The impact of COVID-19 pandemic on healthcare associated infections: A teaching hospital experience

Abstract

Coronavirus disease-19 (COVID-19) is a global pandemic, with a high capability of contagious distribution, where national secondary and co-infections characterization are lacking. The objective of this study was to assess the impact of the COVID-19 pandemic on infection rates among patients admitted to the intensive care units at King Abdullah University Hospital, profiling the drug resistance rates nationally. This is a cross-sectional study of COVID-19 associated infections that was conducted at a teaching hospital, in the north of Jordan. It included all COVID-19 patients who were admitted to intensive care units during the first and second pandemic waves. Data on age, gender, length of stay, co-morbidities, co-infections and sensitivity to antibiotics were retrospectively collected from the hospital information database. Statistical analyses were performed using SPSS software. A total of 589 COVID-19 patients were included, of whom 20% developed bacterial associated infections. The ratio of bacterial co-infection to secondary infections was 1:8. Gram-negative bacteria, Acinetobacter baumannii (40.1%), Eschericia coli (17.5%), Klebsiella pneumonia (6.8%), and Pseudomonas aeruginosa (5.1%) were the most abundant isolated species. The detection rates of E coli (ESBL), K pneumonia (ESBL), A baumannii (CRO), P aeruginosa (CRO), S aureus (MRSA) were 52%, 67%, 97%, 44%, and 67%, respectively.

1. Introduction

The Coronavirus pandemic (COVID-19) manifested with different clinical features ranging from mild symptoms to sever pneumonia requiring intensive care unit admissions,^[1–3] and increased hospitalization rates.^[4] Bacterial co-infections are commonly associated with viral respiratory infections and with increased morbidity, mortality, and challenges in diagnoses and treatment.^[5,6] Intensive care unit’s patients are at high risk of developing healthcare associated infections that affect the circulatory, urinary, and respiratory systems.^[7]

Multicentre international studies from China and the UK showed that more than 80% of patients were treated with antibiotics during their admission.^[8,9] Moreover, the incidence of bacterial infection rates ranged between 3% and 12 % in developing countries and between 7% and 19% in low- and middle-income countries.^[10]

Despite the fact that multiple worldwide studies had reported a low frequency of co-infections in cases of COVID-19 patients,^[11–13] the prevalence, incidence and characteristics of bacterial infection in COVID-19 infected patients are not fully understood.^[14]

The first case reported for COVID-19 in Jordan was on March 2, 2020, in a patient who came from Italy. More cases were reported 2 weeks later with a dramatic spread of the virus all over the country. The result was a dramatic increase in hospital admissions and their hospital associated infections. Two previous studies reported gram-negative Acinetobacter baumannii (A baumannii) as the most pathogenic bacteria detected in adult and pediatric intensive care units.^[5,15]

To our knowledge, there is no national study that addresses the impact of COVID-19 infection rates among patients admitted to the intensive care units with characterization of their secondary and co-infections rates. This study aimed to fill this gap.

2. Methods

This is a cross-sectional retrospective study of COVID-19 associated infections in intensive care units at a university hospital. It was conducted between November 2020 and April 2021 on patients with COVID-19 infections that were confirmed by Reverse Transcription Polymerase Chain Reaction that tested nasopharyngeal or lower respiratory tract fluids. A bacterial infection was considered as co-infection when the bacteria and the COVID-19 were detected concomitantly on hospital admission and as super-infection when the bacteria were detected after 48 hours of hospital admission.

King Abdullah University Hospital is a tertiary-care referral center in the north of Jordan serving 1.2 million inhabitants of the governorate of Irbid. Over a period of 6 months, data was collected and included age, gender, co-morbidities, length of hospital stay, co- and secondary infections, antibiotic sensitivity results and mortality of all COVID-19 diagnosed patients.

Multidrug resistance (MDR) bacteria refer to microbial resistance against 1 antibiotic in at least 3 different classes. Extended spectrum beta lactamase (ESBL) refers to enzymes produced by bacteria that provide multi-resistance to β-lactam antibiotics, such as penicillin, broad-spectrum cephalosporins, and monobactams. Carbapenem resistance organisms (CRO) refer to gram-negative bacteria that are resistant to any of the carbapenem antibiotics. Extensive drug resistance (XDR) refers to bacterial isolates that remain susceptible to only 1 or 2 classes of antibiotics. Pan-drug resistance (PDR) refers to being resistant to all antibiotic classes.

This study was approved by the Institutional Review Board of the Faculty of Medicine at the Jordan University of Science and Technology.

Statistical analyses were performed using SPSS software. Descriptive analysis was performed, continuous and categorical variables were presented as medians and percentages respectively. Chi-square test was used to compare differences between patients with other infections and those without. A P < .05 was considered as significant.

3. Results

A total of 589 COVID-19 patients were hospitalized between November 2020 and April 2021. Of those, 375 (60.6%) were male, with a median age of 67 years. The main characteristics of patients are shown in Table 1. Overall, 151 (25.6%) of COVID-19 patients developed microbial infections, 3 of patients had secondary and co-infections. There was a statistically significant association between patients’ gender and intensive care unit’s length of stay (ICULOS) in all groups. Hospital Length of Stay, lung disease and mortality had a statistically significance difference in relation to secondary infections as summarized in Table 1.

Table 1.

General characteristics of COVID-19 hospitalized patients.

	Secondary-infections	Co-infections	No infections	P value*
	(n = 137)	(n = 17)	(n = 438)	P value†
Age‡	69.5 (58–76)	72 (65–76)	67 (57–76)	.853
(yr)				.866
Gender F N (%)	69 (50.4)	14 (82.4)	149 (34)	.003
				0
HLOS‡	16 (10–23)	12 (9–24)	9 (5–14)	0
(d, median)				.194
ICULOS‡ (d, median)	7 (4–14)	6 (4–13)	3 (2–6)	0
				.001
Co-morbidities	122 (89.1)	17 (100)	376 (85.8)	.400
				.105
DM	69 (50.4)	13 (76.5)	250 (57.1)	.127
				.085
HTN	91 (66.4)	15 (88.2)	288 (65.8)	.971
				.055
Cardiac diseases	36 (26.3)	7 (41.2)	148 (33.8)	.096
				.415
Brain diseases	19 (13.9)	2 (11.8)	55 (12.6)	.700
				.887
Lung diseases	12 (8.8)	2 (11.8)	17 (3.9)	.036
				.223
Kidney diseases	15 (11)	2 (11.8)	69 (15.8)	.167
				.737
Malignancies	12 (8.8)	1 (5.9)	36 (8.2)	.831
				.712
Smoking	13 (9.5)	1 (5.9)	51 (11.6)	.510
				.491
Mortality (%)	126 (92)	15 (88.2)	351 (80.1)	.001
				.561

DM = diabetes mellitus, HLOS = hospital length of stay, HTN = hypertension., ICULOS = intensive care unit length of stay.

^*Comparison between patients without infections and bacterial secondary infections.

^†Comparison between patients without infections and bacterial co-infections.

^‡Median (25–75 percentiles).

The mortality rate was 83%, the Kaplan–Meier curve showed that 50% of mortality occurred by the 6th day of ICU admission (Fig. 1).

Figure 1.

Illustrates Kaplan–Meier curve for cumulative survival during intensive care unit length of stay for Covid-19 patients. ICULOS = Intensive Care Unit Length of Stay.

There were 177 infections, of which 80% were bacterial. Of these, 36% were pneumonia; associated with ventilator usage in 28% of cases. Urinary tract infections were common in cases of Foley catheter insertions. Less common infections were associated with central venous catheter insertions (Fig. 2).

Figure 2.

Illustrates device associated infections in percentile among Covid-19 patients’ hospital length of stay. CAUTI = Catheter-Associated Urinary Tract Infection, UTI = urinary tract infection, VAP = ventilator associated pneumonia.

Regarding the distribution of reported microorganisms, both gram-negative and gram-positive bacteria were isolated in 72% and 8.5% of cases, respectively. On the other hand, Candida species were associated with 20% of the total infections. Non fermenting gram-negative bacteria were frequent. A baumannii was the commonest agent at 40.1%, followed by Eschericia coli (E coli) at 17.5%. Gram-positive isolates, Staphylococcus aureus (S aureus) was predominant at 3.4%.

The detection rates of E coli (ESBL), K pneumonia (ESBL), A baumannii (CRO), P aeruginosa (CRO), S aureus (MRSA) were 52%, 67%, 97%, 44%, 67%, respectively (Table 2). PDR, XDR, and MDR (A baumannii) had the highest resistance rates at 26.8%, 64.8%, and 5.6%, respectively. While 77% of E. coli was XDR, about 58% of K pneumonia was MDR and 11% of P aeruginosa was PDR. The incidence rate of drug resistant gram-negative bacteria to ticarcillin, pipracillin, and tetracycline were 100%, 92%, and 90%, respectively. Resistance to cephalosporins was about 80%. Furthermore, fluoroquinolones resistance rates were approximately 84%. The resistance rate to tigecycline was 22%. Other tetracycline antibiotics had the highest resistance rates among analyzed classes (Table 3).

Table 2.

The isolated microorganisms from COVID-19 patients.

Microorganism species	Total number of isolates (%)	Resistance prevalence (%)	Resistant codes
A baumannii	71 (40.1)	97	CRO
E coli	31 (17.5)	52	ESBL
K pneumonia	12 (6.8)	67	ESBL
P aeruginosa	9 (5.1)	44	CRO
S maltophilia	2 (1.1)	0	NA
M morganii	1 (0.6)	0	NA
B cepacia	1 (0.6)	0	NA
S aereus	6 (3.4)	67	MRSA
E feacalis	5 (2.8)	100	MDR
E facium	4 (2.3)	25	VRE
C albicans	3 (1.7)	0	NA
C tropicalis	1 (0.6)	0	NA

CRO = carbapenem resistant organism, ESBL = extended spectrum beta lactamase, MDR = multidrug resistant, MRSA = methicillin resistant Staphylococcus aureus, N/A = non-applicable, VRE = VANCOMYCINE resistant enterococci.

Table 3.

Antibacterial resistance panel in intensive COVID-19 associated infections.

Antibiotics names (%)		A baumannii	E coli	K pneumoniae		P aeruginosa
		N = 71	N = 31	N = 12		N = 9
Aminoglycosides
Amikacin	39.4		3.2		16.7		44.4
Gentamycin	57.7		16.1		41.7		33.3
Tobramycin	50.7		16.1		16.7		44.4
Carbapenems
Aztronem	8.5		22.6		58.3		11.1
Imipenem	67.6		6.5		33.3		44.4
Meropenem	84.5		3.2		8.3		22.2
Cephalosporins
Ceftazidime	53.5		29		41.7		44.4
Cefepem	97.2		48.4		50		44.4
Ceftriaxone	73.2		48.4		75		–
Cefixime	1.4		29		66.7		–
Fluoroquinolones
Ciprofloxacin	67.6		58.1		50		55.6
Levofloxacin	66.2		77.4		66.7		11.1
Penicillins
Piperacillin	81.7		35.5		75		33.3
Penicillins & B inhibitors
Ticarcillin/Clavulanic acid		67.1	12.9		33.3		44.4
Piperacillin/Tazobactam		64.8	3.2		33.3		44.4
Ampicillin		4.2	48.4		50		0
Ampicillin/Sulbactam		38	35.5		50		11.1
Polymyxins
Colistin	–		–		–		–
Tetracyclines
Tetracyline	21.1		22.6		25		–
Tigecycline	19.7		0		8.3		11.1
Minocycline	26.8		12.9		25		–
Type of resistance (%)
MDR	5.6		77.4		58.3		22.2
XDR	64.8		0		16.7		22.2
PDR	26.8		0		0		11.1

– = not tested, MDR = multidrug resistant, PDR = pan drug resistant, XDR = extensively drug resistant.

4. Discussion

COVID-19 is a potential life-threatening infection. Bacterial infections among ICU patients during the pandemic were associated with increased morbidity and mortality rates. This retrospective study addressed the prevalence of community acquired and healthcare associated infections in a developing country. The results highlight the importance of antimicrobial stewardship and infections control.

In this study, microbiological tests confirmed that about 1 quarter of COVID-19 patients had a 3-fold increase in bacterial infections compared to the results of other international studies.^[16,17]

A high prevalence of infection was secondary in 91% of cases, while the number of co-infections was rare. This is compatible with other studies where the prevalence was 3.5 and 14.3% for co- and secondary infections respectively.^[18,19]

The exact cause for this higher rate is unknown, except for the significant difference in the longer median length of ICU stay of the co- and secondary infections compared with the non-infected patients. This is compatible with the findings of an Iranian study,^[20] but not with those of a Chinese study.^[8]

Another risk factor was female gender, in which the proportions of females’ co- and secondary infections were significantly higher than in non-infected cases. The reason may include immunological pathways affected by sex hormones, as well as consequences of differential expression of X-chromosome-encoded genes on immune responses to pathogens. This study’s finding in relation to this parameter is contrary to a study from Wuhan, China.^[21]

Lung disease was the only co-morbid condition that was associated with secondary infections, with a high rate compared with non-infected patients. Other studies suggest that critical stages of disease, medical device usage and more advanced age as being associated with a statistically significant increase in morbidity and mortality rates.^[22,23]

Regarding the distribution of reported microorganisms, both gram-negative and gram-positive bacteria were isolated in 72% and 8.5% of cases, respectively. This finding was different from a Chinese study, where gram-negative bacteria accounted for 87.5% and 12.5% for gram-positive isolates.^[24]

This study showed that gram-negative bacterial infections had the highest drug resistant rate, where A baumannii was the common isolated microorganism, followed by E coli, K pneumonia, and P aeruginosa. Furthermore, these isolates were associated with the hospital’s ICUs and non-COVID-19 admitted patients. This is consistent with other reported studies.^[15,25]

On the other hand, an international retrospective study showed that gram-positive bacteria were the most common isolated microorganisms from COVID-19 patients. Streptococcus pneumonia and S aureus stood at 16.2% each. Comparisons suggest that drug resistance rats and pathogenic microorganism distributions vary between different countries.^[26]

Several resistance mechanisms have been reported. In this study, ESBL was the main cause of antibiotic resistance in K pneumonia and E coli at 67% and 52%, respectively. Moreover, there were a high percentage of carbapenem resistant gram-negative bacteria, with A baumannii (CRO) at 97% and P aeruginosa (CRO) at 44%. This plays an important role being a cause for serious complications in cases of COVID-19 infection. Our figures are higher than those of a Chinese study where E coli (ESBL), K pneumoniae (ESBL), A baumannii (CRO), P aeruginosa (CRO) were 30.5%, 16.1%, 19.5%, and 10%, respectively. In addition, neither XDR, nor PDR were reported in contrast to the findings of this study.^[24]

The resistance rates to tigecycline, amikacin and tobramycin were 22%, 46%, and 49%, respectively. These antibiotics have a lower resistance rate compared with the other antibiotics. Profiling bacterial distribution and drug resistance patterns in various countries will help determine the choice of a suitable antibiotic with the lowest resistance rate.^[27]

Drug susceptibility pattern for A baumannii showed resistance against different antibiotic classes. Resistance limits the choice of therapeutic agents, with higher morbidity and mortality in different parts of the world, especially in Asian countries.^[28,29]

5. Conclusion

This study demonstrates that viral-bacterial synergistic interactions can be a cause for higher morbidity and mortality and highlights a high prevalence of bacterial infections and antibiotic resistance rates among COVID-19 patients.

Acknowledgment

Special thanks to the Jordan University of Science and Technology for funding the research (20220078).

Author contributions

Conceptualization: Haneen Obeidat, Ziad El-nasser.

Data curation: Haneen Obeidat, Almutazballah Qablan, Faris Gharaibeh.

Formal analysis: Haneen Obeidat.

Funding acquisition: Ziad El-nasser.

Investigation: Haneen Obeidat.

Methodology: Haneen Obeidat, Almutazballah Qablan, Faris Gharaibeh.

Project administration: Haneen Obeidat, Ziad El-nasser.

Resources: Haneen Obeidat.

Software: Haneen Obeidat, Almutazballah Qablan, Faris Gharaibeh.

Supervision: Haneen Obeidat, Ziad El-nasser.

Validation: Haneen Obeidat, Ziad El-nasser, Zouhair Amarin.

Visualization: Haneen Obeidat.

Writing – original draft: Haneen Obeidat.

Writing – review & editing: Haneen Obeidat, Ziad El-nasser, Zouhair Amarin.