showed that COVID-19 patients were more likely to be associated with Clostridioides difficile infection than non–COVID-19 patients [13]. Yu et al. [14] revealed that The increased incidence of resistance to carbapenems in COVID-19 patients could be attributable to increased use of broad-spectrum antibiotics. Extensive and empiric use of antibiotics in treating patients, particularly in the early stages of the pandemic, has had adverse effects on antibiotic resistance management practices [15]. Our experimental results showed that the bacterial co-infection rate in COVID-19 patients was 56.7%, which was quite different from the previous pooled study results of 7% [7]. In addition to factors such as differences in population and hospital management, one of the speculated reasons may be related to different laboratory testing techniques. Previously laboratory-confirmed bacterial infections were mostly confirmed by respiratory samples, blood bacterial or fungal cultures, by antigen testing methods, or PCR testing for respiratory pathogens [16]. However, In the early stage of infection, if antigen and antibody detection are used to diagnose pathogen infection, there may be a false negative window period. If methods such as bacterial culture are used, there may be false negatives such as failure to inoculate in time or various other reasons leading to pathogen death and culture failure. In most cases, the diagnosis is stopped first by testing the most likely or most incidental viral pathogen and then by testing the main associated infectious agents, and simultaneous, rapid, reliable, and accurate identification of each pathogen associated with infection is critical for patient management, surveillance, and infection control, including prevention of nosocomial transmission [17].
This study used a multiplex PCR-capillary electrophoresis method to improve detection sensitivity and specificity. It can detect 26 pathogenic nucleic acids at once, which is conducive to the rapid and accurate early identification of infectious pathogens. It has been reported in the literature that the use of multiplex PCR methods to detect respiratory pathogens increases the diagnostic yield by 30–50% compared with direct fluorescent antibodies and culture [18]. Meta-analysis data showed that multiplex PCR provides highly accurate results over the relevant time frame [19]. It is worth considering that the mixed infectious pathogens in this study were defined as those recovered from respiratory secretions or alveolar lavage, however, accurately distinguishing relevant pathogens from incidental or colonized organisms is essentially difficult, especially from sites such as the respiratory tract. On the other hand, the recovery of pathogens is affected by antimicrobial use, and the majority of cases have empirically used antibiotics at the beginning of receiving treatment, possibly leading to a reduced detection rate of some pathogens.
Despite being in other viral epidemic seasons such as influenza, our observations showed that COVID-19 patients showed lower (1.3%) co-viral infections and lower than non-COVID-19 patients (1.3% vs. 3.2%, P > 0.05), with no statistical difference. It may be possible that during the peak period of SARS-CoV-2 infection, the population uses masks and other respiratory tract protection measures and has a high awareness of hand hygiene, which reduces the infection probability of other viruses to a certain extent. In addition, because virus infection may exist mutual inhibition [20], virus infection after the activation of non-specific antiviral function (including protein kinase R, 2–5 A system, Mx protein, and apoptosis pathway), and after SARS-CoV-2 first infection, in the face of multiple activated antiviral responses, inhibit the second virus cause infection will be a reasonable result [21]. The majority of patients included in this study were adults, with an older average age, while common respiratory viral infections are common in children, so further expansion of the study population is needed to further improve the study of SARS-CoV-2 combined with viral infection.
Respiratory tract infection is from mild cold to severe pneumonia of a variety of similar respiratory symptoms [22]. Multiple PCR combined with capillary electrophoresis experimental method can be used to determine the SARS-CoV-2 positive patients early virus, bacteria, infection, timely identification of coexisting pathogens, provide pathogen targeted therapy, more cautious, targeted reasonable use of antibiotics, avoid a large, empirical prescription of antibiotics, to prevent the adverse outcome of SARS-CoV-2 patients during the pandemic.
Conclusion
In conclusion, the incidence of mecA gene in COVID-19 patients is significantly higher than in those without COVID-19, and the mecA gene positivity and KP may be the independent risk factors for severe disease (mechanical respiration, endotracheal intubation, ECOMO, etc.) or death in COVID-19 patients. Therefore, It is recommended to evaluate for co-infections in the early stages of SARS-CoV-2 infection, with accurate and rational use of antibiotics, reduce co-bacterial infection in COVID-19 patients, and reduce the risk of severe illness and death in COVID-19 patients.
Acknowledgements
Thanks to The First Affiliated Hospital of Ningbo University, Zhejiang Province, for supporting the work.
Author contributions
Y.D. and S.T. supervised and designed the experiments and wrote the article. H. Y., Q. F., J.K., F. G., Y. Y., P. C., Y. Z., and W. J generated and collected the data and helped to write the article. W.L. helped to review and analyze the data.
Funding
This work was supported by the Novel Coronavirus infection disease science and technology research project (XGZ2303).
Data availability
All data generated or analyzed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
This study has been approved by the The First Affiliated Hospital of Ningbo University ethics committee (approval number: 2024-Research No.175RS). Waiver of informed consent was obtained because of the nature of the study.
Consent for publication
All authors have approved of the consents of this manuscript and provided consent for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yanping Dai and Shuan Tao these co-first authors share the first author position.
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Associated Data
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Data Availability Statement
All data generated or analyzed during this study are included in this published article.