Abstract
Objectives
To compare the occurrence of healthcare-associated infections acquired in intensive care units (HAI-ICUs) in France among patients with COVID-19 and those without it in 2020 and the latter with that in patients before the COVID-19 pandemic.
Methods
Multicentre HAI-ICU surveillance network (REA-REZO) data were used to identify 3 groups: 2019 patients (2019Control), a COVID-19 group (2020Cov), and a non–COVID-19 group (2020NonCov). The primary outcome was the occurrence of HAI-ICU (ventilator-associated pneumonia [VAP], bloodstream infections [BSIs], catheter-related bacteraemia). Standardized infection ratios of VAP were calculated for each quarter in 2020 and compared with those in 2019.
Results
A total of 30 105 patients were included in 2020: 23 798 in the 2020NonCov group, 4465 in 2020Cov group, and 39 635 patients in the 2019Control group. The frequency of VAP was strikingly greater in the 2020Cov group: 35.6 (33.4–37.8) episodes/1000 days of mechanical ventilation versus 18.4 (17.6–19.2) in the 2020NonCov group. VAP standardized infection ratio was high in 2020 patients, particularly during the 2 quarters corresponding to the 2 waves. BSI/1000 days were more frequent in the 2020Cov group (6.4% [6.4–6.4%] vs. 3.9% [3.8–3.9%] in the 2020NonCov group). VAP and BSI were also more frequent in the 2020NonCov group than in the 2019Control group. The microbial epidemiology was only slightly different.
Discussion
The data presented here indicate that HAI-ICUs were more frequent during the COVID-19 period, whether the patients were admitted for COVID-19 or, to a lesser extent, for another cause. This implies that managing patients with severe disease in a pandemic context carries risks for all patients.
Keywords: COVID-19, Hospital-acquired infections, Intensive care, Surveillance network, Ventilator-associated pneumonia
Introduction
In France, and worldwide, intensive care units (ICUs) have been the main battleground to treat patients with severe COVID-19 that is associated with a high risk of death [1]. The pandemic had a major effect on the hospital organization, with work overload, creation of temporary beds in ICUs, involvement of personnel not usually dedicated to ICUs, and an initial shortage of personal protective equipment [2]. This situation was further complicated by the continuous flow of patients in ICU without COVID-19. REA-REZO is a nationally active surveillance network dedicated to the epidemiology of ICU-acquired infections as well as the use of antimicrobials and bacterial epidemiology running since 2004 in voluntary French ICUs [[3], [4], [5]]. During the COVID-19 pandemic period, the ongoing surveillance programme was continued on the same basis with the identification of patients with COVID-19. The objective of the study was to compare first the occurrence of healthcare-associated infections acquired in ICUs (HAI-ICUs) in 2020 patients with COVID-19 with that in 2020 patients without COVID-19, and secondly the latter with that in 2019 patients before the COVID-19 pandemic.
Methods
Surveillance design
The continuous surveillance is carried out in ICUs on a volontary basis and is patient-based, including each patient with a length of stay ≥2 calendar days in an adult ICU. Individual data were prospectively recorded on HAI, with selected antimicrobial resistance and individual risk factors (Table S1). The database was approved by the National Data Protection Commission (Commission nationale de l'informatique et des libertés, Number 919149) and by the institutional review board (CPP SUD EST—IRB 00009118). The protocol is available on the website of the network [6].
Surveillance data
General patient characteristics
Age, sex, severity as assessed by the Simplified Acute Physiological Score II [7], date of ICU admission and discharge, status at ICU discharge (alive or deceased), antibiotic treatment (excluding prophylaxis) ± 2 days before or after admission day, category of diagnosis (medical, surgical scheduled or emergency, and trauma), origin of the patient (community, long-term care, rehabilitation centre, acute care, and other ICU), and immunosuppression were recorded.
Individual exposure to invasive device
The dates of insertion and removal of the endotracheal tube and central venous catheter (CVC) as well as the site of CVC insertion were recorded.
Healthcare-associated infections acquired in intensive care units
Pneumonia, including ventilator-associated pneumonia (VAP), catheter-related bacteraemia (CRB), as well as bloodstream infections (BSIs) of all origins were recorded. Pulmonary infection data included the date of onset and the method of diagnosis of pneumonia.
For each infection, up to 2 microorganisms were recorded, as well as resistance status by tracer phenotypes for bacteria of interest (Staphylococcus aureus, Enterococcus faecalis and Enterococcus faecium, Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter sp.; (Table S1). Susceptibility testing in all units was performed according to the European Committee on Antimicrobial Susceptibility Testing [8].
Definitions of ICU-acquired infections
HAIs are infections occurring >48 hours after admission. Definitions follow the European Centre for Disease Control definitions [9].
Briefly, pneumonia is defined by a combination of clinical, radiological, and laboratory criteria. VAP is a lung infection in a patient mechanically ventilated for >48 hours. BSIs are defined by the positivity of at least one blood culture for a recognized pathogen or two positive blood cultures for a common skin contaminant. The complete definition set can be found on the European Centre for Disease Control Website [10].
The outcomes include the incidence of HAI expressed as incidence, as well as incidence density per 1000 patient-days for BSI and per 1000 days of device exposure for specific infections (mechanical ventilation for VAP and catheter for CRB).
Surveillance design
The data collection was performed using a standardized form completed for each patient by the physician in charge in collaboration with the Infection Control Unit; the data collected concerns patient characteristics, devices used, and HAI. These data were collected during the ICU stay, and the form was finalised at the end of the ICU stay, for each patient staying 2 or more days.
Statistical plan
Descriptive statistics were expressed by the median and interquartile range for quantitative variables and by the number and percentage (%) for qualitative variables. The device utilization ratio was calculated by dividing the total number of device days by the total number of patient-days during the stay. Differences among the groups were estimated using the Wilcoxon rank-sum test for quantitative variables and the chi-square test for qualitative variables or Fisher exact test when applicable. If heterogeneity among groups was detected, a two-by-two comparison was performed to detect the group differences. The statistical threshold for between-group comparisons was set at 0.001.
Standardized infection ratios (SIRs) were computed as previously described [11]: the proportion of change (%) in VAP incidence was calculated as follows: ([2020 SIR − 2019 SIR] ÷ 2019 SIR) × 100. Temporal comparisons in VAP incidence between 2019 and 2020 were analysed using SIR, calculated for each calendar quarter by dividing the number of reported infections by the number of predicted infections. A SIR <1 indicates fewer infections observed than predicted; likewise, a SIR >1 indicates that more infections were observed than predicted. The predicted individual probability of occurrence of VAP was estimated using logistic regression: first, a backward stepwise regression was performed to select the best minimal model to explain a VAP using a subset of predefined variables (Table S2). The model fit was maximized using the minimal Akaike information criterion. The OR and their 95% CI were computed for the variables retained in the final model. Analyses were performed using the SAS-Studio (SAS Institute Inc., Cary, NC).
Results
Population
The number of participating units was N = 110 in 2019 and N = 90 in 2020. In 2020, 30 105 patients were reported in the surveillance network database: 23 798 patients in the non–COVID-19 (2020NonCov) group; 4465 patients in the COVID-19 (2020Cov) group, including 3800 patients with COVID-19 diagnosed by PCR and 665 on clinical basis (mainly before complete accessibility of PCR early in the year 2020). However, 1842 patients with an unknown COVID-19 status were not included in the present study. In 2019, 39 635 patients were included in the surveillance (Fig. 1 ).
Fig. 1.
Flowchart. 2020NonCov, non–COVID-19; 2020Cov, COVID-19 group.
Patient characteristics
The 2019Control and 2020NonCov patients were comparable, except for the proportion of scheduled surgical patients who were low in the 2020NonCov group. A greater proportion of patients in the 2020Cov group was transferred from a ward or other ICU than patients in the 2020NonCov. According to the number of 2020Cov and 2020NonCov admissions each month in 2020, there were 2 waves in France: the first in March, the second in October-November (Fig. S1). The median length of ICU stays, sex ratios, and fatality rates were greater in the 2020Cov group than in the 2020NonCov group. Exposure to antibiotics was not different between the 2019Control and the 2020NonCov population, but higher in the 2020Cov group than in the 2020NonCov group (Table 1 ).
Table 1.
Characteristics of included patients
| 2019Control |
2020NonCov |
2020Cov |
p | |
|---|---|---|---|---|
| (N = 39 635) | (N = 23 798) | (N = 4465) | ||
| Age (y), median (IQR) | 67.0)56–75) | 66.0 (55–74) | 67.0 (58–74) | NS |
| Sex-ratio M/F | 1.72 | 1.84 | 2.35 | <0.001a,b |
| Length of ICU stay (d), median (IQR) | 6 (4–11) | 6 (4–11) | 10 (6–19) | <0.001a |
| SAPS II, median (IQR) | 44 (32–58) | 43 (32–57) | 38 (30–49) | <0.001a,b |
| ICU case fatality, n (%) | 6498 (16.4) | 3998 (16.8) | 1017 (22.8) | <0.001a |
| Antibiotics ± 48 h around admission, n (%) | 22 184 (56.1) | 13 137 (55.3) | 3098 (69.6) | <0.001a |
| Admission from, n (%) | ||||
| Home | 21 784 (55.1) | 13 809 (58.1) | 1963 (44.0) | <0.001a,b |
| Nursing home | 574 (1.5) | 320 (1.3) | 63 (1.4) | NS |
| Long-term care facility | 782 (1.8) | 202 (0.9) | 66 (1.5) | <0.001a,b |
| Rehabilitation | 626 (1.6) | 305 (1.3) | 42 (0.9) | NS |
| Other wards (acute care) | 14 201 (35.9) | 7892 (33.2) | 1973 (44.2) | <0.001a,b |
| Other ICU | 1653 (4.2) | 1225 (5.2) | 352 (7.9) | <0.001a,b |
| Diagnostic category at admission, n (%) | ||||
| Medical | 26 886 (67.9) | 16 627 (69.9) | 4195 (94.1) | <0.001a,b |
| Emergency surgery | 7160 (18.1) | 4338 (18.2) | 189 (4.2) | <0.001a |
| Scheduled surgery | 5556 (14.0) | 2809 (11.8) | 75 (1.7) | <0.001a,b |
| Trauma, n (%) | 2826 (7.2) | 1817 (7.6) | 155 (3.5) | <0.001a |
| Immunosuppression, n (%) | 5908 (15.3) | 3345 (14.6) | 625 (14.2) | NS |
| Including <500 Neutrophils/mm3, n (%) | 652 (1.7) | 360 (1.6) | 48 (1.1) | NS |
| Device exposure, n (%) | ||||
| Intubation probe | 24 109 (60.9) | 15 131 (63.7) | 2628 (58.9) | <0.001a,b |
| Central venous catheter | 26 706 (67.5) | 17 089 (71.9) | 2935 (65.8) | <0.001a,b |
| Urinary catheter | 33 236 (86.0) | 20 735 (88.1) | 3353 (77.6) | <0.001a,b |
| Exposure duration (d), median (IQR) | ||||
| Mechanical ventilation | 4 (2–10) | 5 (2–11) | 12 (6–22) | <0.001a,b |
| Central venous catheter | 6 (4–12) | 7 (4–12) | 12 (7–22) | <0.001a |
| Device utilization ratio, % | ||||
| Intubation probe | 50.9 | 55.2 | 64.0 | <0.001a,b |
| Central venous catheter | 68.5 | 72.0 | 72.4 | <0.001a,b |
Data are shown as the number of patients n and percentage (%) or median and interquartile range (IQR). Between-group comparisons with significant p value set at 0.001.2020NonCov, non–COVID-19; 2020Cov, COVID-19 group; ICU, intensive care unit; NS, not significant; SAPS II, Simplified Acute Physiological Score II.
Significant difference between 2020Cov and 2020NonCov.
Significant difference between 2020NonCov and 2019Control.
Exposure to invasive devices (endotracheal tube or CVC) was slightly increased in the 2020NonCov group. The exposure duration and the device utilization ratio were higher in the 2020Cov group. During 2020, the proportion of intubated patients decreased among the patients in the 2020Cov group and remained stable in the patients in the 2020NonCov group (Fig. S2). During the same period, the interval between ICU admission and mechanical ventilation increased during the 2 waves (Fig. S3).
Device-related infection rates
Overall rate of HAI-ICU
The overall rate of HAI was higher in the 2020Cov group than in both the 2019Control and 2020NonCov groups. Among the patients in the 2020NonCov group, the increase was partially because of more frequent VAP, but also, at a lesser extent, to more frequent BSI (including CRB) (Table 2 ).
Table 2.
Healthcare-associated infections acquired in intensive care units
| 2019Control 39 635 |
2020NonCov 23 798 |
2020Cov 4465 |
p | |
|---|---|---|---|---|
| Patients with at least one infection, n (%) | 3698 (9.3 [9.04–9.61]) | 2680 (11.3 [10.86–11.66]) | 1160 (26 [24.69–27.27]) | <0.001a,b |
| Pneumonia (including VAP), n (%) | 2852 (7.2 [6.94–7.45]) | 2140 (9 [8.63–9.36]) | 1024 (22.9 [21.70–24.17]) | <0.001a,b |
| VAP, n (%) | 2507 (10.4 [10.01–10.78]) | 1948 (12.9 [12.34–13.41]) | 973 (37 [35.18–37.88]) | <0.001a,b |
| VAP/1000 d of MV | 15.4 (14.78–15.97) | 18.4 (17.62–19.24) | 35.6 (33.42–37.81) | <0.001a,b |
| Interval from MV onset to VAP (d), median (IQR) | 8 (4–12) | 7 (4–12) | 8 (5–12) | <0.001a,b |
| Bloodstream infection | 1271 (3.2 [3.03–3.38]) | 888 (3.7 [3.49–3.97]) | 388 (8.7 [7.86–9.52]) | <0.001a,b |
| Blood stream infection/1000 d of stay | 3.4 (3.33–3.45) | 3.9 (3.84–3.88) | 6.4 (6.36–6.44) | <0.001a |
| Central catheter-related bacteraemia, n (%) | 163 (0.6 [0.52–0.70]) | 118 (0.6 [0.57–0.81]) | 36 (1.2 [0.83–1.62]) | <0.001a |
| Central catheter-related bacteraemia/1000 central catheter d | 0.6 (0.47–0.65) | 0.6 (0.58–0.65) | 0.6 (0.63–0.63) | NS |
Data are shown as the number of patients n and percentage (%) or median and interquartile range (IQR). Between-group comparisons with significant p value set at 0.001.2020NonCov, non–COVID-19; 2020Cov, COVID-19 group; MV, mechanical ventilation; NS, not significant; VAP, ventilator-associated pneumonia.
Significant difference between 2020Cov and 2020NonCov.
Significant difference between 2020NonCov and 2019Control.
VAP
At least 1 episode of VAP was diagnosed in 37% of patients in the 2020Cov group, compared with 12.9% in the 2020NonCov group (Table 2). Logistic regression of the predicted individual probability of VAP is provided in Table S3. The greatest change in VAP SIR was found in the second and fourth quarter for the whole 2020 cohort (2020Cov + 2020NonCov); when only patients in the 2020NonCov were considered, the greatest change was found in the second quarter (Table 3 ).
Table 3.
Standardized infection ratio for VAP
| SIR 2019 (95% CI) | SIR 2020NonCov |
% Change SIR (95% CI) | SIR 2020Cov + 2020NonCov |
% Change SIR (95% CI) | |
|---|---|---|---|---|---|
| Q1 | 0.92 (0.86–0.99) | 0.97 (0.89–1.05) | 4.3 (−0.3; 8.9) | 0.95 (0.88–1.03) | 3.3 (−1.3; 7.9) |
| Q2 | 0.88 (0.81–0.95) | 1.32 (1.23–1.41) | 50.0 (36.1; 63.9) | 1.57 (1.49–1.64) | 78.4 (61.1; 95.8) |
| Q3 | 0.99 (0.92–1.06) | 1.06 (0.97–1.14) | 7.1 (1.9; 12.3) | 1.11 (1.03–1.19) | 12.1 (5.3; 18.9) |
| Q4 | 0.91 (0.84–0.99) | 1.03 (0.94–1.13) | 12.0 (5.2; 18.7) | 1.59 (1.52–1.67) | 74.7 (57.8; 91.7) |
The proportion of change (%) was calculated as follows: ([2020 SIR − 2019 SIR] ÷ 2019 SIR) × 100. Statistical significance based on 2-tailed p ≤ .05, reflected in the relative % change in magnitude. The number of predicted infections was obtained using regression models created from the 2019–2020 baseline data. A SIR <1 indicates fewer infections observed than predicted, signalling a reduction; likewise, a SIR >1 indicates more infections were observed than predicted, signalling an increase. 2020NonCov, non–COVID-19; 2020Cov, COVID-19 group; Q, calendar quarter; SIR, standardized infection ratio; VAP, ventilator-associated pneumonia.
Bloodstream infection
The increase in BSI rate in the 2020Cov group compared with the 2019NonCov group was related to an increase in intra-vascular device origin of infection, particularly from peripheral catheters, but also from pulmonary origin, whereas bacteraemia of digestive origin were less frequent (Table 4).
Table 4.
Origin of bacteraemia of each origin
| 2019Control (N = 1271) | 2020NonCov (N = 1030) | 2020Cov (N = 466) | |
|---|---|---|---|
| Intra-vascular devices n (%) | 404 (27.5) | 38.4 | 35.7 |
| Arterial catheter | 107 (7.3) | 123 (11.9) | 43 (9.2) |
| Peripheral catheter | 38 (2.6) | 81 (7.9) | 48 (10.3) |
| Central venous catheter | 201 (13.7) | 146 (14.2) | 60 (12.9) |
| PICC | 8 (0.5) | 8 (0.8) | 2 (0.4) |
| Haemodialysis catheter | 26 (1.8) | 18 (1.7) | 6 (1.3) |
| Implantable port catheter | 13 (0.9) | 3 (0.3) | 0 (0.0) |
| ECMO | 5 (0.3) | 4 (0.4) | 3 (0.6) |
| Midline | — | 3 (0.3) | 3 (0.6) |
| Other vascular devices | 6 (0.4) | 9 (0.9) | 2 (0.4) |
| Lungs, n (%) | 272 (18.5) | 151 (14.7) | 114 (24.5) |
| Urinary tract, n (%) | 102 (6.9) | 47 (4.6) | 19 (4.1) |
| Digestive tract, n (%) | 242 (16.5) | 109 (10.5) | 26 (5.6) |
| SSI, n (%) | 7 (0.5) | 4 (0.4) | 2 (0.4) |
| Skin and soft tissues infections, n (%) | 57 (3.9) | 24 (2.3) | 3 (0.6) |
| Other origin, n (%) | 15 (1.0) | 9 (0.9) | 5 (1.1) |
| Unknown, n (%) | 369 (25.1) | 291 (28.3) | 130 (27.9) |
2020NonCov, non–COVID-19; 2020Cov, COVID-19 group; ECMO, extracorporeal circulation membrane oxygenation; PICC, peripherally inserted central catheter; SSI, surgical site infection.
Bacterial ecology and resistance
Multidrug resistant bacteria carriage and acquisition
The proportion of patients carrying at least 1 targeted multidrug-resistant bacterium (MDRB) was not significantly different between the 2020NonCov and 2019Control group; it was more frequent in the 2020Cov group than in the 2020NonCov group. This was particularly the case for the acquisition of extended spectrum β-lactamase (ESBL), but also for the initial carriage and acquisition of carbapenemase-producing enterobacterales or ceftazidime-resistant Pseudomonas aeruginosa (CRPa) strains (Table S3).
Microorganisms involved in HAI-ICU and antimicrobial resistance profile
The distribution of the different bacterial species of interest is different between the 2019Control and 2020NonCov groups. In 2020, only modest differences in bacterial ecology were found between the 2020Cov and 2020NonCov groups, except for non-fermenting Gram-negative bacilli that are more frequent in patients in the 2020Cov group (Table S4 and S5).
The proportion of patients infected with an MDRB (all infections combined: pneumonia, BSI and CRB) was greater in both 2020 groups (2020NonCov and 2020Cov) than in the 2019Control group. The proportion of patients infected with methicillin-resistant S. aureus, carbapeneme-resistant enterobacteria, and CRPa was significantly greater in the 2020Cov group than in the 2020NonCov group (Table 5 ). It should be noted that all carbapeneme-resistant enterobacteria were isolated in VAP, but never in catheter associated blood stream infection.
Table 5.
Number of patients infected with a multidrug-resistant bacterium during the study period 2019–2020
| 2019Control 39 635 |
2020NonCov 23 798 |
2020Cov 4465 |
p | |
|---|---|---|---|---|
| Patients with at least one infection, n (%) | 3698 (9.3) | 2669 (11.3) | 1158 (26) | <0.001a,b |
| Patients infected by methicillin resistant Staphylococcus aureus, n (%) | 74 (0.2) | 51 (0.2) | 22 (0.5) | <0.001a |
| Patients infected by extended-spectrum β-lactamase, n (%) | 267 (0.7) | 201 (0.8) | 128 (2.9) | <0.001a |
| Patients infected by carbapenem-resistant enterobacteriae, n (%) | 20 | 14 | 11 | <0.001a |
| Patients infected by ceftazidime-R Pseudomonas aeruginosa, n (%) | 166 (0.4) | 136 (0.6) | 69 (1.5) | <0.001a |
Data are shown as the number of patients (n) and percentage (%) from the total population of included patients. Between-group comparisons with significant p value set at 0.001.2020NonCov, non–COVID-19; 2020Cov, COVID-19 group.
Significant difference between 2020Cov and 2020NonCov.
Significant difference between 2020NonCov and 2019Control.
Discussion
The main result of the present study is that HAI-ICU, particularly VAP, were more frequent in both ICU populations during 2020 than in 2019, regardless of their COVID-19 status. This is because of extrinsic and intrinsic factors. Among the extrinsic factors, the pandemic had a major effect on the hospital and ICU organization [2]. The breakdown in infection prevention best practices is highly likely; however, it is probably variable among countries [12] and units. Moreover, it is not possible to further analyse the responsibility of prevention practice in the present study because this is not recorded in our surveillance programme.
The higher rates of pneumonia and BSI have different determinants. VAP was at least 3 times more frequent in patients in the 2020Cov group than in the 2020NonCov group; such a high rate of VAP has been reported in several multicentre studies, for example in 2 French cohorts, the rate of VAP was 43% [13] and 52% [14], and the main study on HAI-ICU in patients with COVID-19, conducted in Italy, reported a rate of 50% of VAP in intubated patients (26.0 [95% CI, 23.6–28.8] VAP per 1000 mechanical ventilation-days) [15]. In addition, during the 2 years of surveillance, no modification in the diagnostic practice of VAP were found in the different periods and groups (data not shown), and the high SIR of VAP corresponded to the 2 waves of the pandemic in France. The high rate of VAP in the 2020Cov was related to the lung tropism of SARs-CoV-2 and the resulting lung lesions that are particularly exposed to pulmonary super-infections, as shown, for instance, in a comparison between COVID-19 and influenza [16]. Intrinsic factors related to the disease process itself included lung parenchymal damage, immune dysregulation, and an increased risk of thrombosis [17].
A High rate of BSI has also been reported elsewhere [18]. It is related to a more frequent intra-vascular device origin of infection, which could be attributed at least partially to the modification of the management of patients in the ICU [2]. In addition, it is also associated with a more frequent pulmonary origin (related to the more frequent VAP) and a decrease of digestive origin (fewer surgical patients) in patients in the 2020Cov group.
The surveillance data provide information about several interesting characteristics of the patients in the 2020Cov group. The median age of the 2020Cov cohort was similar to the 2020NonCov and the historical 2019Control cohorts, indicating that very old patients were not necessarily admitted to the ICU [19]. In addition, the classical male predominance in patients with COVID-19 was found in the 2020Cov cohort [20]. Mortality was more frequent in the 2020Cov group despite a low severity score (Simplified Acute Physiological Score II) at admission; this could be explained by the early admission of patients in the 2020Cov group to ICU. Furthermore, there was a reduced direct admission to ICU, which is likely to be explained by a more frequent previous admission to a medical ward. Moreover, scheduled surgical activity was reduced in relation to reorientation of ICU beds towards COVID-19 in the European countries [2]. It is also important to note that antibiotic exposure measured around the ICU admission was very high in the 2020Cov group, approximately 70%. This has been well analysed in an editorial by De Waele et al. [21]: possible co-infections, use of immunomodulating medications, such as corticosteroids and interleukin inhibitors, and a longer duration of mechanical ventilation in patients in the 2020Cov group. As the understanding of COVID-19 progressed, the initial overexposure of patients with COVID-19 antibiotics for the fear of bacterial co-infection slightly decreased in our study (data not shown).
MDRB carriage was more frequent in patients in the 2020Cov group, especially among ESBL-producing enterobacterales and CRPa, a result possibly related to a high exposure to antibiotics at admission and during a previous stay in a ward. There was no remarkable difference in the distribution of the most frequent bacteria: enterobacterales were found at the same rate in all groups, and there was a slightly high rate of detection of infections resulting from P. aeruginosa in patients in the 2020Cov group. Furthermore, resistance levels were not very different: for instance, there was less ceftazidime resistance in Pseudomonas isolated from patients in the 2020Cov group, and only ESBL enterobacterales were slightly more frequently isolated from these patients. Taken together, these data suggest that the microbial epidemiology and resistance are not a major problem in SARS-CoV-2 infections.
Strengths and limitations
Surveillance network data from the REA-REZO are of value as a large number of patients are included, and because the network has existed for many years, the quality of the data is high. It also has the advantage of measuring the burden of HAI-ICU in the ICU populations during the pandemic period, showing that patients in the 2020NonCov group were also concerned by the increase in HAI-IC. However, data are limited to the variables collected from the surveillance, not including the comorbidities, such as obesity and the treatments potentially associated with the development of HAIs (i.e. corticosteroids and tocilizumab).
Moreover, we need to point-out that nearly 15% of the patients with COVID-19 were not diagnosed with the use of PCR because of the lack of availability of this method at the beginning of the pandemic. However, the risk of misclassifying these patients is limited by the strict recommendations from the ministry of health and learned medical societies for the case definition.
In conclusion, the data presented here indicate that HAI-ICU were more frequent during the COVID-19 period, whether the patients were admitted to ICU for COVID-19 or another cause. This implies that besides the specific role of COVID-19, particularly in pulmonary super-infection, the high in flow of patients decreases the quality of care provided to all patients, leading to an increased risk of HAI-ICU for all patients.
Author contributions
A.L., A.M., C.B., A.F., and A.S. designed the study. A.M. and C.V. analysed and interpreted the data. A.L. and A.M. wrote the manuscript. A.L., C.B., A.F., C.V., and A.S. revised the manuscript.
Transparency declaration
The authors declare that they have no conflicts of interest.
Acknowledgements
The authors are grateful to Philip Robinson for manuscript editing assistance.
The authors thank the members of the REA-REZO Network: Martin Maelle, Bourigault Céline; Maxime Virginie, Lawrence Christine; Alvarez Antonio, Rohr Laetitia; Le Quoc Viet, Hayo Françoise; Galliot Richard, Farfour Eric; Pugliesi Paul-Simon, Le Coq Muriel; Troche Gilles, Neulier Caroline; Leblanc Pierre-Etienne, Ouzani Souad; Pillot Jérôme, Bordes-Couecou Stéphanie; Le Floch Anne-Sophie, Sechaud Dominique; Launoy Anne, Lavigne Thierry; Alfandari Serge; Bulyez Stephanie; Belin Nicolas, Beilouny Bassam; Ricome Sylvie; Hammad Emmanuelle, Cassir Nadim; Tonnelier Alexandre, Rolland-Jacob Gwenaël; Prelipcean Cristian, Meyer Ella-Pauline; Thiphagne Benoit, Delhomme Joel; Anguel Nadia; Ledochowski Stanislas, Bernerd Cécile; Durand Michel, Landelle Caroline; Saint-Leger Mélanie, Boutreux Sébastien; Le Gall Fanny, Allaire Alexandra; Patrigeon René-Gilles, Callanquin Marie; Bonnivard Michel, Bajolet Odile; Henry Christophe, Maheu Claire; Hausermann Marie, Guignabert Catherine; Hadj-Slimane Fethi, Joron Sylvie; Cornesse Marie-Elisabeth, Manzon Christiane; Moschietto Sébastien, Comparot Sylvie; Knani Lyes, Nizou Jacques-Yves; Robine Adrien, Canu Nathalie; Courouble Patricia, Gallais-Hoff Severine; Kempf Jean, Meunier Olivier; Trusson Rémi, Daurat Aurélien; Muller Céline; Mardrus, Chartier Vanessa; Simon Georges, Beilouny Bassam Yassine Hussein; Clayer Celine; Chopin Fabrice, Picault Sylvie; Berrouba Aziz, Toro Alexandre; Pommier Christian, Haond Catherine; Crombe Christian, Daumas Martine; Corno Gaelle, Van Rossem Vanessa; Martin Audrey, Curnier Véronique; Bouhara Abdenour; Huntzinger Julien, Fedun Yannick; Lescot Thomas, Barbut Frédéric; Picos Gil Santiago, Deffarges Dominique; Hyvernat Hervé, Berrouane Yasmina; Fillatre Pierre, Dupin Clarisse; Bele Nicolas, Fribourg Agnès; Roche Anne-Claude; Galland Claude, Lecoutre Lucie; Petit Jean-Sebastien, Dusseau Jean-Yves; Gauzere Valérie, Pascal Michel; Dulac Thierry, Pina Patrick; Catanese Vincent; Biangoma Sandrine, Quenee Vincent, Chassaing Guillaume; Leon Rusel, Si-Ali Amine; Roche Anne-Claude; Tasle Marine, Dusseau Jean-Yves; Delastre Olivier, Martin Emmanuelle; Roustan Jérôme, Dijols Isabelle; Delanghe Fanny, Brochart Julie; Tagawa Philippe, Cohen Agnès; La Combe Béatrice, Grolier-Bois Liliane; Roques Adrien; Cayuela Marie-Anne; Cortes Esther, Fleurial Véronique; Maindron Aurélie, Bertrand Julie; Beague Sébastien, Durand Joly Isabelle; Gaubert-Duclos Julien, Bonjean Severine; Meziani Ferhat; Gasan Gaelle, Chatelet Céline; Capron Matthieu, Bourzeix Sylvie; Mahul Martin, Birot Jaulin Fabienne, Lier Marie Laure; Gibot Sebastien, Florentin Arnaud; Carbognani Daniel, Venelle Myriam; Djedaini Kamel; Giacardi Christophe, Raposo Olga; Pavillard Frédérique, Grau Delphine; Gourgues Michele, Simac Catherine; Nicolas François, Chausset Robert; Bouillard Philippe, Vasse Laurence; Lepouse Claire, Bajolet Odile; Picard Walter, Larroude Peggy; Vitris Michel, Derramond Fabienne; Barjon Genevieve, Jolibois Boris; Tinturier François, Brochart Julie; Argaud Laurent, Munier-Marion Elodie; Florentin Arnaud; and Vincent Jean-François, Venot Christine.
Editor: L. Scudeller
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.cmi.2022.10.023.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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