Abstract
BACKGROUND:
ICU and other patients hospitalized with corona-virus disease 2019 (COVID-19) are more susceptible to secondary infections. Undetected secondary infections tend to have a severe clinical impact, associated with prolonged hospitalization and higher rates of inpatient mortality.
OBJECTIVES:
Estimate the prevalence of secondary infections, determine the frequency of microbial species detected at different body sites, and measure the association between secondary infections and outcomes among hospitalized COVID-19 patients.
DESIGN:
Cross-sectional analytical study.
SETTING:
Tertiary care center in Riyadh
PATIENTS AND METHODS:
Data were collected through retrospective chart review of hospitalized COVID-19 patients >18 years old from March 2020 until May 2022 at King Saud University Medical City (27 months). Rates of secondary infections among hospitalized COVID-19 patients were described and data on clinical outcomes (intensive care admission, invasive management procedures and mortality) was collected.
MAIN OUTCOME MEASURES:
Features and rates of infection and mortality.
SAMPLE SIZE:
260
RESULTS:
In total, 24.2% of the study population had secondary infections. However, only 68.8% of patients had secondary infection testing, from which 35.2% had a confirmed secondary infection. These patients had a significantly higher prevalence of diabetes mellitus (P<.0001) and cardiovascular diseases (P=.001). The odds of ICU admissions (63.3%) among secondarily infected patients was 8.4 times higher compared to patients with only COVID-19 infection (17.3%). Secondarily infected patients were more likely to receive invasive procedures (OR=5.068) and had a longer duration of hospital stay compared to COVID-19 only patients. Overall mortality was 16.2%, with a predominantly higher proportion among those secondarily infected (47.6% vs 6.1%) (OR=14.015). Bacteria were the most commonly isolated organisms, primarily from blood (23.3%), followed by fungal isolates, which were mostly detected in urine (17.2%). The most detected organism was Candida albicans (17.2%), followed by Escherichia coli (9.2%), Klebsiella pneumoniae (9.2%) and Pseudomonas aeruginosa (9.2%).
CONCLUSION:
Secondary infections were prevalent among hospitalized COVID-19 patients. Secondarily infected patients had longer hospital stay, higher odds of ICU admission, mortality, and invasive procedures.
LIMITATION:
Single-center study, retrospective design and small sample size.
CONFLICT OF INTEREST:
None.
INTRODUCTION
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic caused by severe acute respiratory syndrome coronavirus 2. COVID-19 patients have shown diverse clinical presentations, varying from mild self-limiting, to severe manifestations.1,2 Throughout the pandemic, patients who required intensive care and hospitalization were found to be susceptible to co-infections and hospital-acquired superinfections.3,4 “Co-infection” means detection of a second pathogen at the time of COVID-19 diagnosis and “superinfection” means detection of a second pathogen later during hospitalization.4
Viral respiratory infections predispose patients to concomitant bacterial/fungal infections.5 These secondary infections were associated with poor outcomes in other epidemic and pandemic outbreaks of respiratory viral infections.6 This has also been noted during the COVID-19 pandemic;7 COVID-19 patients with undetected secondary infections had severe clinical impacts associated with prolonged hospitalization, varied treatment approaches, and higher rates of inpatient mortality.8 Therefore, investigating the implications of a microbiologically confirmed secondary infection on the clinical outcome of a COVID-19 patient is necessary to limit the burden of hospitalizations during the pandemic and possible waves of other local or worldwide viral outbreaks.
Several studies had exclusively included only intensive care unit (ICU) admitted COVID-19 patients implying that co-infections, particularly among ICU-admitted patients, were associated with higher mortality rates. A local retrospective study conducted in Asir Central Hospital, Abha, Saudi Arabia included 34 ICU-admitted COVID-19 patients.9 After extensive analysis of the patient data, it was found that 16 patients were infected with COVID-19 only, while 18 patients were co-infected with COVID-19 and bacterial infections. Their study showed that higher mortality rates were associated with patients in the co-infection group compared to the COVID-19 only infected group (50% vs. 18.7%, respectively). The same investigators also reported a longer length of hospital stay for patients infected with both COVID-19 and bacterial infections with a median of 35.2 days compared to 16.2 days for patients infected with only COVID-19 (P<.0001). The sample size in the Abha study was only 34 patients. This study also excluded patients admitted to wards other than the ICU, a possible reason for the underestimation of co-infections.9 Additionally, during the first wave of the pandemic, a single-center retrospective study in Iran including a total of 553 ICU-admitted patients reported a significantly higher rate of inpatient mortality among patients with concomitant bacterial infections compared to those without (83% vs 32.1%). However, this study was unable to address the true burden of fungal infections due to the lack of a mycology laboratory in their hospital.6
Locally, there is a significant gap in information on secondary infections in COVID-19 patients. This lack of knowledge could interfere with the approach to care for hospitalized COVID-19 patients. In our study, we aimed to measure the local prevalence of secondary infections and explore their association with the demographics, clinical course, and mortality rates of hospitalized COVID-19 patients. In addition, the frequency of different bacterial and fungal species detected at different anatomical locations was assessed.
PATIENTS AND METHODS
We conducted this analytical cross-sectional study and assessed a sample of reverse transcriptase polymerase chain reaction (RT-PCR) confirmed COVID-19 patients admitted to King Saud University Medical City (KSUMC), Riyadh, Saudi Arabia during the period from March 2020 to May 2022 (27 months). A minimal sample size of n=236 was calculated using a single proportion formula to sufficiently estimate the prevalence of secondary infections in COVID-19 patients, assuming a confidence interval (CI) of 95% with a 5% margin of error, and a proportion of 19% based on the literature.4 However, the number was increased to n=260 (additional 10%) to allow for possible insufficiencies in the medical records. We used systematic random sampling as our sampling method. We included all COVID-19 RT-PCR positive patients at KSUMC during the study period and selected every fifth patient from a list of positives. Our exclusion criteria were non-hospitalized patients (outpatients) and patients 18 years old and younger. Repeat patient encounters were excluded, only data from the patients’ first visit were considered in our analyses. Ethical approval for this study was granted by the Institutional Review Board (IRB) on December 12, 2022 at King Saud University (IRB number: E-22-7119). The data were collected taking into consideration patient confidentiality.
Data of eligible participants were extracted from their electronic medical records. Extracted data included characteristics related to demographics (age, gender, nationality, body mass index ([BMI] and comorbidi-ties), ICU admission and duration, duration of hospital stay, patient management (medications, oxygen treatment, and invasive procedures), secondary infection testing results (sample site, secondary infection status, genus and species of the isolated organisms), and mortality.
A secondary infection implies the development of bacterial, fungal, or viral infections during the course of COVID-19 infection.4 Secondary infections are further categorized based on their time of detection: A co-infection implies the development of secondary infections during the course of COVID-19 infection and <48 hours from hospital admission, while identification of those infections ≥ 48 hours from admission is considered superinfection.4 Eleven invasive procedures were considered during data collection: nasogastric tube insertion, urine catheterization, chest tube placement, lumbar puncture, peripheral/central line/intraosseous line placement, pericardiocentesis, needle decompression, tube thoracotomy, intubation, tracheostomy, and blood transfusion.
Data were analyzed using IBM SPSS Statistical software for Windows version 29.0 (IBM Corp., Armonk, N.Y., USA). Descriptive statistics (frequencies, percentages, mean, standard deviation, median and inter-quartile range) were used to describe the categorical and quantitative variables. Bi-variate analysis using the t-test for independent samples was carried out to compare the mean values in relation to the categorical study variables. Also, Pearson's Chi-square test was used to test the association between categorical study and outcome variables. The odds ratio was used as a measure of association between two categorical variables. Where chi-square test (for a 2×2 table) was not applicable, an alternative Fisher's exact test was used. Analyzed data is presented in tabular format. Multivariable logistic regression analysis was carried out to evaluate the association between selected variables (gender, nationality, age, BMI, comorbidities, O2 need, ICU admission, length of stay) and the odds of acquiring secondary infections. Hierarchical logistic regression was used to assess mortality and ICU admission among secondary infected patients. A P of ≤.05 and 95% CI were used to report the statistical significance and precision of the results.
RESULTS
Of 1600 patients selected by systematic random sampling and assessed for eligibility, 1326 were excluded as they were ≤18 years or not hospitalized and 14 were excluded because they were repeat encounters. Therefore, the final sample size was 260.
Table 1 shows the sociodemographic and clinical characteristics of the study sample, which was divided based on secondary infection status. A total of 179/260 (68.8%) patients had secondary infection testing, from which 63/179 (35.2%) had a positive result indicating a microbiologically confirmed secondary infection. Most patients, 202/260 (77.7%), were national patients and 150 (57.7%) were females. The mean (SD) age of the study sample was 54.8 (18.5) years. Of the 260 admitted COVID-19 patients, only 3.8% were free of underlying medical conditions. The mean (SD) BMI of the study sample was 36.7 (7.3) and patients with secondary infections did not differ significantly in BMI from those with COVID-19 only.
Table 1.
Sociodemographic and clinical characteristics of hospitalized COVID-19 patients (n=260).
| All patients | Frequency | P value | ||
|---|---|---|---|---|
| COVID-19 only n=197 (75.8%) | Secondary infection n=63 (24.2%)a | |||
| Gender | ||||
| Male | 110 (42.3) | 83 (42.1) | 27 (42.9) | .919 |
| Female | 150 (57.7) | 114 (57.9) | 36 (57.1) | |
| Nationality | ||||
| National | 202 (77.7) | 149 (75.6) | 53 (84.1) | |
| Non-national | 58 (22.3) | 48 (24.4) | 10 (15.9) | .159 |
| Age category | ||||
| 19–30 | 27 (10.4) | 25 (12.7) | 2 (3.2) | |
| 31–40 | 45 (17.3) | 40 (20.3) | 5 (7.9) | |
| 41–50 | 38 (14.6) | 28 (14.2) | 10 (15.9) | |
| 51–60 | 47 (18.1) | 34 (17.3) | 13 (20.6) | |
| >60 | 103 (39.6) | 70 (35.5) | 33 (52.4) | .016 |
| Underlying medical condition: | ||||
| None | 10 (3.8) | 10 (3.8) | – | .125 |
| Cardiovascular disease | 143 (55) | 97 (49.2) | 46 (73) | .001 |
| Diabetes mellitus | 123 (47.3) | 81 (41.1) | 42 (66.7) | <.0001 |
| Respiratory disease | 29 (11.2) | 21 (10.7) | 8 (12.7) | .655 |
| Chronic kidney disease | 38 (14.6) | 21 (10.7) | 17 (27) | .001 |
| Stroke | 19 (7.3) | 9 (4.6) | 10 (15.9) | .005 |
| Pregnancy | 18 (6.9) | 17 (8.6) | 1 (1.6) | .083 |
| Immunocompromised | 6 (2.3) | 5 (2.5) | 1 (1.6) | .999 |
| Overweight (BMI:25-29.9) | 10 (3.8) | 10 (5.1) | – | .125 |
| Obese (BMI:30-34.9) | 131 (50.4) | 97 (37.3) | 34 (54) | .513 |
| Extremely obese (BMI>34.9) | 87 (33.5) | 66 (33.5) | 21 (33.3) | .980 |
| Prescribed medication (n=341)b | ||||
| None | 43 (16.5) | 42 (21.3) | 1 (1.6) | <.0001 |
| Antibiotics | 138 (53.1) | 123 (62.4) | 60 (95.2) | <.0001 |
| Antiviral | 8 (3.1) | 6 (3.2) | 2 (3.2) | .999 |
| Immunosuppressant | 128 (49.2) | 85 (43.1) | 43 (68.3) | .001 |
| Antifungal | 14 (5.4) | 4 (2) | 10 (15.9) | <.0001 |
| VTE prophylaxis | 119 (45.8) | 81 (41.1) | 38 (60.3) | .008 |
| Oxygen | ||||
| Yes | 148 (56.9) | 100 (50.8) | 48 (76.2) | |
| No | 112 (43.1) | 97 (49.2) | 15 (23.8) | <.0001 |
| Invasive procedures | ||||
| Yes | 135 (51.9) | 85 (43.1) | 50 (79.4) | |
| No | 125 (48.1) | 112 (56.9) | 13 (20.6) | <.0001 |
| ICU admission | ||||
| Yes | 74 (28.5) | 34 (17.3) | 40 (63.3) | |
| No | 186 (71.5) | 163 (82.7) | 23 (36.5) | <.0001 |
| Care status | ||||
| Deceased | 42 (16.2) | 12 (6.1) | 30 (47.6) | |
| Discharged | 216 (83.1) | 183 (92.9) | 33 (52.4) | |
| Still hospitalized | 2 (0.77) | 2 (1) | – | <.0001 |
| Vaccination statusc | ||||
| Yes | 32 (12.3) | 19 (13.2) | 13 (30.2) | |
| No | 154 (59.2) | 108 (75.0) | 46 (107.0) | <.0001 |
| BMI (kg/m2)d | 30.67 (7.33) | 30.60 (7.73) | 30.90 (5.99) | NA |
| Hospital stay (days)e | 10.0 (14.0) | 7.0 (10.0) | 21.0 (27.0) | NA |
| ICU stay (days)f | 0 (2) | 0 (0) | 6 (20) | NA |
Categorical variables are reported as numbers (percentages) and continuous variables are reported as median IQR for hospital and ICU stay, or mean (SD) for BMI.
Table shows an overall secondary infection rate of 24.2%. However, only 68.8% of patients had secondary infection testing, of which 35.2% were positive.
Number of prescribed medications for all 260 patients;
3.5% missing data;
Missing data of 53/197 (27%) for COVID-19 only patients and 20/63 (31.7%) for secondary infected due to undocumented vaccination status in their medical charts;
Excluding 2 extreme values: Patient 1 still admitted (1119 days) & Patient 2 (120 days);
For 260 patients with data on ICU stay.
Management of 138 patients consisted of empirical antibacterial therapy. Only 16.5% of the 260 admitted patients did not receive antibiotics, antivirals, antifungals, nor VTE prophylaxis (P<.0001) and 43.1% did not have oxygen therapy during their stay (P<.0001). Distribution of most frequently prescribed antibiotics were as follows: total ceftriaxone, n=102 (75.5% for COVID-19 only patients); total azithromycin, n=92 (77.2% for COVID-19 only patients); total vancomycin n=52 (69.2% for secondarily infected patients), total piperacillin/tazobactam n=50 (56% for COVID-19 only patients), and total meropenem n=37 (70.3% for secondarily infected patients). As for antifungals, micafungin was prescribed in 8 secondarily infected patients (100%). Use of nystatin, fluconazole, amphotericin b, and voriconazole were also reported (n=4,3,1,1 respectively). Moreover, invasive management procedures were performed on approximately half (51.9%) of the study sample (P<.0001). Invasive procedures were analyzed from those secondarily infected with relation to their end of care status. Only 14 (22.2%) exclusively received non-invasive management and the majority of them, 10/14 (P=.014) were discharged. Urinary catheterization (n=27, 42.9%), nasogastric tube insertion (n=24, 38.1%), and airway management procedures (intubation) (n=20, 31.7%) were the most reported among the 63 secondarily infected patients. All three invasive management procedures were more commonly reported among the 30 secondarily infected patients who eventually died, 56.7%, (P=.046), 56.7%, (P=.009), and 53.3%, (P=.001) respectively.
As shown in Table 1, all secondary infected patients had comorbidities. Compared to those with COVID-19 only, a greater percentage of patients with secondary infections had diabetes mellitus (P<.0001) and cardiovascular diseases (P=.001) such as hypertension, chronic heart disease, and dyslipidemia. The majority of secondarily infected patients had a high BMI, where 34/63 (54%) were obese (BMI: 30-34.9) and 21/63 (33.3%) were extremely obese (BMI>34.9). Among secondarily infected patients (n=63), 60 patients received antibiotics (95.2%), 43 received immunosuppressants (68.3%), and 38 received VTE prophylaxis (60.3%) (P<.0001, P=.001 and P=.008, respectively).
In total, there were 42 (16.2%) deaths in the study. Regarding ICU admission, 63.3% (40/63) of the secondarily infected group were admitted, compared to 17.3% (34/197) of those with only COVID-19. In comparisons of age, BMI, length of hospital and ICU stay, differences were statistically significant between the two groups (P<.01) except for BMI (P=.778).
In the logistic regression analysis, obesity was the most significant predictor for secondary infection acquisition, as obese patients showed a 3.5 fold higher likelihood of acquiring secondary infections than others (odds ratio (OR)=3.5 ([95% CI: 1.09-11.5]) (Table 2). Secondary infection status was significantly associated with ICU admission, invasive procedure receival, and mortality rates (Table 3). All three outcomes of interest showed a statistically significant difference between the two groups (P<.001) each. Odds of ICU admission among patients with a secondary infection was 8.338 times higher ([95% CI: 4.431-15.688]) when compared to those with COVID-19 only. Patients with secondary infections were more likely to have received invasive procedures during their stay (OR=5.068 [95% CI: 2.588-9.925]). Higher mortality was also associated with those with secondary infections (OR=14.015 [95% CI: 6.521-30.121]). Further interpretation using hierarchical logistic regression analysis assessed ICU admission and mortality versus secondary infection status while adjusting for age, co-morbidities and invasive procedures showed that the odds of ICU admission among patients with secondary infections was 5 times higher (OR=5 [95% CI: 2.5-10.0]) and the odds of mortality was 4.9 times higher (OR=4.9 [95% CI: 2.0-12.4]) compared to those without.
Table 2.
Multivariable logistic regression analysis for predictors of acquiring secondary infections.
| Predictors | B | P value | OR | 95% CI | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| Female gender | .40 | .330 | 1.49 | .67 | 3.37 |
| Saudi nationality | .79 | .142 | 2.19 | .77 | 6.26 |
| Age in years | .46 | .266 | 1.58 | .71 | 3.53 |
| Admission age | -.04 | .143 | .96 | .90 | 1.01 |
| BMI | .00 | .082 | 1.00 | .99 | 1.00 |
| Oxygen treatment | -.20 | .692 | .82 | .30 | 2.21 |
| ICU admission | .61 | .231 | 1.83 | .68 | 4.93 |
| Invasive treatment procedures | .58 | .202 | 1.79 | .73 | 4.40 |
| Length of stay | .00 | .673 | 1.00 | 1.00 | 1.01 |
| Co-morbidities | |||||
| Cardiovascular disease | -.17 | .729 | .84 | .31 | 2.24 |
| Diabetes mellitus | .50 | .242 | 1.64 | .71 | 3.78 |
| Respiratory disease | -.08 | .887 | .92 | .29 | 2.90 |
| Chronic kidney disease | .61 | .221 | 1.85 | .69 | 4.92 |
| Stroke | .69 | .284 | 1.99 | .56 | 7.04 |
| Pregnancy | -.88 | .531 | .41 | .03 | 6.52 |
| Immunocompromised | -.38 | .787 | .68 | .04 | 10.87 |
| Others | .16 | .701 | 1.17 | .53 | 2.59 |
| Overweight | .27 | .648 | 1.31 | .41 | 4.25 |
| Obese | 1.26 | .036 | 3.54 | 1.09 | 11.51 |
| Extreme obesity | .39 | .548 | 1.48 | .41 | 5.25 |
Model summary measures: Omnibus test, chi-square=101.986, P>.001; -2 likelihood= 185.951, Nagelkerke R square=.435
Table 3.
Association between secondary infections and outcomes among hospitalized COVID-19 patients (N=260).
| COVID-19 Patients | Total N (%) | X2 value | P value | OR | 95% CI | |||
|---|---|---|---|---|---|---|---|---|
| Without SecondaryInfection n (%) | With Secondary Infection n (%) | Lower | Upper | |||||
| ICU admission (%) | 34 (45.9) | 40 (54.1) | 74 (100) | 50.112 | <.001 | 8.338 | 4.431 | 15.688 |
| Invasive procedures (%) | 85 (63) | 50 (37) | 135 (100) | 25.083 | <.001 | 5068 | 2.588 | 9.925 |
| Mortality rates (%) | 12 (28.6) | 30 (71.4) | 42 (100) | 60.545 | <.001 | 14.015 | 6.521 | 30.121 |
Among the 63 secondarily infected patients, 163 organisms were isolated (Table 4). Most positive samples were collected from urine (34.4%), respiratory sources (31.9%), followed by blood (27%). The lower proportion were isolated from wounds (5.5%), stool (0.6%) and peritoneum (0.6%). Furthermore, 72.4% of the secondarily infected patients with COVID-19 were diagnosed with superinfection, which mostly indicates hospital acquired infections. Bacteria (n=104) were the only organism found in all six sample sites, but mostly from blood, 36.5% (38/104).
Table 4.
Comparison of isolates from different sample sites among co-infected (<48h) and superinfected (≥48h) hospitalized COVID-19 patients (n=163).
| Sample site | Isolate | Co-infection | Superinfection | Total |
|---|---|---|---|---|
| Blood | Virus | 1 | 0 | 1 |
| Bacteria | 12 | 26 | 38 | |
| Fungus | 1 | 4 | 5 | |
| Total | 14 (31.1) | 30 (25.4) | 44 (27) | |
| Respiratory sources a | Virus | 1 | 0 | 1 |
| Bacteria | 5 | 25 | 30 | |
| Fungus | 3 | 18 | 21 | |
| Total | 9 (20) | 43 (36.4) | 52 (31.9) | |
| Urine | Virus | 1 | 0 | 1 |
| Bacteria | 14 | 13 | 27 | |
| Fungus | 1 | 27 | 28 | |
| Total | 16 (35.6) | 40 (33.9) | 56 (34.4) | |
| Wound | Bacteria | 4 | 3 | 7 |
| Fungus | 0 | 2 | 2 | |
| Total | 4 (8.9) | 5 (4.2) | 9 (5.5) | |
| Stool | Bacteria | 1 | 0 | 1 |
| Total | 1 (2.2) | 0 (0.0) | 1 (0.6) | |
| Peritoneum | Bacteria | 1 | 0 | 1 |
| Total | 1 (2.2) | 0 (0.0) | 1 (0.6) | |
| Total | Virus | 3 | 0 | 3 |
| Bacteria | 37 | 67 | 104 | |
| Fungus | 5 | 51 | 56 | |
| Total | 45 (100) | 118 (100) | 163 (100) |
Data are number of cases for each category and percentage.
Respiratory sources include sputum, lung lavage, nasal, and endotracheal tubes aspirate.
We found that 60/145 (41.4%) of the most common secondary infecting organisms were gram-negative bacteria (Table 5). Additionally, we found that Candida albicans was the most frequently infecting organism, followed by Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. Further, our analysis showed that between the most frequent organisms, 13 were bacterial, 6 fungal and 1 viral. A total of 18/163 (11.04%) organisms were not included in Table 5, as they were only isolated once 1/163 (0.6%); these organisms include: Streptococcus anginosus, Staphylococcus capitis, Coryneform bacteria, Morganella morganii, Staphylococcus haemolyticus, Serratia ficaria, Leuconostoc lactis, Staphylococcus intermedius, Citrobacter freundii, Candida kefyr, Candida auris, Klebsiella aerogenes, Clostridium difficile, Aspergillus flavus, Pantoea agglomerans, Pseudomonas fluorescens, Trichosporon asahii and Lactobacillus species.
Table 5.
Most frequently isolated organisms from secondary infections of hospitalized COVID-19 patients (n=145).
| Most common organisms | Total | Sample sites (number of isolations) |
|---|---|---|
| Candida albicans | 28 (17.2) | Respiratory (15), Urine (10), Blood (2), Wound (1) |
| Escherichia coli | 15 (9.2) | Urine (11), Blood (2), Respiratory (1), Wound (1) |
| Klebsiella pneumoniae | 15 (9.2) | Urine (7), Respiratory (3), Blood (5) |
| Pseudomonas aeruginosa | 15 (9.2) | Respiratory (7), Urine (4), Blood (3), Wound (1) |
| Staphylococcus epidermidis | 13 (8.0) | Blood (12), Peritoneum (1) |
| Candida tropicalis | 8 (4.9) | Urine (7), Blood (1) |
| Staphylococcus hominis | 7 (4.3) | Blood (7) |
| Candida glabrata | 6 (3.7) | Urine (6) |
| Candida parapsilosis | 6 (3.7) | Respiratory (3), Urine (1), Blood (2) |
| Stenotrophomonas maltophilia | 5 (3.1) | Respiratory (5) |
| Enterococcus faecalis | 5 (3.1) | Urine (3), Blood (1), Wound (1) |
| Acinetobacter baumannii | 4 (2.5) | Respiratory (4) |
| Staphylococcus aureus | 3 (1.8) | Respiratory (3) |
| Cytomegalovirus | 3 (1.8) | Blood (1), Urine (1), Respiratory (1) |
| Proteus mirabilis | 2 (1.2) | Respiratory (1), Wound (1) |
| Aeromonas hydrophila | 2 (1.2) | Wound (2) |
| Candida lusitaniae | 2 (1.2) | Urine (1), Respiratory (1) |
| Candida dubliniensis | 2 (1.2) | Urine (1), Respiratory (1) |
| Burkholderia cepacia | 2 (1.2) | Blood (1), Respiratory (1) |
| Non-tuberculous Mycobacterium | 2 (1.2) | Respiratory (2) |
| Total N (%) | 145 (88.9) | - |
Data are number of isolates for each category and percentage.
Bacteria were the most frequently isolated organisms and were most frequently isolated from blood (23.3%), respiratory sources (18.4%) and urine (16.6%) (Figure 1). Furthermore, fungal infections came second to bacterial infections, and were mostly detected in urine (17.2%), followed by respiratory sources (12.9%), blood (3.1%) and wound (1.2%). The least detected organisms were viruses, which were found to be 0.6% at each of blood, respiratory sources and urine.
Figure 1.
Percentage of isolated organisms from secondary infected COVID-19 patients at different body sites (n=163).
DISCUSSION
Amidst the increasing recognition of secondary infections associated with previous viral pandemics like Influenza A,10,11 we report a secondary infection prevalence of 24.2% with a predominance of isolates detected ≥48 hrs after admission, suggesting the existence of hospital-acquired superinfections among COVID-19 patients. Studies showed a varied prevalence of secondary infections (7.2% in Spain, 1.6% on admission and 5.5% within 48 hours in England, and 3.95% in China).12–14 Local studies conducted in Ha’il and Medina reported an incidence relatively closer to ours than international numbers (36% and 71%, respectively).15,16 Hospital-acquired superinfections were reported more than co-infections in multiple other articles.9,12,14,15,17
In our study, there was a significant difference in duration of stay between COVID-19 only vs. secondarily infected COVID-19 patients. Our results are in line with multiple studies, which showed that the length of hospital stay increased exponentially both locally (16.2 vs. 35.2 days), and internationally where the duration of ICU stay was 6.7 vs. 13.8 days, and hospital stay was 11.8 vs. 18.6 days.9,17
The majority of COVID-19 patients requiring hospitalization at our center had comorbidities, whereas only 3.8% were free of underlying medical conditions upon admission. Cardiovascular diseases were the most prevalent comorbidities with a cumulative proportion of 55%. Underlying comorbidities dampen innate and adaptive immunity and hence increase susceptibility to secondary infections.18,19 This was noticed in our study where 73% of those with secondary infections had cardiovascular diseases, 66.7% had diabetes mellitus, and 54% were obese.
Similarly, secondary infections were appreciable among the older age groups. It is noteworthy to mention that the mean age of 63.0 years for secondarily infected patients was 7.4 years greater than those with COVID-19 only (t=2.18, P<.005). The mean age of our study sample of 54.8 years is consistent with an international mean (SD) age of 58.1 (7.24),4 and a national mean (SD) of 59.6 (18.3) years reported in a nationwide retrospective study.20 However, a multicenter study including all COVID-19 patients across Saudi Arabia reported a mean age of 36 years.21 This difference might be attributed to the inclusion criteria and sample size.
As for patient management, 53.1% of the study population received empirical antibacterial therapy, while only 16.5% of patients did not receive antibiotics nor VTE prophylaxis during their hospitalization. Among those with secondary infections, 95.2% received antibiotics, 68.3% received immunosuppressants and 60.3% had VTE prophylaxis. Our results correlate with other studies on ICU patients. In a retrospective cohort study from England, 94.9% of patients received antibiotics during admission.13 A case control study conducted at Northeastern Ohio and Florida showed that 83.2% of critically ill COVID-19 patients received antibiotics, and that a higher rate of corticosteroids and tocilizumab were administered to patients with co-infections compared to patients without co-infections, 66.1% vs. 56.8% (P=.013) and 22.7% vs. 13.2% (P=.001), respectively.17 An updated literature review notes that rather than evidence-based estimation of the risk of co-infection or superinfection, the present practice of empirical antibiotic use in COVID-19 appears to be based on the risk of co-infection with influenza virus. However, empirical antimicrobial agents have been useful in high-risk patients such as severely ill and immunocompromised.22
Our study found that 63.3% of the patients with secondary infections needed to be admitted to the ICU. Significantly, 54.1% of those admitted to the ICU were had secondary infections, which was probably associated with mechanical ventilation and urinary catheter use. This falls within the reported range of ICU-limited studies conducted in Abha (53%) and Khobar (42.4%) but is incompatible with an incidence of 7.1% reported in another local study.9,23,24 Moreover, 51.9% of our study sample were managed by invasive procedures and 37% had secondary infections. Furthermore, 79.4% of the patients with secondary infections underwent invasive procedures, which is viewed as a moderately high proportion according to the literature.5 Longer hospital stays in our secondarily infected patients may have contributed to this high proportion. This is in line with an international study, reporting a higher proportion of invasive procedures for co-infected ICU patients only.17
Mortality among those with secondary infection was higher than those with COVID-19 only at 47.6% and 6.1%, respectively. A study conducted in Spain reported a mortality rate of 57.1%, (P=.033) in secondarily infected patients.25 According to another study with adjusted odds, patients with co-infections had a 5.92 ([95% CI 3.21-10.91]) risk of death.26 However, we found a greater odds of mortality among those with secondary infections (OR=14.015 [95% CI: 6.521-30.121]).
Despite the implementation of standard of care infection control precautions such as personal protective equipment, appropriate patient placement and environmental disinfection at the hospital, 72.4% of the secondarily infected patients with COVID-19 were diagnosed ≥48 hours after admission, which mostly indicates hospital-acquired superinfection. This is consistent with other previous reports.9,12,14,15,17 We believe this may be due to the overall risk of superinfection for hospitalized COVID-19 patients.
Furthermore, only one patient was positive for a viral infection (CMV) detected from three different sources: blood, respiratory tract, and urine. Our findings of low detection of viral respiratory pathogens could be due to testing practices mainly focusing on COVID-19 during peaks of the pandemic and masking and distancing measures in place in Saudi Arabia during most of the study period. Our results are supported by a previous study that reported a low incidence of community-acquired respiratory viral co-infections in hospitalized COVID-19 patients.12 Several studies have reported that most secondary infections were detected in the bloodstream and the respiratory tract.12,27,28 However, according to both our study and an international study, secondary infections were commonly detected in the urine and respiratory tract.17 Moreover, our results are concordant with a recent study that found that the majority of bacteria were isolated from blood compared to other sites.9
From our study results, the most common organism was Candida albicans at 17.2%, which is consistent with many studies in the literature.11,14,29–31 An study in Chile found a 2.8% prevalence of Klebsiella species,32 whereas other studies reported a significantly higher prevalence of Klebsiella species.9,13,28,33 These reports suggest that gram-negative bacteria were more dominant than gram-positives, which is compatible with our results.9,28,30,31 Two international studies done in China and Indonesia revealed Acinetobacter baumannii as one of the most frequently isolated bacteria, in comparison to our study which revealed a 2.5% prevalence only. Although the China study had a similar population to ours, which included all hospitalized, the one in Indonesia studied ICU patients only.14,30
Our study highlighted the detrimental impact of secondary infections on the clinical outcomes of hospitalized COVID-19 patients. Our results draw attention to age and comorbidities as factors further increasing patient susceptibility to secondary infections. Knowing associated characteristics will help healthcare systems tackle the problem by developing a timely, accurate diagnostic approach.
The principal limitation of our single-center study is its small sample size and retrospective design and limited generalizability. The prevalence of secondary infections is affected by many factors including testing practices and empiric antimicrobial use. For instance, not all patients were initially tested; hence the detection of pathogens later during admission might have been influenced by empirical antibiotic use. An additional limiting factor was the reliance on culture-dependent techniques prone to false positives (due to contamination or colonization) and false negative results. Furthermore, missing data related to vaccination status limited our ability to assess its effect on acquiring secondary infections and outcomes among our patient population.
In conclusion, secondary infections were commonly reported among hospitalized COVID-19 patients at KSUMC during our study period. The development of secondary infections was more common among those with underlying chronic illness, ICU admission, and in patients undergoing invasive management procedures. Candida albicans, Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa were the most isolated organisms identified in our study. The burden of secondary infections during the COVID-19 pandemic within our sample was characterized by prolonged hospital and ICU stay, frequent ICU admission, and high mortality among secondarily infected patients. It is of vital importance to consider the detrimental impact of secondary infections reported in COVID-19 patients to strengthen the management guidelines of all viral outbreaks. Hence subsequent prospective multi-center studies are recommended to strengthen the evidence and hopefully direct management guidelines of future viral outbreaks.
ACKNOWLEDGMENT
The authors gratefully acknowledge Rahmah A. Alzahrani for her contributions to the manuscript.
Funding Statement
None
REFERENCES
- 1.Barry M, AlMohaya A, AlHijji A, Akkielah L, AlRajhi A, Almajid F, et al. Clinical Characteristics and Outcome of Hospitalized COVID-19 Patients in a MERS-CoV Endemic Area. J Epidemiol Glob Health. 2020. Sep;10(3):214–221. doi: 10.2991/jegh.k.200806.002. PMID: 32954712; PMCID: PMC7509106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Barry M, Alotaibi M, Almohaya A, Aldrees A, AlHijji A, Althabit N, et al. Factors associated with poor outcomes among hospitalized patients with COVID-19: Experience from a MERS-CoV referral hospital. J Infect Public Health. 2021. Nov;14(11):1658–1665. doi: 10.1016/j.jiph.2021.09.023. Epub 2021 Oct 1. PMID: 34627061; PMCID: PMC8485705. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Alguwaihes AM, Al-Sofiani ME, Megdad M, Albader SS, Alsari MH, Alelayan A, et al. Diabetes and Covid-19 among hospitalized patients in Saudi Arabia: a single-centre retrospective study. Cardiovasc Diabetol. 2020. Dec 5;19(1):205. doi: 10.1186/s12933-020-01184-4. PMID: 33278893; PMCID: PMC7718833. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Musuuza JS, Watson L, Parmasad V, Putman-Buehler N, Christensen L, Safdar N.. Prevalence and outcomes of co-infection and superinfection with SARS-CoV-2 and other pathogens: A systematic review and meta-analysis. PLoS One [Internet]. 2021;16(5):e0251170. doi: 10.1371/journal.pone.0251170. PMID: 33956882; PMCID: PMC8101968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Feldman C, Anderson R.. The role of co-infections and secondary infections in patients with COVID-19. Pneumonia (Nathan) [Internet]. 2021;13(1):5. doi: [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.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 [Internet]. 2022;12:784130. doi: 10.3389/fcimb.2022.784130. PMCID: PMC8904895; PMID: 35281440 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Morris DE, Cleary DW, Clarke SC.. Secondary bacterial infections associated with influenza pandemics. Front Micro-biol [Internet]. 2017;8:1041. doi: 10.3389/fmicb.2017.01041. PMID: 28690590; PMCID: PMC5481322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sharifipour E, Shams S, Esmkhani M, Khodadadi J, Fotouhi-Ardakani R, Koohpaei A, et al. Evaluation of bacterial co-infections of the respiratory tract in COVID-19 patients admitted to ICU. BMC Infect Dis [Internet]. 2020;20(1):646. doi: 10.1186/s12879-020-05374-z. PMID: 32873235; PMCID: PMC7461753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Alqahtani A, Alamer E, Mir M, Alasmari A, Alshahrani MM, Asiri M, et al. Bacterial coinfections increase mortality of severely ill COVID-19 patients in Saudi Arabia. Int J Environ Res Public Health [Internet]. 2022;19(4):2424. doi: 10.3390/ijerph19042424. PMID: 35206609; PMCID: PMC8871991. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Arnold FW, Fuqua JL.. Viral respiratory infections: a cause of community-acquired pneumonia or a predisposing factor? Curr Opin Pulm Med. 2020. May;26(3):208–214. doi: 10.1097/MCP.0000000000000666. PMID: 32068577. [DOI] [PubMed] [Google Scholar]
- 11.Brundage JF, Shanks GD.. Deaths from bacterial pneumonia during 1918-19 influenza pandemic. Emerg Infect Dis. 2008. Aug;14(8):1193–9. doi: 10.3201/eid1408.071313. PMID: 18680641; PMCID: PMC2600384. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Garcia-Vidal C, Sanjuan G, Moreno-García E, Puerta-Alcalde P, Garcia-Pouton N, Chumbita M, et al. COVID-19 Researchers Group. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study. Clin Microbiol Infect. 2021. Jan;27(1):83–88. doi: 10.1016/j.cmi.2020.07.041. Epub 2020 Jul 31. PMID: 32745596; PMCID: PMC7836762. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Baskaran V, Lawrence H, Lansbury LE, Webb K, Safavi S, Zainuddin NI, et al. Co-infection in critically ill patients with COVID-19: an observational cohort study from England. J Med Microbiol. 2021. Apr;70(4):001350. doi: 10.1099/jmm.0.001350. PMID: 33861190; PMCID: PMC8289210. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lv Z, Cheng S, Le J, Huang J, Feng L, Zhang B, et al. Clinical characteristics and co-infections of 354 hospitalized patients with COVID-19 in Wuhan, China: a retrospective cohort study. Microbes Infect. 2020. May-Jun;22(4-5):195–199. doi: 10.1016/j.micinf.2020.05.007. Epub 2020 May 18. PMID: 32425649; PMCID: PMC7233257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Said KB, Alsolami A, Moussa S, Alfouzan F, Bashir AI, Rashidi M, et al. COVID-19 Clinical Profiles and Fatality Rates in Hospitalized Patients Reveal Case Aggravation and Selective Co-Infection by Limited Gram-Negative Bacteria. Int J Environ Res Public Health. 2022. Apr 26;19(9):5270. doi: 10.3390/ijerph19095270. PMID: 35564665; PMCID: PMC9101447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Alosaimi B, Naeem A, Hamed ME, Alkadi HS, Alanazi T, Al Rehily SS, et al. Influenza co-infection associated with severity and mortality in COVID-19 patients. Virol J. 2021. Jun 14;18(1):127. doi: 10.1186/s12985-021-01594-0. PMID: 34127006; PMCID: PMC8200793. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Orsini EM, Sacha GL, Han X, Wang X, Duggal A, Rajendram P.. Risk factors associated with development of coinfection in critically Ill patients with COVID-19. Acute Crit Care. 2022. Aug;37(3):312–321. doi: 10.4266/acc.2022.00136. Epub 2022 Aug 29. PMID: 36102003; PMCID: PMC9475158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Santos AP, Gonçalves LC, Oliveira ACC, Queiroz PHP, Ito CRM, Santos MO, et al. Co-Infection in Patients with COVID-19 Hospitalized (ICU and Not ICU): Review and Meta-Analysis. Antibiotics (Basel). 2022. Jul 4;11(7):894. doi: 10.3390/antibiotics11070894. PMID: 35884147; PMCID: PMC9312179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Lai CC, Wang CY, Hsueh PR.. Co-infections among patients with COVID-19: The need for combination therapy with non-anti-SARS-CoV-2 agents? J Microbiol Immunol Infect. 2020. Aug;53(4):505–512. doi: 10.1016/j.jmii.2020.05.013. Epub 2020 May 23. PMID: 32482366; PMCID: PMC7245213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Al-Otaiby M, Almutairi KM, Vinluan JM, Al Seraihi A, Alonazi WB, Qahtani MH, et al. Demographic Characteristics, Comorbidities, and Length of Stay of COVID-19 Patients Admitted Into Intensive Care Units in Saudi Arabia: A Nationwide Retrospective Study. Front Med (Lausanne). 2022. Jul 13;9:893954. doi: 10.3389/fmed.2022.893954. PMID: 35911421; PMCID: PMC9325959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Aleanizy FS, Alqahtani FY, Alanazi MS, Mohamed RAEH, Alrfaei BM, Alshehri MM, et al. Clinical characteristics and risk factors of patients with severe COVID-19 in Riyadh, Saudi Arabia: A retrospective study. J Infect Public Health. 2021. Sep;14(9):1133–1138. doi: 10.1016/j.jiph.2021.07.014. Epub 2021 Jul 28. PMID: 34343963; PMCID: PMC8317445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Omoush SA, Alzyoud JAM.. The Prevalence and Impact of Coinfection and Super-infection on the Severity and Outcome of COVID-19 Infection: An Updated Literature Review. Pathogens. 2022. Apr 7;11(4):445. doi: 10.3390/pathogens11040445. PMID: 35456120; PMCID: PMC9027948. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Alshrefy AJ, Alwohaibi RN, Alhazzaa SA, Almaimoni RA, AlMusailet LI, AlQahtani SY, et al. Incidence of Bacterial and Fungal Secondary Infections in COVID-19 Patients Admitted to the ICU. Int J Gen Med. 2022. Sep 24;15:7475–7485. doi: 10.2147/IJGM.S382687. PMID: 36187162; PMCID: PMC9518678. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Alharthy A, Aletreby W, Faqihi F, Balhamar A, Alaklobi F, Alanezi K, et al. Clinical Characteristics and Predictors of 28-Day Mortality in 352 Critically Ill Patients with COVID-19: A Retrospective Study. J Epidemiol Glob Health. 2021. Mar;11(1):98–104. doi: 10.2991/jegh.k.200928.001. Epub 2020 Oct 3. PMID: 33095982; PMCID: PMC7958266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Soriano MC, Vaquero C, Ortiz-Fernández A, Caballero A, Blandino-Ortiz A, de Pablo R.. Low incidence of co-infection, but high incidence of ICU-acquired infections in critically ill patients with COVID-19. J Infect. 2021. Feb;82(2):e20–e21. doi: 10.1016/j.jinf.2020.09.010. Epub 2020 Sep 19. PMID: 32956729; PMCID: PMC7501527. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Aydemir O, Aydemir Y, Şahin EÖ, Şahin F, Koroglu M, Erdem AF.. Secondary bacterial infections in patients with coronavirus disease 2019-associated pneumonia. Rev Assoc Med Bras (1992). 2022. Feb;68(2):142–146. doi: 10.1590/1806-9282.20210745. PMID: 35239872. [DOI] [PubMed] [Google Scholar]
- 27.Gerver SM, Guy R, Wilson K, Thelwall S, Nsonwu O, Rooney G, et al. National surveil-lance of bacterial and fungal coinfection and secondary infection in COVID-19 patients in England: lessons from the first wave. Clin Microbiol Infect. 2021. Nov;27(11):1658–1665. doi: 10.1016/j.cmi.2021.05.040. Epub 2021 Jun 8. PMID: 34481722; PMCID: PMC8186130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Bazaid AS, Barnawi H, Qanash H, Alsaif G, Aldarhami A, Gattan H, et al. Bacterial Coinfection and Antibiotic Resistance Profiles among Hospitalised COVID-19 Patients. Microorganisms. 2022. Feb 23;10(3):495. doi: 10.3390/microorganisms10030495. PMID: 35336071; PMCID: PMC8955474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Hughes S, Troise O, Donaldson H, Mughal N, Moore LSP.. Bacterial and fungal coinfection among hospitalized patients with COVID-19: a retrospective cohort study in a UK secondary-care setting. Clin Micro-biol Infect. 2020. Oct;26(10):1395–1399. doi: 10.1016/j.cmi.2020.06.025. Epub 2020 Jun 27. PMID: 32603803; PMCID: PMC7320692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Asmarawati TP, Rosyid AN, Suryantoro SD, Mahdi BA, Windradi C, Wulaningrum PA, et al. The clinical impact of bacterial co-infection among moderate, severe and critically ill COVID-19 patients in the second referral hospital in Surabaya. F1000Res. 2021. Feb 15;10:113. doi: 10.12688/f1000research.31645.2. PMID: 33868645; PMCID: PMC8030114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Senok A, Alfaresi M, Khansaheb H, Nassar R, Hachim M, Al Suwaidi H, et al. Coinfections in Patients Hospitalized with COVID-19: A Descriptive Study from the United Arab Emirates. Infect Drug Resist. 2021. Jun 21;14:2289–2296. doi: 10.2147/IDR.S314029. PMID: 34188495; PMCID: PMC8232897. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ceballos ME, Nuñez C, Uribe J, Vera MM, Castro R, García P, et al. Secondary respiratory early and late infections in mechanically ventilated patients with COVID-19. BMC Infect Dis. 2022. Sep 29;22(1):760. doi: 10.1186/s12879-022-07743-2. PMID: 36175841; PMCID: PMC9521562. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Mahmoudi H. Bacterial co-infections and antibiotic resistance in patients with COVID-19. GMS Hyg Infect Control. 2020. Dec 17;15:Doc35. doi: 10.3205/dgkh000370. PMID: 33391970; PMCID: PMC7747008. [DOI] [PMC free article] [PubMed] [Google Scholar]