Evaluation of Acinetobacter baumannii, Klebsiella pneumoniae, and Staphylococcus aureus respiratory tract superinfections among patients with COVID-19 at a tertiary-care hospital in Tehran, Iran
European Journal of Medical Research volume 28, Article number: 314 (2023)
The emergence of healthcare-associated infections (HAIs) or superinfections in COVID-19 patients has resulted in poor prognosis and increased mortality.
In a cross-sectional study, 101 respiratory samples were collected from ICU-admitted COVID-19 patients. The HAI rate, demographics, and antibiotic resistance were assessed.
The HAI rate was 83.16% (76.62% bacterial and 6.54% fungal). The prevalence of 3 major HAI-causing organisms included Klebsiella pneumoniae (41.5%), Acinetobacter baumannii (20.8%), and Staphylococcus aureus (4.9%). Mortality and intubation ventilation proportions of 90% (p = 0.027) and 92.2% (p = 0.02) were significant among patients with superinfection, respectively. Multiple logistic regression analysis showed SpO2 pressure (odds ratio 0.842; 95% CI 0.750–0.945; p = 0.004) as a predictive factor in the association between antibiotic usage and mortality. More than 50% of patients received carbapenems. The resistance rates to at least one antibiotic of third-generation cephalosporins, aminoglycosides, quinolones/fluoroquinolones, tetracyclines, and β-lactam inhibitors were 95.2%, 95.2%, 90%, 57.1%, and 100% among A. baumannii isolates and 71.4%, 55%, 69%, 61.9%, and 59.5% among K. pneumoniae isolates, respectively. A proportion of 60% was recorded for methicillin-resistant S. aureus isolates.
As a result, antibiotic treatment should be administered following the microbial resistance profile. Contact isolation and infection control measures should be implemented as needed.
SARS-CoV-2, the newest and seventh member of the Coronaviridae family, is the causative agent of severe respiratory illness in humans, similar to SARS-CoV and MERS-CoV , which was discovered in Wuhan’s Hubei Province in China at the end of 2019 and was designated coronavirus disease 2019 (COVID-19) by the WHO on February 11, 2020 . Since its first appearance, the COVID-19 virus has infected more than 753 million individuals worldwide, resulting in over 6.8 million fatalities by February 2023 . This global pandemic has impacted individuals of all ages, causing various clinical symptoms ranging from the common cold to fatal pneumonia . Risk factors and severe clinical COVID-19 cases are usually seen in elderly and immunocompromised individuals who suffer from various concomitant problems, such as cardiovascular diseases, diabetes, hypertension, and chronic obstructive pulmonary disease . SARS-CoV-2 virus can predispose immunocompromised hosts to various bacterial infections by exaggerated production of inflammatory cytokines causing uncontrollable immune responses, multiple organ damage, and severe pneumonia . The lung histopathologic findings reveal the role of healthcare-associated infections (HAIs) causing pneumonia in tissue damage, which can accompanied by increased mortality  (Fig. 1).
HAIs are defined as infections that manifest in hospitalized patients at least 48 h after hospital admission and are classified according to the Centers for Disease Control and Prevention/National Healthcare Safety Network (CDC/NHSN) criteria . K. pneumoniae, A. baumannii, and S. aureus isolates are three prevalent microorganisms causing HAI. These organisms are the leading cause of multidrug-resistant nosocomial pathogens, the so-called Enterococcus faecium, S. aureus, K. pneumoniae, A. baumannii, Pseudomonas aeruginosa, and Enterobacter spp. (ESKAPE) group, which account for rising annual healthcare costs and mortality .
Carbapenem-resistant K. pneumoniae (CRKP) and A. baumannii (CRAB) isolates contributed to a significant death rate among critically ill COVID-19 patients with a prolonged hospitalization history [10, 11]. In addition, S. aureus isolates are the second leading source of up to 11.8% of HAIs in the United States. They are responsible for both community and HAIs isolated from the lower respiratory tract of COVID-19 patients receiving ventilation  (Fig. 1). Given the apparent complication of MRSA in causing ventilator-acquired superinfections in previous SARS-CoV and influenza outbreaks , the prevalence of superinfection caused by this organism should be considered in the current pandemic to account for therapeutic targets against this infection .
This study aimed to assess the prevalence of healthcare-associated respiratory infections or superinfections, antibiotic resistance profiles against the three HAIs mentioned above, and compliance with clinical outcomes among COVID-19 patients admitted to the ICUs of a major hospital in Tehran, Iran.
The current study was reviewed and approved by the institutional ethics committee of Tehran University of Medical Sciences with an approved ID: IR.TUMS.MEDICINE.REC.1399.1092. Informed consent was obtained from patients or the relatives of unconscious patients. Additionally, due to the probable inconvenience for critical COVID-19 patients and the risk of virus transmission during sampling, we used respiratory samples taken routinely by nursing personnel who were transferred to the microbiology laboratory.
Study design and sample collection
In a cross-sectional study between March 2021 and July 2021, 101 single and nonduplicate respiratory adult patient samples, including endotracheal aspirate (ETA), bronchoalveolar lavage, and sputum, were collected from confirmed critically ill SARS-CoV-2 patients admitted to Imam Khomeini Hospital Complex, a referral teaching hospital of Tehran University of Medical Sciences. The current study was conducted according to guidelines by the Helsinki Declaration per Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, , and the collection of 101 respiratory patient samples was based on a pilot study on 20 ICU patients with COVID-19, which estimated the prevalence of nosocomial infections to be 77%, with a type I error of 5% and a power of 80%. The population consisted of all ICU patients with SARS-CoV-2, excluding pregnant women and children.
Data were gathered from electronic medical and nursing records. These included patient demographics, clinical SARS-CoV-2 symptoms, baseline comorbidities, average length of hospital stay, microbial analysis, radiographic findings of lung involvement, SpO2 pressure, need for mechanical ventilation, prescribed antibiotic therapy and final disposition (discharged alive or expired). In this study, the severity of coronavirus disease in all confirmed COVID-19 patients was evaluated based on criteria including the need for intubation, respiratory rate > 30 per minute, lung involvement > 50%, and SpO2 pressure, which was divided into four categories [mild (SpO2 ≥ 93), moderate (90 ≥ SpO2 ≥ 92), severe (88.1 ≥ SpO2 ≥ 89.9), and critical (SpO2 ≤ 88)].
Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for detection of confirmed SARS-CoV-2 patients
Nasopharyngeal samples of suspected SARS-CoV-2 patients were tested by multiple one-step quantitative RT-PCR approaches. This experiment was carried out by targeting the E-gene (FAM) and S-gene (ROX) as screening values and detecting the RNaseP (HEX) gene of coronavirus as a confirmatory agent using the Covitech multiplex qPCR kit (ACECR, Iran) according to the manufacturer's instructions.
Detection of bacterial and fungal superinfection
Respiratory specimens were collected after 48 h of admission and transported to the microbiology laboratory to detect bacterial and fungal infection types by culturing the samples on standard culture media such as blood agar, chocolate agar, and Sabouraud dextrose agar. The generated colonies were then subcultured on several differential culture media, such as MacConkey Agar, Mannitol Salt Agar (MSA), and CHROM Agar Candida media, with incubation at 35–37 °C for 18–24 h. The resulting colonies were validated by Gram staining and a series of standard biochemical and diagnostic tests, such as catalase, urease, Simmon’s citrate, oxidase, triple sugar iron (TSI) agar, sulfide indole motility (SIM), methyl red/Voges–Proskauer (MR/VP), DNase, and lysin decarboxylase. A quantitative culture-based method was also performed to discriminate true infection from potential contamination. A colony-forming unit load of at least 104 per positive culture was considered a true pathogen [18, 19].
To identify the suspected A. baumannii colonies, the 16S-23SrRNA gene intergenic spacer (ITS) region and blaOXA-51-like carbapenemase gene were amplified, as described previously . In addition, for confirmation of the Candida albicans complex colonies, the hwp1 gene was also amplified as described previously .
Antibacterial susceptibility determination
Antimicrobial susceptibility testing was carried out by the Kirby–Bauer disk diffusion method and broth microdilution approaches . The Kirby–Bauer disk diffusion results were interpreted by measuring an inhibition zone around every antibiotic disk in Muller–Hinton agar (MHA) media containing each isolate and categorizing them as susceptible (S), intermediate (I), or resistant (R) according to the zone diameter breakpoint recommended by CLSI guidelines . The antibiotic disks (Rosco, Taastrup, Denmark) used against each bacterial group consisted of the following:
Antibiotic disks were used against K. pneumoniae isolates
Cefazolin (CZ, 30 µg), cefepime (CPM, 30 μg), cefotaxime (CTX, 30 µg), ceftriaxone (CRO, 30 µg), ceftazidime (CAZ, 30 μg), aztreonam (ATM, 30 µg), imipenem (IPM, 10 μg), meropenem (MER, 10 μg), gentamycin (GM, 10 µg), tobramycin (TM, 10 µg), amikacin (AN, 30 µg), tetracycline (TE, 30 µg), doxycycline (DO, 30 µg), levofloxacin (LVX, 5 μg), ciprofloxacin (CP, 5 µg), ofloxacin (OFL, 5 µg), trimethoprim–sulfamethoxazole (SXT, 1.25/23.75 µg), chloramphenicol (C, 30 µg), piperacillin–tazobactam (TZP, 100/10 µg), and ampicillin–sulbactam (SAM, 10/10 µg).
Antibiotic disks were used against A. baumannii isolates
Cefepime (CPM, 30 μg), cefotaxime (CTX, 30 µg), ceftriaxone (CRO, 30 µg), ceftazidime (CAZ, 30 μg), imipenem (IPM, 10 μg), meropenem (MER, 10 μg), gentamycin (GM, 10 µg), tobramycin (TM, 10 µg), amikacin (AN, 30 µg), doxycycline (DO, 30 µg), levofloxacin (LVX, 5 μg), ciprofloxacin (CP, 5 µg), trimethoprim–sulfamethoxazole (SXT, 1.25/23.75 µg), piperacillin–tazobactam (TZP, 100/10 µg), and ampicillin–sulbactam (SAM, 10/10 µg).
Antibiotic disks were used against S. aureus isolates
Gentamycin (GM, 10 µg), amikacin (AN, 30 µg), tetracycline (TE, 30 µg), doxycycline (DO, 30 µg), levofloxacin (LVX, 5 μg), ciprofloxacin (CP, 5 µg), ofloxacin (OFL, 5 µg), trimethoprim–sulfamethoxazole (SXT, 1.25/23.75 µg), chloramphenicol (C, 30 µg), penicillin (P, 10 U), cefoxitin (FOX, 30 μg), azithromycin (AZM, 15 μg), clindamycin (CD, 2 µg), rifampin (RA, 5 µg), and linezolid (LZD, 30 µg).
The broth microdilution assay
The broth microdilution (BMD) assay was performed by the minimum inhibitory concentration (MIC) method to evaluate the antimicrobial effect of colistin (COL) against A. baumannii and K. pneumoniae isolates and vancomycin (VAN) and oxacillin (OXA) against S. aureus isolates. The standard MIC breakpoints of COL against A. baumannii and Enterobacterales (MIC ≤ 2 μg/ml: intermediate and MIC ≥ 4 μg/ml: resistant) were employed. Additionally, the MIC breakpoints of VAN (MIC ≤ 2 µg/ml: susceptible, 4 ≤ MIC ≤ 8: intermediate, and MIC ≥ 16: resistant) and OXA (MIC ≥ 4 µg/ml: resistant and MIC ≤ 2 µg/ml: susceptible) against S. aureus isolates were used . Escherichia coli (ATCC 25922), P. aeruginosa (ATCC 27853), S. aureus (ATCC 25923), and S. aureus (ATCC 700699) were treated as controls. Detection of MRSA isolates was further confirmed by the amplification of the mec-A gene by PCR .
Determination of MDR and ESBL-producing isolates
The prevalence of MDR isolates was assessed. MDR isolates are categorized as resistant or nonsusceptible to at least one antimicrobial agent among three or more antibiotic classes . Additionally, the combination disk technique was used to examine the extended-spectrum beta-lactamase (ESBL)-producing isolates. Ceftazidime (CAZ, 30 g) and ceftazidime–clavulanate (CAV, 30/10 g, Mast), cefotaxime (CTX, 30 g), and cefotaxime–clavulanate (CV, 30/10 g, Mast) were used on Muller–Hinton agar inoculated by suspected ESBL-producing isolates . The positive ESBL-producing K. pneumoniae isolates were verified by a ≥ 5 mm increase in inhibition zone diameter surrounding cefotaxime–clavulanate and/or ceftazidime–clavulanate compared to the diameter inhibition zone of ceftazidime and cefotaxime separately .
Student’s t test was used to find the mean (SD) of continuous variables through a comparative analysis. The Chi-square test or Fisher's exact Chi-square test was employed to compare categorical variables. Univariate and multiple logistic regression analyses were conducted to determine the parameters independently associated with antibiotic treatment and mortality in COVID-19 patients with and without HAIs. Initially, univariate logistic regression was conducted by considering all causes of mortality. The predictive variables with a p value ≤ 0.2 were selected and introduced into the multiple logistic regression model to control the effect of confounding variables. P values less than 0.05 were considered statistically significant. All statistical analyses were performed with IBM SPSS Statistics 26.0.
Clinical and demographic features
HAI was diagnosed in 89% (84/101) of confirmed COVID-19 patients. Patients with HAI were older on average, with a mean age of 61 (SD 12.45; IQR 17) years; more than half (49/84) were men (58.3%). Almost all clinical symptoms and severity of COVID-19 increased in individuals with HAI compared to those without HAI. As a result, lung involvement of 50% was only recorded in patients with HAI. The median length of hospital stay duration was 18 (IQR 17) days (p = 0.184). The proportion of in-hospital mortality (90%, 63/70) (p = 0.027) and the need for intubation ventilation (92.2%, 59/64) (p = 0.02) were significantly higher in patients with HAI. Almost 84.3% (59/70) of expired patients were under mechanical ventilation (p = 0.000). Hypertension, diabetes mellitus, immunodeficiency, and coronary heart disease were more prevalent in patients with HAI, as shown in Table 1.
Microbial and fungal isolates causing HAI
Bacterial and fungal superinfections accounted for 82.17% and 5.94%, respectively. Approximately 65.3% of samples (66/101) were monomicrobial, 17.8% (18/101) were multimicrobial, and 1.98% (2/101) included bacterial and fungal infections and were considered mixed infections, while 11.88% (12/101) of patient samples had no culturable infections. The isolated microorganisms were K. pneumoniae (41.5%, 42/101), A. baumannii (20.8%, 21/101), S. aureus (4.9%, 5/101), E. coli (3.9%, 4/101), P. aeruginosa (3.9%, 4/101), S. epidermidis (2.9%, 3/101), Citrobacter spp. (1.9, 2/101), Enterobacter spp. (0.9%, 1/101), Stenotrophomonas spp. (0.9%, 1/101) and Candida albicans complex (5.94%, 6/101). One expired patient with lung involvement ≥ 80% was diagnosed with bacterial and fungal superinfection.
Medications and HAI
Antibiotics, antivirals (remdesivir), antifungals, corticosteroids, and other associated drugs were prescribed more frequently in COVID-19 patients with HAI (Table 1). More than 85.5% of the patients with HAI received at least one antibiotic. While more than 55% of all patients were under treatment with carbapenems (IPM and MER), almost 86% of patients who received carbapenems had HAI. Meropenem (53.7%, 44/50), COL (37.8%, 31/33), and VAN (39%, 32/37) were the most commonly used antibiotics in patients with HAI. Except for one discharged patient, no history of antibiotic therapy was recorded. Univariate logistic regression revealed that age (men) (OR, 1.056; CI 95%, 1.017–1.097), (p = 0.004), total HAI (OR, 3.789; CI 95%, 1.216–11.810), (p = 0.022), SpO2 pressure (OR, 0.864; CI 95%, 0.786- 0.949), (p = 0.002), and antibiotic use (OR, 3.471; CI 95%, 1.240–9.714), (p = 0.018) could be independent factors associated with in-hospital mortality. The multiple regression analysis and SpO2 pressure (OR, 0.842; CI 95%, 0.750–0.945), (p = 0.004) were ascertained as the final predictive factors in the correlation between antibiotic use and in-hospital mortality, as shown in Table 2.
Antibacterial susceptibility profile
The results of antibacterial susceptibility testing, as shown in Figs. 2, 3, 4, demonstrated a dramatic increase in antibiotic resistance among K. pneumoniae and A. baumannii isolates. CRAB and CRKP proportions of 100% and 50% were detected against at least one antibiotic of carbapenems (MER and/or IPM). Approximately 24% of A. baumannii and 7.14% of K. pneumoniae isolates were resistant to COL. The resistance proportion to at least one antibiotic of third-generation cephalosporins (CTX, CRO, and CAZ), aminoglycosides (AN, TM, and GM), quinolones/fluoroquinolones (LVX, CP, and OFL), tetracyclines (TE and DO), and β-lactam inhibitors (TZP and SAM) was 95.2%, 95.2%, 90%, 57.1%, and 100% among MDR A. baumannii isolates and 71.4%, 55%, 69%, 61.9%, and 59.5% among K. pneumoniae isolates, respectively. In addition, resistance to SXT increased against two Gram-negative bacteria.
Although all five S. aureus isolates in our study (100%) were susceptible to VAN (MIC ≤ 2 µg/ml), AN, LZD, RA, and DO, all were resistant to P. Of the five isolates of S. aureus, three (60.0%) were resistant to FOX and OXA (MIC ≥ 4 µg/ml), suggesting that they were MRSA and were eventually confirmed by amplification of the mec-A gene. The MRSA isolates (n = 3/5) were resistant to at least three (16.6%) and a maximum of 11 (61.1%) antibiotics, while MSSA isolates (2/5) were resistant to at least one (5.6%, 1/18) and a maximum of six (33.3%, 6/18) antibiotics. Resistance proportions of 66.6% to GM and 33.3% to TE, AZM, LVX, CP, OFL, CD, and SXT were obtained among MRSA isolates. Meanwhile, the MSSA isolates showed a resistance proportion of 50% (1/2) to AZM, LVX, CP, OFL, and C. Two (66.6%, 2/3) patients infected with MRSA isolates had critical symptoms and died.
The MDR and ESBL-producing isolates
The MDR proportion was 100% and 71.4% among A. baumannii and K. pneumoniae isolates, respectively. Interestingly, 82.71% (18/21) of MDR A. baumannii isolates and 86.2% (25/29) of MDR K. pneumoniae isolates led to 100% mortality in patients with HAI. The ESBL-producing K. pneumoniae isolates were 24% (10/42). Additionally, all ESBL-producing K. pneumoniae isolates were MDR.
This study detected a high HAI proportion of 83.2% among ICU-admitted COVID-19 patients. Most patients with HAI were critically ill. Additionally, we were in lacked access to microbiological data during the first admission. Thus, some might have been admitted with coinfections that were classified as superinfections. Furthermore, the length of stay in our study was relatively long (median = 18 days). In numerous other studies, the length of hospital stay was prolonged for patients with various superinfections and was accompanied by increased mortality [27,28,29].
Approximately 92% of patients with HAI were intubated, and of those, 80% had at least one underlying disease, which led to a mortality rate of 90%. Similarly, in some investigations, hypertension, diabetes, and coronary heart disease were found to be most common among patients suffering from comorbidities and HAI [29, 30]. In addition, 84% of patients with HAI received immunosuppressive therapies more frequently, primarily because of multiple organ damage due to hyperinflammatory immune responses in critically ill COVID-19 patients . These patients may have been infected by various bacterial, fungal, and viral superinfections that exacerbated the severity of their symptoms . These factors may be justifiable for our high incidence of HAI.
In our study, antibiotic administration was more frequent among patients with HAI than those without it, which had a significant association with mortality. In addition, HAI-causing A. baumannii and K. pneumoniae isolates exhibited high resistance to many antibiotics. Similarly, in the Floridia et al. study, the most frequent pathogens causing infections were Enterobacterales, mainly K. pneumoniae, followed by A. baumannii and S. aureus isolates. In addition, a higher rate of antibiotic resistance was exhibited by these organisms during the first and second waves of COVID-19 than during a prior pandemic . In one study by Kariyawasam et al., MDR isolates of K pneumoniae (n = 274), A. baumannii (n = 218), P. aeruginosa (n = 203), and MRSA (n = 132) were more frequent than the others . Similar to the Mdrzycka et al. study, the highest prevalence of CRKP was up to 53%, mainly occurring in elderly and male SARS-CoV-2-infected individuals .
In agreement with our findings, Tadesse et al. stated the presence of carbapenemase and many antimicrobial resistance (AMR)-encoding genes, such as ESBLs, on mobile genetic elements to facilitate the accumulation and spread of AMR genes. This emphasizes why most carbapenem-resistant Gram-negative bacteria, such as CRKP and CRAB isolates, are primarily resistant to other antibiotic classes, such as aminoglycosides, fluoroquinolones, and SXT . In our study, all ESBL-producing K. pneumoniae isolates were MDR. Mazzariol et al. reported a proportion of 61.5% (8/13) for CRKP and 23.5% (3/13) for ESBL-producing K. pneumoniae, which was comparable to our findings .
In one study by Pourajam et al., in contrast to our results, the superinfection-causing K. pneumoniae isolates showed the highest antibiotic resistance (97.9%) to CAZ, CRO, CPM, SAM, PTZ, and CP. They had increased resistance to LVX (95.8%), MER (95.8%), GM (93.6%), and AN (80.9%). While similar to our results, the resistance rate of A. baumannii isolates to COL was 10.6%, the full resistance rate to CAZ, CRO, CPM, TZP, MER, and CP, and 97.2%, 94.3%, and 60% resistance to GM, AN, and SAM were recorded, but no resistance rate to COL was found . In the Bahce et al. study, similar to our study, the A. baumannii isolates showed full resistance to MEM, TPZ, CIP, LEV, and IMP. They also revealed an increased resistance to AK (63.8%), GN (95.7%), and TM (97.7%). In contrast to our results, the resistance rate of A. baumannii isolates to SXT was low (46.8%). Additionally, the resistance rate of K. pneumoniae isolates to GN (44.4%), COL (42.9%), MEM (71.4%), TZP (77.8%), CIP (100%), and FEP (77.8%) was reported to be relatively high compared to our results .
In a cross-sectional study by Qodrati et al., a low rate of MRSA (37.5%, 216/576) was obtained from clinical specimens one year before the pandemic. The antibiotic resistance against MRSA isolates was greater than that against MSSA isolates and was similar against GM, TE, LVX, SXT, CP, CD, and SXT. Given the full susceptibility of VAN and LZD to all S. aureus isolates, the authors considered these drugs as the first treatment priority against MRSA-causing infections . Despite the frequent use of VAN in hospitalized COVID-19 patients, low numbers of MSSA (2/5) and MRSA isolates (3/5) were observed in our study. Additionally, Falcon  and Punjabi et al.  reported a low incidence of S. aureus and culture-based MRSA (5.7%) isolates from respiratory COVID-19 patient samples. In contrast to Qodrati et al., Punjabi et al. believed that continuous VAN treatment might not be more beneficial for superinfections caused by MRSA isolates .
Although the prescription of antimicrobial treatment in infections of the ongoing SARS-CoV-2 pandemic is unavoidable, there has been no documented sign of its efficacy in lowering mortality . Some reports have shown widespread antibiotic use challenges in body metabolism and could prevent the body from generating proper antibodies against SARS-CoV-2 infections. This could be one of the main reasons for the high mortality in the ongoing COVID-19 pandemic . The presence of MDR Gram-negative isolates in our investigation might justify the elevated HAI rate and consequent high incidence of superinfected patient mortality.
The current study had certain limitations. First, all analyses were conducted solely on patients with SARS-CoV-2, and the frequency of HAI in hospitalized patients without COVID-19 was not evaluated. Second, the diagnosis of HAI was based on the microbial culture results. We had some constraints in performing the molecular-based methods to detect additional atypical infectious agents. Last, we did not have access to the antibiotic administration data and preliminary microbiological culture results before hospitalization to evaluate the efficacy of antimicrobial regimens on the type and proportion of HAIs among SARS-CoV-2 patients.
The present study reveals a substantial prevalence of bacterial and fungal superinfections (83.2%) among SARS-CoV-2 ICU-admitted patients, particularly MDR CRKP and CRAB isolates. The superinfections were significantly associated with increased mechanical ventilation (92.2%) and high mortality (90%). To mitigate antimicrobial resistance, patients were treated according to their precise microbial resistance profile using antibiotic regimens based on the clinicians’ recommendations and hospital procedures. Additionally, implementing contact isolation and infection control programs is necessary if needed.
Availability of data and materials
The datasets generated and analyzed in the current study are available from the corresponding author upon reasonable request.
Severe acute respiratory syndrome coronavirus 2
Intensive care unit
Reverse transcription-quantitative polymerase chain reaction
Clinical and Laboratory Standards Institute
Minimum inhibitory concentrations
Combination disk diffusion
American Type Culture Collection
Extended spectrum beta-lactamases
Multi drug resistance
Methicillin-resistant Staphylococcus aureus
Methicillin-sensitive Staphylococcus aureus
Andersen KG, Rambaut A, Lipkin WI, Holmes EC, Garry RF. The proximal origin of SARS-CoV-2. Nat Med. 2020;26(4):450–2. https://doi.org/10.1038/s41591-020-0820-9.
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–13. https://doi.org/10.1016/S0140-6736(20)30211-7.
WHO. Cartographer coronavirus disease (COVID-19) dashboard. Geneva: World Health Organization; 2020.
Sanyaolu A, Okorie C, Marinkovic A, Patidar R, Younis K, Desai P, et al. Comorbidity and its impact on patients with COVID-19. SN Compr Clin Med. 2020. https://doi.org/10.1007/s42399-020-00363-4.
Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91–5. https://doi.org/10.1016/j.ijid.2020.03.017.
Ku CL, Chen IT, Lai MZ. Infection-induced inflammation from specific inborn errors of immunity to COVID-19. FEBS J. 2021;288(17):5021–41. https://doi.org/10.1111/febs.15961.
Clancy CJ, Schwartz IS, Kula B, Nguyen MH. Bacterial superinfections among persons with coronavirus disease 2019: a comprehensive review of data from postmortem studies. Open Forum Infect Dis. 2021. https://doi.org/10.1093/ofid/ofab065.
Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36(5):309–32. https://doi.org/10.1016/j.ajic.2008.03.002.
Ma YX, Wang CY, Li YY, Li J, Wan QQ, Chen JH, et al. Considerations and caveats in combating ESKAPE pathogens against nosocomial infections. Adv Sci. 2020;7(1):1901872. https://doi.org/10.1002/advs.201901872.
Dumitru I, Dumitrascu M, Vlad N, Cernat R, Ilie-Serban C, Hangan A, et al. Carbapenem-resistant Klebsiella pneumoniae associated with COVID-19. Antibiotic. 2021;10(5):561. https://doi.org/10.3390/antibiotics10050561.
Rangel K, Chagas TPG, De-Simone SG. Acinetobacter baumannii infections in times of COVID-19 pandemic. Pathogen. 2021;10(8):1006. https://doi.org/10.3390/pathogens10081006.
O’Toole RF. The interface between COVID-19 and bacterial healthcare-associated infections. Clin Microbiol Infect. 2021;27(12):1772–6. https://doi.org/10.1016/j.cmi.2021.06.001.
Punjabi CD, Madaline T, Gendlina I, Chen V, Nori P, Pirofski L-A. Prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in respiratory cultures and diagnostic performance of the MRSA nasal polymerase chain reaction (PCR) in patients hospitalized with coronavirus disease 2019 (COVID-19) pneumonia. Infect Cont Hosp Epidemol. 2021;42(9):1158. https://doi.org/10.1017/ice.2020.440.
Duployez C, Le Guern R, Tinez C, Lejeune A-L, Robriquet L, Six S, et al. Panton-valentine leukocidin-secreting Staphylococcus aureus pneumonia complicating COVID-19. Emerg Infect Dis. 2020;26(8):1939–41. https://doi.org/10.3201/eid2608.201413.
Russell CD, Fairfield CJ, Drake TM, Turtle L, Seaton RA, Wootton DG, et al. Co-infections, secondary infections, and antimicrobial use in patients hospitalized with COVID-19 during the first pandemic wave from the ISARIC WHO CCP-UK study: a multicentre, prospective cohort study. Lancet Microbe. 2021;2(8):e354–65. https://doi.org/10.1016/S2666-5247(21)00090-2.
Putot A, Bouiller K, Laborde C, Gilis M, Févre A, Hacquin A, et al. Association between early antibiotic therapy and in-hospital mortality among older patients with SARS-CoV-2 pneumonia. J Gerontol Ser. 2021. https://doi.org/10.1093/gerona/glab209.
Daniel WW. Biostatistics; a foundation for analysis in the health sciences. New York: Wiley; 1974.
Dickson RP, Erb-Downward JR, Prescott HC, Martinez FJ, Curtis JL, Lama VN, et al. Analysis of culture-dependent versus culture-independent techniques for identification of bacteria in clinically obtained bronchoalveolar lavage fluid. J Clin Microbiol. 2014;52(10):3605–13. https://doi.org/10.1128/JCM.01028-14.
Loens K, Van Heirstraeten L, Malhotra-Kumar S, Goossens H, Ieven M. Optimal sampling sites and methods for detection of pathogens possibly causing community-acquired lower respiratory tract infections. J Clin Microbiol. 2009;47(1):21–31. https://doi.org/10.1128/JCM.02037-08.
Haeili M, Kafshdouz M, Feizabadi MM. Molecular mechanisms of colistin resistance among pandrug-resistant isolates of Acinetobacter baumannii with high case-fatality rate in intensive care unit patients. MDR. 2018;24(9):1271–6. https://doi.org/10.1089/mdr.2017.0397.
Lotfali E, Erami M, Fattahi M, Nemati H, Ghasemi Z, Mahdavi E. Analysis of molecular resistance to azole and echinocandin in Candida species in patients with vulvovaginal candidiasis. Curr Med Mycol. 2022;8(2):1–7. https://doi.org/10.18502/CMM.8.2.10326.
CALSI. Performance standards for antimicrobial susceptibility testing CLSI supplement M100. Wayne: Clinical and Laboratory Standards Institute; 2021.
Ngassam Tchamba C, Duprez J-N, Lucas P, Blanchard Y, Boyen F, Haesebrouck F, et al. Comparison of the staphylococcal chromosome cassette (SCC) mec in methicillin-resistant Staphylococcus aureus (MRSA) and non-aureus staphylococci (MRNAS) from animals and humans. Antibiotic. 2021;10(3):256. https://doi.org/10.3390/antibiotics10030256.
Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas M, Giske C, et al. Multidrug-resistant, extensively drug-resistant and pandrug resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18(3):268–81. https://doi.org/10.1111/j.1469-0691.2011.03570.
Silago V, Kovacs D, Samson H, Seni J, Matthews L, Oravcová K, et al. Existence of multiple ESBL genes among phenotypically confirmed ESBL producing Klebsiella pneumoniae and Escherichia coli concurrently isolated from clinical, colonization, and contamination samples from neonatal units at bugando medical center, Mwanza. Antibiotics. 2021. https://doi.org/10.3390/antibiotics10050476.
Ugwu MC, Shariff M, Nnajide CM, Beri K, Okezie UM, Iroha IR, et al. Phenotypic and molecular characterization of β-Lactamases among enterobacterial uropathogens in Southeastern Nigeria. Can J Infect Dis Med. 2020;2020:1–9. https://doi.org/10.1155/2020/5843904.
Canton R, Gijon D, Ruiz-Garbajosa P. Antimicrobial resistance in ICUs: an update in the light of the COVID-19 pandemic. Curr Opin Crit Care. 2020;26(5):433–41. https://doi.org/10.1097/MCC.0000000000000755.
Garcia-Vidal C, Sanjuan G, Moreno-García E, Puerta-Alcalde P, Garcia-Pouton N, Chumbita M, et al. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study. Clin Microbiol Infect. 2021;27(1):83–8. https://doi.org/10.1016/j.cmi.2020.07.041.
Pickens CO, Gao CA, Cuttica MJ, Smith SB, Pesce LL, Grant RA, et al. Bacterial superinfection pneumonia in patients mechanically ventilated for COVID-19 pneumonia. Am J Resp Crit Care. 2021;204(8):921–32. https://doi.org/10.1164/rccm.202106-1354OC.
He S, Liu W, Jiang M, Huang P, Xiang Z, Deng D, et al. Clinical characteristics of COVID-19 patients with clinically diagnosed bacterial co-infection: a multi-center study. PLoS ONE. 2021. https://doi.org/10.1371/journal.pone.0249668.
Mazumder P, Kalamdhad A, Chaminda GT, Kumar M. Coalescence of co-infection and antimicrobial resistance with SARS-CoV-2 infection: the blues of post-COVID-19 world. CSCEE. 2021. https://doi.org/10.1016/j.cscee.2021.100093.
Cox MJ, Loman N, Bogaert D, O’Grady J. Co-infections: potentially lethal and unexplored in COVID-19. Lancet Microbe. 2020. https://doi.org/10.1016/S2666-5247(20)30009-4.
Floridia M, Giuliano M, Monaco M, Palmieri L, Lo Noce C, Palamara AT, et al. Microbiologically confirmed infections and antibiotic resistance in a national surveillance study of hospitalized patients who died with COVID-19, Italy 2020–2021. Antimicrob Resist Infect Control. 2022. https://doi.org/10.1186/s13756-022-01113-y.
Kariyawasam RM, Julien DA, Jelinski DC, Larose SL, Rennert-May E, Conly JM, et al. Antimicrobial resistance (AMR) in COVID-19 patients: a systematic review and meta-analysis (November 2019–June 2021). Antimicrob Resist Infect Control. 2022. https://doi.org/10.1186/s13756-022-01085-z.
Medrzycka-Dabrowska W, Lange S, Zorena K, Dabrowski S, Ozga D, Tomaszek L. Carbapenem-resistant Klebsiella pneumoniae infections in ICU COVID-19 patients—a scoping review. Jpn J Clin Med. 2021. https://doi.org/10.3390/jcm10102067.
Tadesse S, Mulu W, Genet C, Kibret M, Belete MA. Emergence of high prevalence of extended-spectrum beta-lactamase and carbapenemase-producing enterobacteriaceae species among patients in Northwestern Ethiopia region. Biomed Res Int. 2022;2022:1–9. https://doi.org/10.1155/2022/5727638.
Mazzariol A, Benini A, Unali I, Nocini R, Smania M, Bertoncelli A, et al. Dynamics of SARS-CoV2 infection and multi-drug resistant bacteria superinfection in patients with assisted mechanical ventilation. Front Cell Infect Microbiol. 2021. https://doi.org/10.3389/fcimb.2021.683409.
Pourajam S, Kalantari E, Talebzadeh H, Mellali H, Sami R, Soltaninejad F, et al. Secondary bacterial infection and clinical characteristics in patients with COVID-19 admitted to two intensive care units of an academic hospital in Iran during the first wave of the pandemic. Front Cell Infect Microbiol. 2022. https://doi.org/10.3389/fcimb.2022.784130.
Bahçe YG, Acer Ö, Özüdoğru O. Evaluation of bacterial agents isolated from endotracheal aspirate cultures of Covid-19 general intensive care patients and their antibiotic resistance profiles compared to pre-pandemic conditions. Microb Pathog. 2022;164:105409. https://doi.org/10.1016/j.micpath.2022.105409.
Qodrati M, SeyedAlinaghi S, Dehghan Manshadi SA, Abdollahi A, Dadras O. Antimicrobial susceptibility testing of Staphylococcus aureus isolates from patients at a tertiary hospital in Tehran, Iran, 2018–2019. Eur J Med Res. 2022;27(1):1–8. https://doi.org/10.1186/s40001-022-00778-w.
Falcone M, Tiseo G, Giordano C, Leonildi A, Menichini M, Vecchione A, et al. Predictors of hospital-acquired bacterial and fungal superinfections in COVID-19: a prospective observational study. J Antimicrob Chemother. 2021;76(4):1078–84. https://doi.org/10.1093/jac/dkaa530.
Mirzaei R, Goodarzi P, Asadi M, Soltani A, Aljanabi HAA, Jeda AS, et al. Bacterial co-infections with SARS-CoV-2. IUBMB Life. 2020;72(10):2097–111. https://doi.org/10.1002/iub.2356.
Huttner B, Catho G, Pano-Pardo J, Pulcini C, Schouten J. COVID-19: don’t neglect antimicrobial stewardship principles! Clin Microbiol Infec. 2020;26(7):808. https://doi.org/10.1016/j.cmi.2020.04.024.
The authors thank the personnel of the infectious unit of Imam Khomeini Hospital for their assistance in collecting respiratory samples.
This work was supported by Tehran University of Medical Sciences [Grant No: 50900].
Ethics approval and consent to participate
The current study was reviewed and approved by the institutional ethics committee of Tehran University of Medical Sciences with an approved ID: IR.TUMS.MEDICINE.REC.1399.1092 on February 14, 2021. Informed consent was obtained from patients or the relatives of unconscious patients.
All authors declare no competing interests related to this study.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Mobarak-Qamsari, M., Jenaghi, B., Sahebi, L. et al. Evaluation of Acinetobacter baumannii, Klebsiella pneumoniae, and Staphylococcus aureus respiratory tract superinfections among patients with COVID-19 at a tertiary-care hospital in Tehran, Iran. Eur J Med Res 28, 314 (2023). https://doi.org/10.1186/s40001-023-01303-3