Incidence of secondary bacterial infections and risk factors for in-hospital mortality among coronavirus disease 2019 subjects admitted to secondary care hospital: A single-center cross-sectional retrospective study

ABSTRACT

Background:

This study aims to determine the prevalence of secondary bacterial infections (SBIs) in hospitalized coronavirus disease 2019 (COVID-19) subjects and evaluate their antibiotic susceptibility. The study also sought to identify risk factors for the outcome of SBIs in COVID-19 subjects.

Methods:

This single-center cross-sectional retrospective study was carried out at Sohar Hospital in Oman. The study examined hospitalized COVID-19 subjects diagnosed with SBIs during March 2020–December 2022. The relevant subjects’ data were extracted from hospital electronic health records and analyzed using STATA version 14. The Chi-square test or Fisher’s exact test was employed for analyzing categorical variables, and P < 0.05 was deemed statistically significant.

Results:

The research encompassed a total of 817 bacteria recovered from various clinical samples of 421 subjects. The older individuals (39.4%) and men (65.6%) experienced bacterial infections more frequently, with bloodstream and respiratory infections being the most common. Gram-negative bacilli (GNB) were responsible for a higher proportion (85.6%) of infections, with Acinetobacter baumannii, Pseudomonas aeruginosa, and Klebsiella pneumoniae being the most common pathogens. Subjects who underwent mechanical ventilation, received corticosteroid therapy, and who had underlying comorbidities, such as diabetes and chronic renal disease, were found to have higher mortality rates. Neutrophilia, elevated C-reactive protein, lymphocytopenia, decreased serum albumin level, sepsis, and pneumonia were found to be independent contributors to mortality.

Conclusions:

SBI is common among COVID-19-hospitalized subjects. GNB were primarily linked to SBI. The severity and the likelihood of SBI increased in subjects undergoing medical interventions and immunosuppressive therapy.

INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic, the most significant global health crisis of the 21^st century, has severely impacted the health-care system and caused significant harm to both the physical and mental well-being of individuals. The pathogen responsible for causing the disease, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was initially discovered in Hubei province of China in December 2019. It has since spread globally, and its variants are currently circulating in various parts of the world.[1,2] As of July 19, 2023, the World Health Organization has received reports of over 768 million confirmed cases of COVID-19, with more than 6.9 million deaths. The initial instance of COVID-19 in Oman was documented in February 2020. From that time until July 2023, the World Health Organization received reports of over 399,000 confirmed cases and more than 4600 deaths associated with COVID-19.[3] Another significant challenge that the world has encountered during the pandemic is the rise in the occurrence of antimicrobial-resistant bacterial pathogenic infections, both in health-care facilities and the general community.[4,5] Viral infections inhibit the immune system, making individuals more susceptible to secondary bacterial infections (SBIs). Furthermore, the prevalence of multidrug-resistant pathogenic infections, which are challenging to treat due to limited antibiotic options, is increased by factors such as mechanical ventilation, extended hospital stays, the use of immunosuppressive agents such as corticosteroids and monoclonal antibodies (tocilizumab) in COVID-19 subjects, and the inappropriate use of antibiotics in the majority of hospitalized subjects.[6] Secondary bacterial infections are commonly observed in hospitalized COVID-19 subjects. These infections are associated with poor prognosis, as they raise the likelihood of intensive care unit (ICU) admission, frequent treatment failure, and higher mortality rates. This is particularly true for individuals with weakened immune systems.[7,8,9,10] Studies have shown that the presence of secondary bacterial infections (SBIs) in COVID-19 subjects in the ICU greatly raises the risk of death, with an increase of up to 50%.[9,11,12]

The predominant bacterial pathogens linked to SBIs in hospitalized COVID-19 subjects are Acinetobacter spp., Klebsiella pneumoniae, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Nevertheless, the occurrence rate and antimicrobial susceptibility pattern of bacterial isolates exhibit significant variations across different regions. Assessing the proportion of COVID-19 subjects who develop secondary bacterial infections, identifying the specific type of pathogen causing the infection, and understanding its susceptibility to antibiotics in a local hospital would aid in the targeted administration of antibiotics. This approach would help mitigate the problem of antibiotic resistance and ultimately enhance patient care.[13] Prior research has identified several factors that independently predict the severity of COVID-19 and increased mortality. These factors include leukocytosis, elevated D-dimer level, elevated C-reactive protein (CRP), presence of pneumonia and sepsis, decreased serum albumin level, and the need for mechanical ventilation.[13,14,15] The objective of our study was to ascertain the frequency of secondary bacterial infections (SBIs) among COVID-19 subjects, identify the types of bacteria causing these infections and their susceptibility to antibiotics, analyze the antimicrobial treatments used, and examine the clinical characteristics associated with SBIs. In addition, we aimed to determine the outcomes of SBIs, particularly in-hospital mortality, and identify the risk factors associated with it.

METHODS

We carried out a retrospective study at a secondary care hospital in Oman, which served as the primary response center for the pandemic in the North Batinah region. The study encompassed all COVID-19 subjects who were admitted to the hospital between March 1, 2020, and December 31, 2022. These subjects were confirmed to have COVID-19 through a positive reverse transcription-polymerase chain reaction (RT-PCR) test and also developed a secondary bacterial infection, which was confirmed through positive bacterial culture and biochemical tests following the standard criteria set by the Clinical Laboratory Standards Institute (CLSI) guidelines.[16] The study received approval from both the Institutional Ethic Committee and the Oman Research and Review Board. The study population’s relevant data, including demographic characteristics, comorbidities, blood studies, bacteriological profile, antibiotic susceptibility pattern, treatment, and infection outcome, were retrieved from the hospital’s electronic health record system and microbiology laboratory records.

Inclusion criteria

The study included subjects who were confirmed to have COVID-19 by positive RT-PCR test and subsequently developed bacterial infections during therapy in the hospital wards and ICUs. The SBI is indicated by clinical symptoms and positive bacterial cultures.

Exclusion criteria

The bacterial isolates obtained from clinical samples of hospitalized COVID-19 subjects who showed no clinical signs of secondary bacterial infection, as these were likely to be contamination and not relevant to the study were excluded. In addition, bacteria isolated from commonly colonized areas such as the nasopharynx, rectum, and axilla, as well as subjects with incomplete data, were omitted from the study.

Figure 1 represents the CONSORT flow chart illustrating the criteria for the inclusion and exclusion of study subjects.

Figure 1.

The CONSORT flow chart illustrating the criteria for including and excluding participants. MRSA: Methicillin-resistant Staphylococcus aureus, COVID-19: Coronavirus disease 2019, RT-PCR: Reverse transcription-polymerase chain reaction

Bacterial identification and antibiotic susceptibility testing

Bacterial identification and antibiotic susceptibility testing were conducted on qualified clinical samples obtained from suspected SBIs in COVID-19 subjects. These samples included endotracheal tube aspirates, bronchoalveolar lavage fluid, sputum, blood, urine, pus/wound swab, cerebrospinal fluid, and other specimens. The samples were cultured on blood agar, chocolate agar, and MacConkey agar, following the established criteria set by the CLSI.[16] Pathogen identification at the species level was conducted using either conventional identification methods or the VITEK^® 2 (bioMérieux Inc., Marcy-l’Étoile, France) automated microbiological system.[16] The isolated pathogen’s antibiotic susceptibility was tested with either the conventional method with the Mueller–Hinton agar plate or the VITEK II automated microbiological system. The disc diffusion method was used to test the susceptibility of antibiotics including amikacin, ampicillin, cefotaxime, cefuroxime, ceftazidime, ciprofloxacin, gentamicin, clindamycin, ceftriaxone, erythromycin, linezolid, imipenem, meropenem, piperacillin-tazobactam, ampicillin-clavulanic acid, tigecycline, and trimethoprim-sulfamethoxazole, following the guidelines set by the CLSI.[16] The broth microdilution method was utilized to conduct antibiotic susceptibility testing for vancomycin and colistin. The results were interpreted as sensitive, resistant, or intermediate according to the standard CLSI guidelines.[16]

Data management and statistical analysis

The predesigned pro forma was formulated within an Excel spreadsheet. Data were entered in Microsoft Excel and analyzed using Statistical Software STATA version 14 (StatCorp LCC, College Station, TX, USA). The categorical variables were summarized using frequency counts and proportions. The quantitative variables were summarized using either the mean and standard deviation or the median and interquartile range. The statistical significance of the relationship between continuous variables and the COVID-19 outcome was evaluated using either the Student’s t-test or the Mann–Whitney U-test, depending on whether the assumptions for the test were met. The association between categorical variables was evaluated using either the Chi-square test or Fisher’s exact test, depending on the assumptions of the test. P <0.05 was considered statistically significant. Simple logistic regression was done, and the association between various predictors and the outcome was expressed as odds ratio with 95% confidence interval.

RESULTS

A total of 1125 bacterial isolates were initially considered. However, after applying specific criteria to include or exclude certain isolates, a final count of 817 nonduplicate bacterial isolates recovered from various clinical samples of 421 subjects was included in the study. Table 1 presents the demographic attributes of the study population. The prevalence of secondary bacterial infection was higher in males (65.6%) and in individuals aged over 60 years (39.4%). The majority of subjects exhibited one or more comorbidities with at least one underlying comorbidity observed in 21.6% of subjects. The prevalence of hypertension, diabetes mellitus, and chronic renal disease was 42.5%, 37.8%, and 20.2%, respectively, making them the most prevalent comorbidities. The majority of subjects (55.1%) underwent mechanical ventilation, whereas a significant proportion (69.1%) received corticosteroid therapy. The majority of SBIs in our study population (70.1%) were caused by Gram-negative bacilli (GNB), whereas Gram-positive bacteria accounted for only 14.4% of the cases [Table 2]. The most prevalent GNB are K. pneumoniae (31.1%), P. aeruginosa (19.7%), and Acinetobacter spp. (16.9%). Enterococcus spp. were the most common Gram-positive isolates (7%), followed by methicillin-resistant S. aureus (MRSA) (4.2%) and methicillin-sensitive S. aureus (2.8%). Table 2 displays the distribution of etiological agents on a sample-by-sample basis. Bacterial isolates were most commonly isolated from endotracheal secretions (35.2%), blood (34%), urine (25.2%), and less commonly in sputum (3.3%), pus/wound swab (1.3%), and other (0.7%) samples. Table 3 displays the antibiotic susceptibility pattern of bacterial isolates. Acinetobacter spp. and Klebsiella have exhibited a significant level of resistance to the majority of commonly employed antibiotics, whereas all strains (100%) have displayed susceptibility to colistin. 93% and 66.7% of Klebsiella spp. were found to be susceptible to tigecycline and Acinetobacter species, respectively. P. aeruginosa, Serratia spp., and Stenotrophomonas maltophilia have exhibited a notable susceptibility rate, ranging from 80% to 100%, to commonly tested antibiotics. The bacteria E. coli, Proteus spp., and Enterobacter spp. experiments have shown that the tested antibiotics have produced different levels of effectiveness, with susceptibility ranging from 40% to 100%. S. aureus has demonstrated susceptibility rates between 90% and 100% to trimethoprim-sulfamethoxazole, linezolid, gentamicin, clindamycin, and amoxicillin-clavulanic acid. MRSA strains exhibited high susceptibility to linezolid and piperacillin-tazobactam (100%), as well as trimethoprim-sulfamethoxazole (94%) and gentamicin (97%). Enterococcus species exhibited significant resistance to the majority of antibiotics, with the exception of linezolid and ceftriaxone. Vancomycin has demonstrated complete susceptibility against all Gram-positive bacteria. Table 4 presents the factors that can be used to predict the outcome of SBIs in subjects with COVID-19. Older subjects, subjects with high neutrophil count, low lymphocyte count, elevated CRP levels, and reduced platelet count and serum albumin, exhibited a heightened likelihood of mortality (P < 0.05). Subjects who developed sepsis, pneumonia, and acute respiratory distress syndrome, as well as those who received corticosteroid therapy and mechanical ventilation and had chronic renal disease, experienced a higher rate of death.

Table 1.

Baseline characteristics of the study population

Characteristics	n (%)
Total number of patients	421
Gender
Male	276 (65.6)
Female	145 (34.4)
Age (years)
0–20	14 (3.3)
21–40	82 (19.5)
41–60	159 (37.8)
>60	166 (39.4)
Total number of bacteria isolated	817
Number of patients with infection at one site	205 (48.7)
Number of patients with infection at two sites	99 (23.5)
Number of patients with infection at three or more sites	117 (27.8)
Comorbidities/risk factors
Respiratory disease (COPD, asthma)	29 (6.9)
Heart disease	47 (11.2)
Chronic neurological disease	8 (1.9)
Chronic renal disease	85 (20.2)
Diabetes mellitus	159 (37.8)
Hypertension	179 (42.5)
Patients with one comorbidity	91 (21.6)
Patients with two comorbidities	76 (18.1)
Patients with three or more comorbidities	116 (27.6)
Length of hospital stay (days), mean±SD	15.15±8.61
Mechanical ventilation	232 (55.1)
Steroid therapy	291 (69.1)

SD: Standard deviation, COPD: Chronic obstructive pulmonary disease

Table 2.

Distribution of bacterial isolates in a sample

	n (%)
Gram-negative bacteria	699 (85.6)
Acinetobacter spp.	138 (16.9)
Klebsiella pneumoniae	254 (31.1)
Escherichia coli	73 (8.9)
Pseudomonas aeruginosa	161 (19.7)
Serratia spp.	17 (2.1)
Enterobacter spp.	35 (4.3)
Proteus spp.	11 (1.3)
Stenotrophomonas maltophilia	4 (0.5)
Other Gram-negative bacteria	6 (0.7)
Gram-positive bacteria	118 (14.4)
Staphylococcus aureus	23 (2.8)
MRSA	34 (4.2)
Enterococcus spp.	57 (7.0)
Streptococcus spp.	4 (0.5)

MRSA: Methicillin-resistant Staphylococcus aureus

Table 3.

Antibiotic susceptibility (S) pattern of bacterial isolates

Bacterial isolate	Antibiotic susceptibility (S) of isolates (%)
	AMIK	AMPI	AUGM	CFTX	CFXM	CIPR	CL	CLIN	CORT	CRONM	CTAZ	ERY	GENT	IMIP	MERO	LINZ	TAZP	TIGE	VANC
Gram-negative bacteria
Acinetobacter spp.	4.4	-	-	-	-	4.2	100	-	26.5	3.1	4.8	-	5.4	5.9	4.5	-	4.5	66.7	-
Pseudomonas aeruginosa	93.8	-	-	-	-	92.9	100	-	-	-	100	-	88.3	86.7	87.4	-	95.6	-	-
Klebsiella spp.	49.5	0	17.1	32.0	23.1	48	100	-	31.0	2.1	3.6	-	50.8	43.7	45.6	-	42.0	92.9	-
Escherichia coli	94.6	7.5	17.1	36.0	26.8	47.2	100	-	34.7	5.4	8.9	-	72.2	95.7	93.0	-	84.2	-	-
Enterobacter spp.	51.7	0	0	30.4	2.9	60	100	-	54.3	13.6	12.5	-	62.9	56	53.6	-	56.7	100	-
Proteus spp.	100	54.5	81.8	87.5	90.9	81.8	-	-	66.7	66.7	75	-	81.9	100	100	-	100	-	-
Stenotrophomonas maltophilia	50	0	-	-	-	100	-	-	100	-	50	-	100	0	0	-	100	-	-
Serratia spp.	100	0	0	93.8	0	100	-	-	100	6/7	100	-	100	100	100	-	100	-	-
Gram positive bacteria
Staphylococcus aureus	-	-	100	-	-	69.6	-	90.5	100	-	-	90.9	100	-	-	100	-	-	100
MRSA	-	-	-	-	-	55.9	-	54.5	94.1	-	-	45.5	97.1	-	-	100	100	-	100
Enterococcus spp.	-	59.3	58.8	6.3	70.8	18.9	-	0	-	100	-	0	-	-	-	100	-	-	100
CONS	-	-	100	-	-	48.7	-	43.6	56.4	-	-	15.4	48.7	-	-	100	-	-	100

AMIK: Amikacin, AMPI: Ampicillin, AUGM: Amoxicillin-clavulanic acid, CFTX: Cefotaxime, CFXM: Cefuroxime, CIPR: Ciprofloxacin, CL: Colistin, CLIN: Clindamycin, CORT: Trimethoprim-sulfamethoxazole, CRONM: Ceftriaxone, CTAZ: Ceftazidime, ERY: Erythromycin, GENT: Gentamicin, IMIP: Imipenem, MERO: Meropenem, LINZ: Linezolid, TAZP: Piperacillin-tazobactam, TIGE: Tigecycline, VANC: Vancomycin

Table 4.

Prediction of outcome with various independent variables

Variable	Recovery (survival)	Death (no survival)	P	OR (95% CI)
n	150	255
Age, mean±SD	48.6733±17.7277	57.4439±16.9757	<0.001	1.02 (1.01–1.04)
Total WBC (109/L), median (IQR)	10.6 (8–14.4)	16 (12.2–23.6)	<0.001	1.16 (1.11–1.21)
Neutrophil, median (IQR)	8 (5–11.4)	13.75 (10.35–20.9)	<0.001	1.23 (1.17–1.30)
Lymphocyte, median (IQR)	1.7 (1.1–2.5)	0.855 (0.5–1.395)	<0.001	0.61 (0.48–0.76)
Platelets, median (IQR)	327 (242–449.3)	215.45 (143.9–298)	<0.001	0.99 (0.99–0.99)
CRP 9 (mg/L), median (IQR)	44.21 (17.4–71.8)	179.5 (58.5–289.7)	<0.001	1.00 (1.00–1.01)
Serum albumin (g/L), mean±SD	33.41±4.95	24.30±5.12	<0.001	0.71 (0.65–0.77)
Blood infection
Bacteremia	42 (30.7)	38 (15.8)	<0.001	1.21 (0.68–2.13)
Septicemia	21 (15.3)	143 (59.6)		9.12 (5.10–16.2)
Catheter-related sepsis	3 (2.2)	6 (2.5)		2.67 (0.64–11.2)
No blood infection	71 (51.8)	53 (22.1)		1
Respiratory tract infection
Pneumonia	11 (8.0)	54 (22.6)	<0.001	4.22 (2.03–8.78)
Respiratory infection other than pneumonia	62 (45.3)	72 (30.1)		1.52 (0.96–2.40)
No respiratory infection	64 (46.7)	113 (47.3)		1
Urinary tract infection
Yes	61 (44.5)	81 (34.0)	0.044	0.64 (0.41–0.98)
Skin and soft-tissue infection
Yes	18 (13.1)	11 (4.6)	0.003	0.32 (0.14–0.70)
ARDS
Yes	12 (8.7)	66 (27.2)	<0.001	3.9 (2.03 –7.54)
Diabetes mellitus
Yes	46 (30.7)	113 (44.3)	0.007	1.79 (1.17–2.75)
Hypertension
Yes	52 (34.7)	127 (49.8)	0.003	1.86 (1.23–2.83)
COPD and asthma
Yes	11 (7.3)	18 (7.1)	0.92	0.95 (0.44–2.09)
Chronic heart disease
Yes	13 (8.7)	34 (13.3)	0.16	1.62 (0.82–3.18)
Chronic renal disease
Yes	10 (6.7)	73 (28.6)	<0.001	5.61 (2.79–11.27)
Corticosteroid therapy
Yes	78 (78.0)	213 (95.1)	<0.001	5.46 (2.53–11.78)
Antiviral drugs
Yes	23 (29.9)	61 (44.5)	0.035	1.88 (1.04–3.40)
Antibiotic therapy
Yes	106 (93.8)	230 (97.5)	0.092	2.53 (0.83–7.71)
Monoclonal antibodies
Yes	41 (43.6)	100 (49.5)	0.35	1.26 (0.77–2.07)
Mechanical ventilation
Yes	42 (46.2)	190 (80.2)	<0.001	2.06 (1.08–3.91)

COPD: Chronic obstructive pulmonary disease, WBC: White blood cell, SD: Standard deviation, IQR: Interquartile range, ARDS: Acute respiratory distress syndrome, OR: Odds ratio, CI: Confidence interval, CRP: C-reactive protein

DISCUSSION

The occurrence of SBI in COVID-19 subjects who were hospitalized was a significant concern during the pandemic. This infection has led to higher mortality rates among COVID-19 subjects due to the combined impact of viral and bacterial infections. Subjects who have a viral infection are more likely to develop secondary bacterial infections (SBIs) because of a complex process involving the suppression of the host immune response, damage to the protective layers of cells, and increased ability of bacteria to attach to tissues.[17] Elderly individuals and individuals with underlying comorbid conditions are more susceptible to acquiring secondary infections, which are often severe.[14] Henry and Lippi discovered a threefold increase in the likelihood of severe COVID-19 in subjects with chronic renal disease.[18] In line with this observation, our study revealed a higher incidence and greater severity of infection in elderly individuals, as well as in subjects with chronic renal disease, diabetes mellitus, and hypertension. Gram-negative pathogens are more prevalent in SBIs compared to Gram-positive bacteria.[19,20] The present study observed comparable results, with more than 80% of infections attributed to Gram-negative pathogens. K. pneumoniae, P. aeruginosa, and Acinetobacter spp. were the most dominant pathogens in the present study, which is consistent with the findings of previous studies conducted by Alshrefy et al. and Musuuza et al.[15,21] Hospital-acquired pathogens linked to bloodstream infections (SBI) include Acinetobacter baumannii, K. Pneumoniae, and MRSA exhibit a higher incidence of multidrug resistance, making them challenging to treat due to the limited availability of effective antibiotics.[6] Excessive treatment with antibiotics during the pandemic may have contributed to the heightened occurrence of multidrug-resistant pathogenic infections.[6]

In our study, bloodstream infection and respiratory infections were the predominant types of healthcare-associated infections. This discovery aligns with recent studies that have documented respiratory system and bloodstream infection as the prevailing types of secondary bacterial infections (SBIs) observed in COVID-19 subjects who are admitted to the hospital.[15,22] This can be attributed to the fact that critically ill COVID-19 subjects are more likely to need lifesaving interventions such as mechanical ventilation and intravenous medications, which make them more susceptible to infections acquired in health-care settings.[23] Approximately 50% of the subjects in our study underwent mechanical ventilation. Research has documented a substantial mortality rate, ranging from 9% to 97%, among COVID-19 subjects who undergo mechanical ventilation.[14,24,25] Goncalves Mendes Neto et al. conducted studies in the United States, whereas Vijay et al. conducted studies in India. The studies found that among COVID-19 subjects who required mechanical ventilation and developed SBI, the mortality rate was 50% and 57%, respectively.[26,27] Similarly, the present investigation revealed a mortality rate of 61.1% among COVID-19 subjects with SBIs. Through multivariate regression analysis, it was determined that total leukocyte count, elevated CRP, and D-dimer levels are indicators of the severity of COVID-19. On the other hand, leukocytosis, the presence of pneumonia and sepsis, decreased serum albumin levels, and the requirement for mechanical ventilation are independent predictors of COVID-19 mortality.[14] Consistent with these results, we observed a notable correlation between death rates and increased levels of CRP, elevated white blood cell count, decreased lymphocyte count, reduced serum albumin levels, as well as the presence of secondary bacterial pneumonia and sepsis.

Various immunomodulatory treatments, including corticosteroids and monoclonal antibodies (such as tocilizumab), were extensively employed during the pandemic to specifically affect the immune response of the host to SARS-CoV-2. Nevertheless, the therapy-induced immunosuppression may increase the susceptibility to secondary bacterial infection. The study revealed a notable correlation between the use of corticosteroid therapy and the occurrence of secondary bacterial infection, as well as an elevated mortality rate. Furthermore, Alshrefy et al. and De Bruyn et al. both found a strong correlation between corticosteroid therapy and SBIs.[15,22] In contrast, Ritter et al. did not discover a correlation between corticosteroid therapy and an elevated risk of SBI. The correlation between the utilization of tocilizumab and the heightened occurrence of SBI is still a subject of discussion and necessitates additional investigation.[28] The current study did not observe a significant correlation between the utilization of tocilizumab and an elevated risk of SBI. In contrast, Alshrefy et al. established a notable correlation between the utilization of tocilizumab and an elevated risk of SBI.[15]

COVID-19 subjects were excessively prescribed antibiotics, despite their ineffectiveness against viruses. Azithromycin had the highest prescription rate among antibiotics, with doxycycline, amoxicillin, and levofloxacin following closely behind.[29] In the current study, a majority of the subjects (79.8%) were administered empirical antibiotics. In addition, the administration of antibiotics did not have any effect on decreasing the mortality rate. The indiscriminate utilization of antimicrobial agents contributes to the escalation of selection pressure and facilitates the spread of resistant bacterial pathogens. This would exacerbate the issue of global multidrug-resistant pathogenic infections.[30] In our study, we observed a significant prevalence of antimicrobial resistance among A. baumannii and K. Pneumoniae strains, demonstrating resistance to the majority of the antibiotics tested. Consequently, this presents a significant challenge in the treatment of the infection. It is essential to implement and strictly follow antibiotic stewardship programs and infection control procedures, including practicing proper hand hygiene. This is crucial for reducing the prevalence of drug-resistant infections caused by pathogenic bacteria and preserving the effectiveness of current antibiotics.[31]

Our study is subject to a few limitations. Due to the retrospective nature of the study, certain laboratory tests such as lactate dehydrogenase, estimation of pro-inflammatory cytokines such as interleukin 6, PaO₂/FiO₂ ratio, and others were not conducted. Hence, we were unable to establish a correlation between their function and the ability to forecast the prognosis of COVID-19 subjects. Furthermore, the specific underlying cause of death and any additional factors that may have influenced mortality were not established. Ultimately, the study was conducted at a single location; therefore, to authenticate our results, it is advisable to conduct a comprehensive study involving multiple centers.

CONCLUSIONS

The study reveals a high prevalence of secondary bacterial infections (SBI) in COVID-19 subjects, particularly in older people and subjects with comorbidities. The most common causative agents are Klebsiella, Pseudomonas, and Acinetobacter. High mortality rates are observed in subjects with leukocytosis, elevated CRP levels, decreased serum albumin, sepsis, pneumonia, and mechanical ventilation. Early detection of risk factors and strict antibiotic stewardship is crucial for optimal care.

Research quality and ethics statement

This study was approved by the Research Ethics Review and Approval Committee (RERAC), Oman (Approval number: MH/DGHS/NBG/RERAC23/2022; Approval date: December 29, 2022). The authors followed the applicable EQUATOR Network (http://www.equato r-network.org/) guidelines, specifically the STROBE Guideline, during the conduct of this research project.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.