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

Abstract

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

1. INTRODUCTION

Coronaviruses have long been recognized as major human pathogens, causing both upper respiratory infections in adults and severe respiratory infections in both adults and children. ¹ Severe pneumonia of viral origin has been related to coronavirus outbreaks, most notably severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and more recently, a novel coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). SARS‐CoV‐2 was initially known as 2019‐nCoV, which emerged in Wuhan, China, causing severe coronavirus disease 2019 (COVID‐19) pneumonia. ²

The majority of patients experience minimal symptoms and have a favorable prognosis. However, some COVID‐19 patients have died after developing severe pneumonia, pulmonary edema, acute respiratory distress syndrome (ARDS), or multiple organ failure. ³ Around 410 836 088 confirmed cases of COVID‐19 and 5 829 532 deaths have been reported worldwide until February 13, 2022. ⁴ According to the World Health Organization COVID‐19 daily report dashboard (https://covid19.who.int/), North America was the most affected continent, followed by Europe. ⁴ Also, Iran has been through this viral issue and has effectively slowed its spread to some extent. However, there have already been more than 6 million confirmed cases and 133 570 deaths in Iran. ⁵

As shown in a Lancet journal research, ARDS is the leading cause of mortality from COVID‐19. ARDS is a frequent immunopathological manifestation of SARS‐CoV‐2 infections. This occurrence, as well as the use of corticosteroids, serves as a basis for superinfection by certain microorganisms, including bacteria in the lungs. This may exacerbate clinical symptoms and therapeutic complications, and also increase mortality. ⁶

Infections that arise after previous infection, particularly those caused by antimicrobial resistant microorganisms, are called superinfections as defined by the Centers for Disease Control and Prevention. ¹ , ⁷ It has long been known that viral respiratory infections predispose patients to bacterial infections and that these superinfections are worse than either infection alone. ⁷ , ⁸ For this reason, an infection control program and sensible antibiotic administration should be implemented as soon as possible for COVID‐19 patients. ⁹

There is a high rate of antibiotic use (approximately 74.5%) among COVID‐19 patients admitted to the intensive care units (ICUs). Patients with COVID‐19 who are on an invasive mechanical ventilator for an extended period of time (approximately 9 days) are substantially more prone to acquire ventilator‐related diseases and multidrug‐resistant (MDR) bacterial infections. As a result, early diagnosis of concomitant infections is critical. Early detection of the causal agents of comorbid infections and assessment of antibiotic resistance are critical for the management, control, and treatment of patients with severe COVID‐19 infections and save patients' lives. ¹⁰

Previous reports, including case series, ¹¹ , ¹² cohort and cross‐sectional studies, ¹³ , ¹⁴ and meta‐analyses, ¹⁵ , ¹⁶ have provided variable findings on co‐infection and superinfection with respiratory pathogens in COVID‐19 patients from different regions. The causative pathogens of respiratory co‐infections and superinfections can be numerous, either common or uncommon, such as bacteria, viruses, and fungi. Bacteria have been considered as one of the most frequently isolated microorganisms. ⁵ The most common co‐infecting bacterial species include Klebsiella pneuminiae, Staphylococcus aureus, Haemophilus influenzae, Mycoplasma pneumoniae, Pseudomonas aeruginosa, and Streptococcus pneumoniae. ¹⁷

Since no study of bacterial superinfections has been performed in COVID‐19 patients in the southwest region of Iran, this research aimed to identify the prevalence of bacterial etiological agents associated with COVID‐19 superinfection and their antibiotic resistance pattern in patients admitted to ICU of a referral hospital in Ahvaz city, southwest Iran. By the results of this study, physicians and medical staff can gain a better understanding of the control and treatment of bacterial superinfections in patients with COVID‐19.

2. METHODS

2.1. Ethical statement

This study was approved by the Ethics Committee of the Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran (IR.AJUMS.REC.1399.408) and was carried out in line with the Declaration of Helsinki principles. This study did not involve the collection of endotracheal tube (ETT) secretion samples. ETT secretion samples were collected as routine clinical care for COVID‐19 patients and remnant of samples were used. As a result, written informed consent was waived by the Ethics Committee of the Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

2.2. Study design, area, and patients

This cross‐sectional study (from 20th January 2021 to 20th April 2021) included intubated patients with laboratory‐confirmed severe COVID‐19 pneumonia who were admitted to the ICU of Razi Hospital, Ahvaz COVID‐19 referral hospital at southwestern Iran. Razi Hospital is one of the main teaching hospitals affiliated with Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. Ahvaz Razi Hospital was commissioned in 1945 with an area of 23 111 square meters and an infrastructure of 2294 square meters. This hospital with 215 active beds is located at Ahvaz city of Khuzestan province, which is the second‐largest city in Iran after Tehran. Patients from neighboring provinces in the southwestern region of Iran including Hormozgan, Kohgiluyeh and Boyer‐Ahmad, Ilam, and Bushehr regularly seek care at this hospital. Razi Hospital in southwest Iran was designated as one of the referral hospitals for the diagnosis and care of COVID‐19 patients at the beginning of the COVID‐19 pandemic. COVID‐19 patients in southwest Iran.

The detection of bacterial pathogens in COVID‐19 patients after 48 h of hospital admission was considered as superinfection. ¹³ Before hospital admission of each patient, SARS‐CoV‐2 was detected and confirmed by a SARS‐CoV‐2 reverse transcription‐quantitative polymerase chain reaction (RT‐qPCR) assay using nasopharyngeal swabs. Also, specialists from the hospital's radiology department evaluated the chest computer tomography scans and/or radiographs for signs of pneumonia. A COVID‐19 infection confirmed with RT‐qPCR, hospital admission, intubation, and mechanical ventilation in ICUs for more than 48 h, were inclusion criteria. Exclusion criteria included patients with a clinical presentation consistent with COVID‐19 but who had a negative RT‐qPCR test. All data were collected from the hospital electronic medical record system as follows: demographic characteristics, hospitalization duration, primary symptoms, comorbidities, regular laboratory findings (hematology, coagulation, serology and biochemistry), and venous blood gasses.

2.3. Sample size and sampling techniques

In this study, purposive sampling technique was used and any COVID‐19 patient who met the inclusion criteria was selected for further investigation. The following formula: z² × p (1‐p)/e² [z = z‐score = 1.96, e = margin of error = 5%, and p = standard of deviation] was used to estimate the optimum sample size with confidence interval level of 95%. Based on the review and meta‐analysis by Langford et al., ¹⁵ the bacterial co‐infection was recognized in 3.5% of patients (95% confidence interval: 0.4%–6.7%). Hence, we select the p = standard of deviation as 5.0% and accordingly the estimated sample size was calculated as 72. We included 77 patients in this study.

2.4. Specimens' collection

The respiratory samples were collected from ETT secretions of critically ill COVID‐19 patients during routine clinical tests by an infectious disease specialist resident in the hospital. A part of the ETTs (about 10 cm) was cut and immediately immersed in the sterile falcon tubes containing NaCl 0.85%. The falcon tubes were placed on dry ice in a cool box and transferred immediately (less than 30 min) to the SARS‐CoV‐2 laboratory for further evaluation. In the laboratory, the falcon tubes were vortexed for three times (30 s) and the suspensions were divided into 2 parts. ¹⁸ One part was immediately prepared for culture to detect the routine bacteria and another part was stored at −70°C for PCR detection of M. pneumoniae and H. influenzae. ^19,20

2.5. Isolation and identification of bacteria

Cultivation process was done under laminar hood class II type A2 (Yekta Tajhiz). The ETT secretion suspensions from previous stage were centrifuged at 2500 rpm for 15 min before culture. The supernatant was then discarded and the remaining sediment was used for culture. Finally, 0.1 ml of the samples were inoculated into routine microbiological media, including sheep blood agar, chocolate agar, MacConkey agar, and Eosin Methylene Blue agar (Merck). All inoculated plates were incubated at 37°C for 24–48 h. Then, the pure colonies were used for bacterial identification. ¹⁸ , ²¹ The isolates were identified by standard bacteriological tests including Gram staining, mannitol salt agar, Dnase, catalase, oxidase and analytical profile index multitest strip (bioMerieux Inc.) according to the manufacturer's protocol. ²¹ , ²² The confirmed isolates were stocked in trypticase soy broth (Merck) with 20% (vol/vol) glycerol and kept at deep freezer.

2.6. Antibiotic susceptibility testing (AST)

Antibiotic susceptibility of isolates was tested using the disc diffusion method in accordance with the Clinical and Laboratory Standards Institute (CLSI) recommendations. ²³ For Gram‐positive bacteria the following antibiotics were used: erythromycin (15 µg), teicoplanin (30 µg), cefoxitin (30 µg), gentamicin (30 µg), ciprofloxacin (5 µg), trimethoprim/sulfamethoxazole (1.25/23.75 µg), clindamycin (2 µg), and linezolid (30 µg). For Gram‐negative bacteria (GNB) the used antibiotics were as follows: imipenem (30 µg), meropenem (10 µg), ceftazidime (30 µg), cefepime (30 µg), amikacin (30 µg), gentamicin (30 µg), cefotaxime (30 µg), piperacillin (100 µg), cefoxitin (30 µg), ciprofloxacin (5 µg), trimethoprim/sulfamethoxazole (1.25/23.75 µg), and erythromycin (15 µg). The detection of methicillin‐resistant Staphylococcus aureus (MRSA) was performed using the cefoxitin (30 µg) disc according to the CLSI criteria. ²³ All antibiotics were purchased from Mast Group Ltd. Few bacterial colonies were dissolved in sterile physiological saline to obtain the 0.5 standard McFarland turbidity. The suspensions were cultivated on Muller Hinton agar medium (Merck) with antibiotic discs placed at regular intervals and kept overnight at 37°C. The results were read after 24 h using a specific ruler. Bacteria that were resistant to three or more antibiotic classes were considered MDR. ²⁴

2.7. Quality control (QC) for assessing the performance and sterility of the culture media and antibiotic discs

In each working run, the QC for performance of the culture media and antibiotic discs was applied according to the CLSI recommendations and by comparison of the results of AST of standard bacteria with the breakpoints provided in the CLSI guidelines. ²³ For the QC of the sterility of the culture media, the uninoculated plates were incubated overnight at 37°C. The sterility of the laboratory made media was ensured by the lack of any bacterial growth after 24 h. The American Type Culture Collection (ATCC) standard strains including Escherichia coli ATCC® 25922™, Staphylococcus aureus ATCC® 25923™, and Pseudomonas aeruginosa ATCC® 27853™ were used for assessing the performance of the culture media and antibiotic discs.

2.8. Investigation of M. pneumoniae and H. influenzae by PCR

To identify H. influenzae and M. pneumoniae, the genomic DNA was extracted using a commercial kit (Sinaclon) according to the manufacturer's protocol. Then, PCR was performed using previously described specific primers for each isolate (Table 1). ¹⁹ , ²⁰ The final PCR mixture (25 µl) consisted of 1x PCR buffer, 1.5 mM MgCl₂, 0.75 μM deoxynucleotide triphosphate (dNTPs), 1 µM of each primer, 2.5 units of HotStar Taq DNA polymerase and 10 ng of extracted DNA. PCR amplification was made using a thermocycler (Eppendorf, Germany) by incubating the samples for 5 min at 94°C followed by 35 cycles of 30 s at 94°C, 30 s at 55°C, 30 s at 72°C, and a final extension at 72°C for 5 min. The amplicons were observed by electrophoresis of 5 µl of PCR products onto a 1% agarose gel containing safe stain (Sinaclon). The genomic DNA from M. pneumoniae ATCC® 29342™ and H. influenzae ATCC® 33391™ were used as the positive controls. Also, DNA/RNA free water was used as a negative control.

Table 1.

Specific primers for Mycoplasma pneumoniae and Haemophilus influenzae used in this study

Genes	Respiratory pathogen	Size (bp)	Nucleotide Sequences (5′–3′)	Reference
P1	Mycoplasma pneumoniae	345	F: 5‐AGGCTCAGGTCAATCTGGCGTGGA‐3	Cifuentes et al. 18
			R: 5‐GGATCAAACAGATCGGTGACTGGGT‐3
ompP6	Haemophilus influenzae	459	F: 5‐ATGAACAAATTTGTTAAATCA‐3	Kumar et al. 19
			R: 5‐TGCGATGTTGTATTCAGGTGTA‐3

2.9. Statistics analysis

To analyze the data, the twentieth version of SPSS software (IBM Corporation) was used. Descriptive statistical tests were used as follows: categorical variables were analyzed with frequency and percentages while continuous variables were analyzed with means and standard deviations (SD). Also, the Fisher's exact test was performed to analyze any statistically significant correlation (p < 0.05) between variables.

3. RESULTS

3.1. Characteristics of COVID‐19 patients

During the study period, 77 COVID‐19 patients (35 females and 42 males) were admitted to the ICU of Razi Hospital for their severe pneumonia after 13 (1–25) days of symptoms onset (Figure 1). The mean age of the patients was 61 ± 14.44 (32–96 years). The hematology, coagulation, serology, and biochemistry parameters of COVID‐19 patients were summarized in Table 2. They mainly had ischemic heart disease (IHD) (n = 33; 42.9%) and diabetes mellitus (DM) (n = 29; 37.7%), with oxygen saturation (SpO₂) of 15%–99% (Table 2).

Figure 1.

Flow chart of the study COVID‐19 patients. COVID‐19, coronavirus disease 2019; ICU, intensive care unit, PCR, polymerase chain reaction

Table 2.

Demographic and clinical properties of COVID‐19 patients admitted to Ahvaz Razi referral hospital

	Total patients	Colony growth
		Positive	Negative
Total	77	49 (63.6%)	28 (36.6%)
Age; M ± SD	64 ± 14.44	65.82 ± 14.65	60.96 ± 13.77
Gender
Male	42 (54.5)	26 (61.9%)	16 (38.1%)
Female	35 (45.5)	23 (65.7%)	12 (34.3%)
Haemophilus influenzae PCR
Positive	8 (10.4%)	5 (62.5%)	3 (37.5%)
Negative	69 (89.6%)	44 (63.8%)	25 (36.2%)
Mycoplasma pneumoniae PCR
Positive	9 (11.7%)	4 (44.4%)	5 (55.6%)
Negative	68 (88.3%)	45 (66.2%)	23 (33.8%)
Median duration of symptom (days); range	7 (4–7)	7 (4–7)	5.5 (4–7)
Underlying disease
IHD	12 (15.6%)	6 (50.0%)	6 (50.0%)
HTN	11 (14.3%)	8 (72.7%)	3 (27.3%)
DM, IHD	26 (5.2%)	19 (73.1%)	7 (26.9%)
DM, IHD, HTN	3 (3.9%)	2 (66.7%)	1 (33.3%)
IHD, HTN	1 (1.3%)	0 (0.0%)	1 (100.0%)
Other disease	24 (31.2%)	14 (58.3%)	10 (41.7%)
Laboratory specifications, day 1, median (range)
Hematology parameters
ESR	67.0 (5–129)	68.5 (8–129)	53.0 (5–101)
Platelet	240.5 (86–395)	240.5 (86–395)	248.5 (102–395)
MCHC	24.3 (13.7–34.9)	31.2 (28.7–33.7)	24.3 (13.7–34.9)
MCH	26.4 (18.8–34.1)	26.2 (18.8–33.7)	29.1 (24.2–34.1)
MCV	81.6 (65.7–97.6)	81.2 (65.7–96.7)	82.5 (67.4–97.6)
HCT	38.9 (23.4–54.4)	38.9 (23.4–54.4)	36.0 (23.7–48.3)
HB	11.9 (6.8–17.1)	12.0 (6.9–17.1)	10.8 (6.8–14.9)
RBC	4.5 (2.2–6.8)	4.2 (2.2–6.3)	5.3 (3.8–6.8)
WBC	14.1 (2.4–25.8)	13.9 (2.4–25.5)	14.7 (3.6–25.8)
Coagulation parameters
PT	21.0 (12–30)	21.0 (12–30)	15.0 (12–18)
PTT	72.5 (25–120)	72.5 (25–120)	73.5 (27–120)
INR	1.4 (1–1.9)	1.4 (1–1.9)	1.4 (1–1.9)
Serology parameter	1+ to 3+	1+ to 3+	1+ to 3+
CRP, mg/L
Biochemistry parameter (mmol/L)
LDH	971.5 (43–1900)	1112.0 (324–1900)	683.5 (43–1324)
BilT	2.3 (0.5–4.1)	1.3 (0.5–2.2)	2.3 (0.6–4.1)
BilD	0.3 (0.1–0.5)	0.3 (0.1–0.5)	0.4 (0.1–0.7)
ALKP	267.0 (90–444)	269.0 (94–444)	189.5 (90–289)
ALT	50.5 (10–91)	22.5 (10–35)	51.0 (11–91)
AST	143.5 (17–270)	143.5 (17–270)	98.5 (19–178)
K	4.8 (3.3–6.3)	4.8 (3.3–6.3)	4.5 (3.3–5.8)
Na	135.0 (123–147)	137.0 (127–147)	133.0 (123–143)
Cr	3.5 (0.2–6.9)	3.8 (0.8–6.9)	1.5 (0.2–2.9)
BUN	62 (5–119)	63.5 (8–119)	27.5 (5–50)
BS	493.0 (61–925)	493.0 (61–925)	376.5 (83–670)
Venous blood gas parameter
NaHCO3	18.1 (4.2–32)	18.1 (4.2–32)	21.4 (13–29.9)
PO2	43.1 (12.2–309)	43.1 (12.2–309)	48.9 (19.8–78.1)
PCO2	43.0 (20–66.1)	43.0 (20–66.1)	44.2 (25–63.4)
PH	7–7.49	7–7.49	6.9–7.4

Abbreviations: ALKP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BUN, blood urea nitrogen; DM, diabetes mellitus; IHD, ischemic heart disease; LDH, lactate dehydrogenase; RBC, red blood cell; WBC, white blood cell.

3.2. Prevalence of superinfection

Using culture and PCR methods, 56 (72.7%) of the 77 COVID‐19 patients (mean age of 55 years, 29 male and 27 female) had superinfection with Gram‐positive, GNB, H. influenzae, M. pneumoniae or several pathogens at the same time. Of these, 42 (75.0%) had underlying diseases such as DM, IHD, hypertension, chronic kidney disease, end‐stage renal disease, hyperkeratosis lenticularis perstans, coronary artery bypass graft surgery, and rheumatoid arthritis. Mechanical ventilation and intubation were required in all patient (n = 77), and the median duration of symptoms in the coinfected patients was 5.3 days. Of the 56 COVID‐19 patients with bacterial superinfection, 39, 7, and 10 patients showed positive results with culture, PCR, and both methods, respectively. Despite several antibiotic treatments, death was recorded as the outcome in ICU for all 77 (100.0%) COVID‐19 patients.

3.3. Bacterial isolates

In total, 67 bacterial isolates were identified from 49 COVID‐19 patients that showed colony growth in culture method. Of which, 29 isolates (43.2%) were Gram‐positive and 38 isolates (56.7%) were GNB (Table 3). Among the 49 patients who had positive bacterial culture, 11 patients had Gram‐positive bacteria, 20 patients had GNB, and 18 patients had both groups. K. pneumoniae (28.4%, n = 19/67) was the most common isolate followed by S. aureus (22.4%, n = 15/67) (Tables 3 and 4). Using PCR, 10.4% (8/77) and 11.7% (9/77) of ETT secretion specimens had H. influenzae and M. pneumoniae amplicons, respectively (Table 2). The co‐occurrence of both pathogens was not detected in any ETT samples.

Table 3.

Frequency of Gram‐positive and Gram‐negative bacteria and their antibiotic resistance rates among COVID‐19 patients with superinfection

Gram‐positive isolates	ERY	TEC	FOX	GEN	CIP	SXT	CLI	LZD
Staphylococcus aureus, n = 15	15 (100.0)	8 (53.3)	15 (100.0)	8 (53.3)	13 (86.7)	15 (100.0)	15 (100.0)	14 (93.3)
n (%)
Coagulase‐negative staphylococci (CONS), n = 13	13 (100.0)	6 (46.1)	13 (100.0)	10 (76.9)	13 (100)	11 (84.6)	13 (100.0)	13 (100.0)
n (%)
Streptococcus species, n = 1	1 (100.0)	1 (100.0)	1 (100.0)	0 (0.0)	0 (0.0)	1 (100.0)	1 (100.0)	1 (100.0)
n (%)
Total, n = 29	29 (100.0)	15 (51.7)	29 (100.0)	18 (62.1)	26 (89.7)	27 (93.1)	29 (100.0)	29 (100.0)
n (%)

Gram‐negative isolates	CPM	CTX	FOX	GEN	CIP	SXT	CF	IPM	MEM	AN	P
Klebsiella pneumoniae, n = 19	18 (94.7)	17 (89.4)	16 (84.2)	16 (84.2)	16 (84.2)	19 (100.0)	19 (100.0)	16 (84.2)	17 (89.4)	16 (84.2)	17 (89.4)
n (%)
Escherichia coli, n = 6	6 (100.0)	6 (100.0)	6 (100.0)	6 (100.0)	6 (100.0)	6 (100.0)	6 (100.0)	5 (75.0)	5 (75.0)	5 (75.0)	6 (100.0)
n (%)
Enterobacter aerogenes, n = 4	4 (100.0)	4 (100.0)	3 (75.0)	4 (100.0)	4 (100.0)	4 (100.0)	4 (100.0)	3 (75.0)	3 (75.0)	3 (75.0)	4 (100.0)
n (%)
Pseudomonas aeruginosa, n = 4	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)	3 (75.0)
n (%)
Citrobacter freundii, n = 3	3 (100.0)	2 (66.6)	2 (66.6)	2 (66.6)	3 (100.0)	3 (100.0)	3 (100.0)	3 (100.0)	3 (100.0)	3 (100.0)	3 (100.0)
n (%)
Serratia marcescens, n = 1	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)	0 (0.0)
n (%)
Acinetobacter baumannii, n = 1	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)	1 (100.0)
n (%)
Total, n = 38	35 (92.1)	33 (86.8)	31 (81.6)	32 (84.2)	33 (86.8)	36 (94.7)	36 (94.7)	31 (81.6)	32 (84.2)	31 (81.6)	34 (89.5)
n (%)

Abbreviations: AN, Amikacin; CPM, Cefepime; CF, Ceftazidime; CLI, Clindamycin; CTX, Cefotaxime; CIP, Ciprofloxacin; FOX, Cefoxitin; ERY, Erythromycin; GEN, Gentamicin; IPM, Imipenem; LZD, Linezolid; MEM, Meropenem; SXT, Trimethoprim/sulfamethoxazole; P, Piperacillin; TEC, Teicoplanin.

Table 4.

Prevalence rates of multidrug‐resistant (MDR) bacterial isolates among COVID‐19 patients

MDR Gram‐positive isolates	n (%)
Staphylococcus aureus	9/15 (60.0)
Coagulase‐negative staphylococci	9/13 (69.2)
Streptococcus species	0/1 (0.0)
Total MDR Gram‐positive isolates	18/29 (62.1)
MDR Gram‐negative isolates
Klebsiella pneumoniae	14/19 (73.6)
Escherichia coli	5/6 (83.3)
Enterobacter aerogenes	3/4 (75.0)
Pseudomonas aeruginosa	2/4 (50.0)
Citrobacter freundii	3/3 (100.0)
Acinetobacter baumannii	1/1 (100.0)
Serratia marcescens	0/1 (0.0)
Total MDR Gram‐negative isolates	28/38 (73.7)
Total MDR isolates	46/67 (68.7)
Resistant to 3 antibiotics	1/46 (2.2)
Resistant to 4 antibiotics	3/46 (6.5)
Resistant to 5 antibiotics	3/46 (6.5)
Resistant to 6 antibiotics	4/46 (8.7)
Resistant to 7 antibiotics	5/46 (10.9)
Resistant to 8 antibiotics	12/46 (26.1)
Resistant to 9 antibiotics	1/46 (2.2)
Resistant to 10 antibiotics	4/46 (8.7)
Resistant to 11 antibiotics	13/46 (28.3)

3.4. Antibiotic resistance pattern of bacterial isolates

The antibiotic resistance rates of the bacterial isolates were presented in Table 3. The results of AST revealed that all Gram‐positive isolates were resistant to clindamycin, cefoxitin, erythromycin, and linezolid. Also, more than 80.0% of them were resistant to ciprofloxacin and trimethoprim/sulfamethoxazole. However, the Gram‐positive bacteria showed the most susceptibility rates to teicoplanin (51.7%) and gentamicin (62.1%), respectively. The Gram‐negative isolates showed the highest resistance rates (more than 90.0%) against trimethoprim/sulfamethoxazole, ceftazidime, and cefepime. The resistance rates of GNB were more than 80.0% against each of other antibiotics tested. Also, 46 (68.7%) of 67 isolates were MDR and 13 (28.3%) of 46 MDR isolates were simultaneously resistant to 13 antibiotics (Table 4). There was no significant difference in the prevalence rate of MDR Gram‐positive (62.1%) and MDR Gram‐negative (73.7%) bacteria (p = 0.426).

4. DISCUSSION

In ICU settings, superinfections in COVID‐19 patients might represent the greatest obstacle in patient treatment resulting in increased mortality. ²⁵ These infections are more frequent in patients with severe disease who are critically ill and requiring mechanical ventilation in ICU. A link between COVID‐19 and superinfection might be due to severe lung damage induced by viral replication, which leads in a cytokine storm and inflammatory processes. ²⁶

This study indicated the incidence rate of 72.7% for bacterial superinfection in COVID‐19 ICU patients in the southwest region of Iran that was greater than previous reports from Wuhan (China) (15.0%–16.0%), ² , ²⁷ Barcelona (Spain) (7.3%), ²⁸ and Valladolid (Spain) (16.0%). ²⁹ The high rate of superinfection observed in this study may be due to the presence of comorbidities or the use of mechanical ventilator that expose patients to infection. However, the discrepancies of various studies may be due to the bacterial detection methods, sample size, sample type, and studied populations.

In this study, all COVID‐19 patients with superinfection used mechanical ventilator and majority (75.0%) of them had comorbidities. Previously, de Wit et al. ³⁰ revealed that comorbid conditions, older age, and male gender play a major role in SARS and MERS severe symptoms and death. Also, in a previous research by Paparoupa et al. ³¹ from Germany, 72.5% of COVID‐19 patients received invasive ventilation that lead to one or more respiratory superinfections in 45% of patients. They claimed that the widespread use of invasive medical devices in ICU patients increases the risk of nosocomial infections. ³¹

The mechanisms of co‐infection by bacterial pathogens during the COVID‐19 pandemic still remains unclear. To determine the appropriate empirical therapy and patient management plans, it is crucial to determine the bacterial co‐infection profiles. ³² In this study, the GNB were more predominant than Gram‐positive bacteria that was similar to the earlier studies by Cataño‐Correa et al. ¹⁴ from Colombia and García‐Vidal et al. ²⁸ from Spain. Also, the most common coinfecting bacterial species were the K. pneuminiae and S. aureus that was in good parallel with the previous reports from Iran ³³ and India. ³⁴ Moreover, in previous studies by Said et al. ³² from Saudi Arabia, Mazzariol et al. ³⁵ from Italy, and Bazaid et al., ³⁶ from Saudi Arabia, the K. pneuminiae, P. aeruginosa, and Acinetobacter baumannii were found as the most coinfecting bacteria in COVID‐19 patients, respectively.

In this study, S. aureus (22.4%) was the most frequent Gram‐positive bacterium followed by coagulase‐negative staphylococci (CoNS) that was in line with the previous reports from Italy ³⁷ and India. ³⁸ Moreover, all S. aureus isolates in this study were methicillin‐resistant. The spread of S. aureus to the lungs is related to some factors including impaired function of lungs and immunological responses that generate favorable conditions for infection with this bacterium. ³⁵ In previous studies from China ³⁹ and Pakistan, ⁴⁰ MRSA strains were detected in 71.43% and 5.52% of COVID‐19 patients, respectively. These inconsistencies in the frequency rate of bacterial causes of superinfections obtained in different studies can be explained by the differences in epidemiology of studied region, studied samples, and the heterogeneity of studied patients in terms of gender, age and other socio‐demographic variables. However, the dysregulated immune system and gut dysbiosis in the COVID‐19 patients could explain the high frequency of Gram‐negative and Gram‐positive bacteria in this study. ³⁸ In COVID‐19 patients, the inflammatory mediators cause the permeability of the intestinal walls to be disrupted, allowing the gut microbes and their metabolites to leak into the bloodstream and to reach the other organs via circulation. ³⁸

In this study, both Gram‐negative and Gram‐positive isolates showed high resistance rates against majority of tested antibiotics and 68.7% (46/67) of them were MDR. All Gram‐positive isolates were resistant to clindamycin, cefoxitin, erythromycin, linezolid and majority of them (more than 80.0%) were resistant to ciprofloxacin and trimethoprim/sulfamethoxazole. However, they showed the most susceptibility rates to teicoplanin and gentamicin. Similar results were observed by Sahu et al. ³⁸ from India. In contrast to the current study, in a previous report from Indonesia, ⁴¹ S. aureus isolates showed low resistance rates (below 25%) against all antibiotic categories. Moreover, in our research, the majority of Gram‐negative isolates showed high resistance rates (75.0%–100.0%) against all tested antibiotics including carbapenems and third‐generation cephalosporins. In line with our results, Bazaid et al. ³⁶ from Saudi Arabia showed high resistance rates of A. baumannii and K. pneumoniae toward all antibiotics except colistin. Also, in a study from Indonesia, ⁴¹ high resistance to third‐generation cephalosporins (cefotaxime and ceftriaxone) in A. baumannii and E. coli and carbapenem in A. baumannii was found. These various results may be due to the differences in the prescribing antibiotic regime in each region or country, presence or absence of surveillance of antimicrobial consumption, and studied population. The high level of resistance observed against fluoroquinolones and cephalosporins, confirms their inappropriateness in COVID‐19 patients, which should be referred to treatment policy committees after final approval.

Data from the literature revealed that COVID‐19 is related with less efficient infection control practices for a variety of reasons. ⁴² Indeed, health‐care workers (HCWs) have faced significant challenges in adhering to basic procedures and using the same equipment for extended periods of time. Furthermore, HCWs were more concerned with self‐protection than with bacterial cross‐transmission in the wards. Finally, overcrowding in wards, a lack of personnel with sufficient infection control training, and a likely loss in laboratory ability to identify MDR carriage are all potential risk factors for MDR dissemination following the COVID‐19 pandemic. ⁴³ Then, it will be critical to continue monitoring MDR infection rates and applying infection control and antimicrobial stewardship strategies. ⁴⁴

In addition to the common bacteria, H. influenzae and M. pneumoniae were identified in 8 (10.4%) and 9 (11.7%) respiratory secretions of COVID‐19 patients by PCR. In a systematic review by Lansbury et al., ⁴⁵ the prevalence of these co‐infecting pathogens in COVID‐19 patients was found in 17 studies at rate of 42% for M. pneumoniae and 12% for H. influenzae. However, they claimed that all co‐infections of M. pneumoniae were diagnosed serologically by detecting IgM antibody. ⁴⁵ In a previous study from Peru, H. influenzae was detected in 10.8% of hospitalized COVID‐19 patients, while no M. pneumoniae was detected using molecular method. ⁴⁶ Also, a low prevalence rate of 0.9% was reported for M. pneumoniae among COVID‐19 patients in Korea. ⁴⁷ A patient with pneumonia caused by M. pneumoniae may experience similar symptoms to those caused by viral infections. Therefore, molecular detection methods are necessary in these conditions. Zhu et al. ¹⁷ from China (40.1%) and Contou et al. ⁴⁸ from France (22%) reported H. influenzae as one of the most prevalent coinfecting bacterium in SARS‐CoV‐2 patients. The difference in sample type, studied population, and the bacteria detection method examined in various regions may explain the differences in prevalence rates of H. influenzae and M. pneumoniae in the current research with the previously performed studies.

In terms of laboratory tests, patients with coinfection had considerably higher blood urea nitrogen (BUN) levels. This might be a sign of acute renal failure. Furthermore, in a critical setting, lactate dehydrogenase (LDH) is an indicator of organ failure. ⁴⁹ In our investigation, the median level of LDH was higher among patients, and all of them had levels higher than normal. Also, as noted in the Table 2 the median of hepatic enzymes has been raised. The median of aspartate aminotransferase (AST), alkaline phosphatase (ALKP), the alanine aminotransferase (ALT) was 143.5, 267.0, and 50.5 respectively that were above the normal limit. Our study was not primarily focused on evaluating these factors, but the results were consistent with the previous studies from Peru, ⁴⁶ China, ⁴⁹ and Iran. ⁵⁰

During a COVID‐19 infection, bacterial coinfections can disrupt various hematological and biochemical markers, affecting the overall clinical situation. ⁵¹ The current study showed considerable differences in some hematologic, biochemical, and coagulation factors between patients with positive and negative bacterial cultures. Several studies have shown changes in the normal range of various laboratory tests among COVID‐19 patients. ⁴⁹ , ⁵² Prior studies found that individuals with coinfection had substantial increases in leucocyte count, creatinine, hemoglobin, and urea levels when compared to those with negative bacterial cultures. ⁴⁹ , ⁵² However, the clinical significance of these variations need to be further examined. Also, previous studies revealed that the death rate of SARS‐CoV‐2 was similar to that of ARDS. ²⁷ , ⁵³ This was validated in our study, which found that all SARS‐CoV‐2 patients died. SARS‐CoV‐2 infection mortality may be linked to underlying diseases and disorders. Diabetes mellitus and hypertension were the most common comorbidities in the present study, which was consistent with the study of Vahedi et al. ⁵⁰ The death rate of COVID‐19 patients admitted to our hospital's ICU was exceptionally high (100%) compared to the mortality of patients admitted for COVID‐19 in other hospitals worldwide including Iran (57.9%), ⁵⁰ Singapore (25.7%), ⁵⁴ China (37.7%), ⁵⁵ the United Kingdom (32%), ⁵⁶ and Spain (21%). ⁵⁷

As a consequence of COVID‐19 crisis situations, this study had some limitations including the relative small sample size, the lack of molecular assays to reveal the genetic mechanism backgrounds behind the antibiotic resistance, and the lack of investigation of other viral and fungal superinfections.

5. CONCLUSIONS

A significant part of the negative outcomes of COVID‐19 patients in ICU is related to superinfections. This study showed a high frequency rate of superinfections by MDR Gram‐negative and Gram‐positive bacteria among COVID‐19 patients in southwest Iran. Also, the high level of resistance observed against fluoroquinolones and cephalosporins, confirms their inappropriateness in COVID‐19 patients. The prevention of long‐term consequences caused by COVID‐19, demands continuous antibiotic surveillance particularly in management of bacterial superinfections.

AUTHOR CONTRIBUTIONS

Sousan Akrami, Effat Abbasi Montazeri, and Morteza Saki: analyzed the patient's data and wrote the manuscript. Sousan Akrami, Morteza Saki, and Nilofar Neisi: performed the microbiological and molecular testing of the patients' samples. Reza Khedri, Sahar Allah Dini, Atefeh Akbari Motlagh and Fatemeh Ahmad: reviewed the manuscript and provided consultation, regarding intellectual argumentation. All authors read and approved the final version of the manuscript.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

Akrami S, Montazeri EA, Saki M, et al. Bacterial profiles and their antibiotic resistance background in superinfections caused by multidrug‐resistant bacteria among COVID‐19 ICU patients from southwest Iran. J Med Virol. 2022;95:e28403. 10.1002/jmv.28403

DATA AVAILABILITY STATEMENT

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.