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/lp/springer-journals/healthcare-associated-infections-in-dutch-hospitals-during-the-covid-qTrjrksSla
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- Springer Journals
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- Copyright © The Author(s) 2023
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- 2047-2994
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- 10.1186/s13756-022-01201-z
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Abstract
Background During the COVID-19 pandemic hospitals reorganized their resources and delivery of care, which may have affected the number of healthcare-associated infections (HAIs). We aimed to quantify changes in trends in the number of HAIs in Dutch hospitals during the COVID-19 pandemic. Methods National surveillance data from 2016 to 2020 on the prevalence of HAIs measured by point prevalence surveys, and the incidence of surgical site infections (SSIs) and catheter-related bloodstream infections (CRBSIs) were used to compare rates between the pre-pandemic (2016–February 2020) and pandemic (March 2020–December 2020) period. Results The total HAI prevalence among hospitalised patients was higher during the pandemic period (7.4%) compared to pre-pandemic period (6.4%), mainly because of an increase in ventilator-associated pneumonia ( VAP), gastro-intestinal infections (GIs) and central nervous system (CNS) infections. No differences in SSI rates were observed during the pandemic, except for a decrease after colorectal surgeries (6.3% (95%-CI 6.0–6.6%) pre-pandemic versus 4.4% (95%-CI 3.9–5.0%) pandemic). The observed CRBSI incidence in the pandemic period (4.0/1,000 CVC days (95%-CI 3.2–4.9)) was significantly higher than predicted based on pre-pandemic trends (1.4/1000 (95%-CI 1.0–2.1)), and was increased in both COVID-19 patients and non-COVID-19 patients at the intensive care unit (ICU). Conclusions Rates of CRBSIs, VAPs, GIs and CNS infections among hospitalised patients increased during the first year of the pandemic. Higher CRBSI rates were observed in both COVID-19 and non-COVID-19 ICU population. The full scope and influencing factors of the pandemic on HAIs needs to be studied in further detail. Keywords Healthcare-associated infections, Covid-19, Pandemic, Surveillance Background When the World Health Organization on March 11, 2020 *Correspondence: Janneke D. M. Verberk officially declared the coronavirus disease 2019 (COVID- janneke.verberk@rivm.nl; j.d.m.verberk-2@umcutrecht.nl 19) a global pandemic [1], COVID-19 hospitalisations Sabine C. de Greeff in the Netherlands were already increasing rapidly. The sabine.de.greeff@rivm.nl Department of Epidemiology and Surveillance, Centre high influx of patients impacted the critical care capac - for Infectious Diseases Control, National Institute for Public Health ity, work processes, and availability and use of protective and the Environment, Antonie Van Leeuwenhoeklaan 9, 3721 equipment in hospitals [2–5]. To handle the pressure and MA Bilthoven, The Netherlands Department of Medical Microbiology and Infection Prevention, high demand of care during this crisis, hospitals reorgan- University Medical Centre Utrecht, Utrecht, The Netherlands ised their resources and delivery of care [6]. For example, Department of Internal Medicine, Amsterdam University Medical elective surgeries were postponed or cancelled, intensive Centres, Infection and Immunity, Amsterdam Public Health, University of Amsterdam, Amsterdam, The Netherlands care unit (ICU) bed capacity was scaled up, the ratio of healthcare workers allocated to patients was reduced, © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Verberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 2 of 11 external staff was hired, and changes to daily care rou - whether the patient was admitted to the hospital due to tines, such as the frequency of patient washing, was COVID-19 (positive test at admission). Hospitals that reduced [7–9]. reported their surveillance data yearly to PREZIES over During this pandemic situation, attention to infection the years 2016–2020 were included in this study and used prevention and control (IPC) measures may have been to evaluate the infection rates during the pre-pandemic deprived given the high work pressure, or redirected and pandemic period. towards the prevention of SARS-CoV-2 transmission [10]. In addition, patients hospitalised with COVID-19 Definition pre‑pandemic and pandemic period are known for having comorbidities, long hospital stays Based on COVID-19 hospitalisation rates in the Nether- and complex care with multiple invasive devices, put- lands, the PPS surveys of 2016–2019 were defined as pre- ting them at higher risk for healthcare-associated infec- pandemic and the surveys of March and October 2020 tions (HAIs) [11]. Hence, an increase of HAIs could be were defined as the pandemic period. Data from the SSI expected and is also reported by previous studies [12, 13]. and CRBSI modules were divided in pre-pandemic (Janu- On the other hand, hospitals applied strict, aggressive ary 2016 to February 2020) and pandemic (from 1st of IPC measures to prevent within-hospital transmission March 2020 to December 2020). of SARS-CoV-2. As a result, a positive (indirect) effect on HAI occurrence can be expected as well and has been reported by others [14–16]. Statistical analyses Given these contrasting findings, there is need for ade - Per module, patient-, surgery-, or CVC- related charac- quate HAI reporting not limited to COVID-19 cohorts teristics were reported and compared between the pre- only, with sufficient historical data to allow pre-pan - pandemic and pandemic period, using a chi-square test demic comparisons. The aim of this study was to quantify for categorical variables and Mann–Whitney U test for trends in the number of HAIs in Dutch hospitals during continuous variables. Thereafter, we quantified the num - the COVID-19 pandemic, using national surveillance ber of HAIs during the pandemic. For PPS data, the dif- that continued during the pandemic. Second, HAI types ference in observed HAI rates between the pre-pandemic were compared between COVID-19 patients versus non- and pandemic period was tested using chi-square 2-tailed COVID-19 patients. test with Yates’ correction. For the SSI and CRBSI incidence, we estimated the expected infection rates for the pandemic period Methods based on pre-pandemic data and compared this with Study design and data sources the actual observed rates in the pandemic period. In this retrospective cohort study, data were derived from To estimate the expected incidence rate for SSI, the the Dutch national nosocomial surveillance network National Nosocomial Infections Surveillance System (PREZIES). In short, acute care hospitals voluntarily par- (NNIS) risk index in pre-pandemic data was used to ticipate in one or more of the three surveillance modules predict the risk of SSI for each NNIS category for the targeting different HAIs: (1) bi-annually Point Prevalence pandemic period (Additional file 1: Figure S1). The Surveys (PPS) performed in March and October in which NNIS risk index, ranging from 0 to 3, is composed of the prevalence of all type of HAIs are measured in all 1 point for each of the following criteria: wound class admitted patients (excluding patients admitted to psychi- classified as contaminated or infected; American Soci- atry and day-care units), (2) Surgical site infection (SSI) ety of Anaesthesiologists (ASA) score of 3, 4, or 5; and incidence surveillance on targeted procedures (see Addi- an operation duration above the 75th percentile [20]. tional file 1: Table S1 for an overview of the procedures), The predicted infection rate was compared with the and (3) hospital-wide catheter-related bloodstream infec- observed infection rate using a chi-square test. In addi- tion (CRBSI) incidence surveillance in patients with a tion, two sensitivity analyses for SSI were performed. central venous catheter (CVC) in place for ≥ 48 h. For First, the same analyses were repeated for deep SSI each module, infection control practitioners in each hos- only, with the rationale that superficial SSIs may pital manually review medical records retrospectively have been missed during follow-up in the pandemic according to the national surveillance protocols and period: patients avoided contact with healthcare pro- annotate which patients meet infection case definitions. viders afraid of becoming infected with SARS-CoV-2, The surveillance protocols and case definitions are based patients did not want to be a burden on the system, on the (European) Centres for Disease Control and Pre- and follow-up appointments were replaced by remote vention and are described elsewhere [17–19]. Only in the care because of stay-at-home orders [9]. Second, PPS and CRBSI modules information was collected about trends in SSI incidence rates were checked per surgical V erberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 3 of 11 specialty. In case an increasing or decreasing trend hospitals respectively that reported their yearly sur- was observed pre-pandemic, the expected SSI rate was veillance data in 2016–2020 to PREZIES (Table 1). In recalculated based on 2019 data only. To estimate the these hospitals, the absolute annual number of admis- expected CRBSI incidence per 1000 CVC days in the sions (PPS module) and surgeries (SSI module) was pandemic period, the mean pre-pandemic incidence lower in 2020 compared to previous years, while there per 1000 CVC days for each of the three application- was a slight increase in the number of inserted CVCs based categories (total parenteral nutrition (TPN); (CRBSI module). dialysis; and the remaining other applications) was multiplied with the pandemic number of CVCs in each Healthcare‑associated infections during the first pandemic category (Additional file 1: Figure S1). The predicted year and observed incidence rates were compared using a Point prevalence survey results mid-P exact test. Last, differences in patient character- During the pandemic period, a higher proportion of hos- istics, medical device use, and HAIs were investigated pitalised patients was male, patients had slightly higher in COVID-19 patients versus non-COVID-19 patients McCabe scores and more ICU admissions were observed based on PPS and CRBSI data, by using a chi-square (Table 2). The proportion of patients having a medical test or Mann–Whitney test. A p value of < 0.05 was device increased during the pandemic period, in par- considered statistically significant and analyses were ticular the use of CVCs. The proportion of patients with performed using SAS version 9.4 software (SAS Insti- antibiotic treatment at the time of the survey was slightly tute, Cary, NC). higher during the pandemic (42.6%) versus pre-pandemic (37.7%; p < 0.01). The total HAI prevalence was higher during the pandemic period compared to pre-pandemic period, mainly because of an increase in gastro-intestinal Results infections and infections of the central nervous system Table 1 shows the number of hospitals participating in (Tables 3, 4). The proportion of patients with lower res - the three different modules, per year. The number of piratory tract infections (LRTIs) in the pandemic period hospitals reporting PPS data during the pandemic year was similar compared to pre-pandemic, however, a larger 2020 was less than half compared with previous years. proportion was associated with mechanical ventilation Subsequent analyses were performed for the PPS, SSI, and CRBSI module, using data from 10, 51, and 11 Table 1 Overview of hospitals included in this study Number of hospitals Number of hospitals included in this study reporting data Number of patients, surgeries, reporting data to PREZIES each year in 2016–2020 (general/teaching/academic) and CVCs included, respectively PPS module 10 (6/1/3) 2016 40 NA 4036 2017 37 NA 3956 2018 27 NA 3841 2019 30 NA 4273 2020 11 NA 3124 SSI module 51 (33/16/2) 2016 84 NA 48,760 2017 81 NA 50,487 2018 75 NA 51,816 2019 68 NA 56,286 2020 66 NA 45,656 CRBSI module 11 (8/3/0) 2016 31 NA 2454 2017 28 NA 2030 2018 26 NA 1735 2019 21 NA 2019 2020 18 NA 2286 PPS point prevalence survey, n number, SSI surgical site infection, NA not applicable, CRBSI catheter-related bloodstream infection, CVCs central venous catheters Verberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 4 of 11 Table 2 Patient-, surgery-, and central venous catheter characteristics Pre‑pandemic Pandemic p value PPS module 16,106 patients 3124 patients Age in years [median, (IQR)] 64.8 (32.9) 63.9 (34.2) < 0.01 Age group (n (%)) < 0.01 < 1 year 1191 (7.3) 272 (8.7) 1–19 year 804 (5.0) 163 (5.2) 20–29 year 708 (4.4) 126 (4.0) 30–39 year 1032 (6.4) 194 (6.2) 40–49 year 1080 (6.7) 211 (6.8) 50–59 year 1951 (12.1) 405 (13.0) 60–69 year 2972 (18.5) 556 (18.1) 70–79 year 3430 (21.3) 705 (22.6) 80–89 year 2412 (15.0) 406 (13.0) ≥ 90 year 526 (3.3) 76 (2.4) Sex [male (n (%))] 8060 (50.0) 1625 (52.0) 0.04 Specialty [n (%)] < 0.01 Cardiology 1654 (10.3) 304 (9.7) Surgery 2284 (14.2) 434 (13.9) Internal medicine 1908 (11.8) 332 (10.6) Paediatrics 1140 (7.1) 216 (6.9) Respiratory medicine 1285 (8.0) 235 (7.6) Other 7835 (48.6) 1603 (51.3) McCabe [n (%)] < 0.01 Non-fatal (> 5 year) 11,615 (72.1) 2141 (68.5) Ultimately fatal (1–5 year) 1394 (8.7) 311 (10.0) Rapidly fatal (< 1 year) 308 (1.9) 69 (2.2) Unknown 2789 (17.3) 603 (19.3) ICU [n (%)] < 0.01 Yes 1170 (7.3) 281 (9.0) No 14,936 (92.7) 2843 (91.0) Medical devices [n (%)] Urethral catheter 3374 (20.9) 711 (22.8) 0.02 Peripheral catheter 9011 (56.0) 1767 (56.6) 0.5 Mechanical ventilation 482 (3.0) 128 (4.2) < 0.01 Central venous catheter 1,572 (9.8) 458 (14.7) < 0.01 Antibiotics [n (%)] < 0.01 Yes 6065 (37.7) 1330 (42.6) No 10,041 (62.3) 1794 (57.4) SSI module 217,212 surgeries 35,793 surgeries Age in years [median (IQR)] 67.7 (57.5–74.7) 67.3 (56.4–74.5) < 0.01 Sex [male (n (%))] 67.137 (31.6) 11.193 (34.0) < 0.01 Body mass index [median (IQR)] 27.3 (24.4–30.8) 27.2 (24.3–30.7) < 0.01 Length of stay in days (median (IQR)) 2 (0–274) 1 (0–95) < 0.01 Duration of surgery in minutes [median (IQR)] 62 (47–80) 59 (44–76) < 0.01 ASA classification [n (%)] < 0.01 1 38,062 (17.5) 5083 (14.2) 2 130,422 (60.0) 21,954 (61.3) 3 38,025 (17.5) 7223 (20.2) 4 1138 (0.5) 217 (0.6) 5 58 (0.0) 4 (0.0) V erberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 5 of 11 Table 2 (continued) Pre‑pandemic Pandemic p value Unknown/NA 9507 (4.4) 1312 (3.6) NNIS index [n (%)] < 0.01 0 139,092 (64.0) 21,199 (59.2) 1 59,217 (27.3) 11,009 (30.8) 2 8891 (4.1) 2186 (6.1) 3 248 (0.1) 66 (0.2) Unknown/NA 9764 (4.5) 1333 (3.7) Type of surgery [n (%)] < 0.01 Cardiothoracic surgery 5596 (2.6) 948 (2.6) Mamma surgery 24,556 (11.3) 4080 (11.4) Colon surgery 26,832 (12.4) 4770 (13.3) Orthopaedic surgery 140,821 (64.8) 22,353 (62.5) Obstetrics 15,465 (7.1) 2896 (8.1) Neurosurgery 3942 (1.8) 746 (2.1) CRBSI module 8595 patients (10,546 CVCs) 1929 patients (2614 CVCs) Age in years [median (IQR)] 69.5 (60.3–76.5) 68.6 (59.1–74.5) < 0.01 Sex [male (n (%))] 5044 (58.7) 1259 (65.3) < 0.01 Number of CVCs per patient [median (IQR)] 1.2 (1–1) 1.3 (1–1) < 0.01 CVC days [median (IQR)] 5 (3–8) 6 (4–9) < 0.01 ICU [n (%)] < 0.01 Yes 6574 (76.5) 1591 (82.5) No 2021 (23.5) 338 (17.5) CVC use [n (%)] Total parenteral nutrition 1,889 (17.9) 428 (16.4) 0.06 Antibiotics 5037 (47.8) 1624 (62.1) < 0.01 Dialysis 1191 (11.3) 312 (11.9) 0.36 Hemodynamic monitoring 5466 (51.8) 1500 (57.4) < 0.01 Other 1861 (17.6) 304 (11.6) < 0.01 PPS point prevalence survey, n number, ICU intensive care unit, SSI surgical site infection, IQR interquartile range, NA not applicable, NNIS National Nosocomial Infections Surveillance System, CRBSI catheter-related bloodstream infection, CVC central venous catheter Patients can have multiple devices at the same time. Percentages are calculated as the proportion of patients with a specific device out of the total number of patients Patients can have a CVC for multiple applications. Percentages are calculated as the proportion of CVCs for a specific use out of all CVCs (ventilator-associated pneumonia (VAP), 22.5% pan- lower during the pandemic (p < 0.01; Table 3). During demic versus 13.5% pre-pandemic, Table 4). 2016–2019, already a decreasing trend in SSI incidence after colorectal surgeries was observed (7.2%; 7.2%; Surgical site infections 6.3%; 5.0%, respectively), while the proportion of closed Within the SSI module, 217,212 surgeries were included procedures increased (p < 0.01, Additional file 1: Figure in the pre-pandemic period versus 35,793 surgeries S2). When calculating the expected SSI incidence after during the pandemic. Compared to the pre-pandemic colorectal surgery based on 2019 data only, the SSI rate period, patients operated during the pandemic period in de pandemic was as predicted (predicted SSI rate: were more often of the male gender, had slightly higher 5.1%; 95%-CI 4.5–5.8, observed SSI rate: 4.4%; 95%-CI: ASA- and NNIS scores and had shorter hospital stays 3.9–5.0, p = 0.1). Sensitivity analysis comparing observed (Table 2). The observed SSI incidence for all type of sur - and expected incidence of deep SSI only showed similar geries combined in the pandemic period was significantly results (Additional file 1: Table S2). lower than predicted (1.8% versus 2.1%, respectively) (Fig. 1 and Table 3). When stratified by surgery type, only the SSI incidence after colon surgery was significantly Verberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 6 of 11 Table 3 Infection rates pre-pandemic, predicted infection rates during pandemic, and observed infection rates during the pandemic Pre‑pandemic [% (95%‑ CI)] Predicted [% (95%‑ CI)] Pandemic [% (95%‑ CI)] HAI prevalence (by PPS) Total HAI (by PPS) 6.4 (6.0–6.8) NA 7.4 (6.5–8.3)* SSIs 2.2 (1.9–2.4) NA 2.3 (1.9–2.9) RTIs 1.2 (1.1–1.4) NA 1.4 (1.1–1.9) BSIs (primary and secondary) 1.3 (1.1–1.5) NA 1.4 (1.1–1.9) UTIs 0.8 (0.7–1.0) NA 0.8 (0.5–1.2) Other 0.9 (0.8–1.1) NA 1.4 (1.1–1.9)* SSI incidence Total 2.1 (2.0–2.1) 2.1 (2.0–2.3) 1.8 (1.6–1.9)* Cardiothoracic surgery 1.7 (1.4–2.1) 1.7 (1.4–2.1) 1.9 (1.2–3.0) Mamma surgery 3.8 (3.6–4.0) 4.0 (3.4–4.6) 3.4 (2.9–4.0) Colon surgery 6.3 (6.0–6.6) 6.5 (5.9–7.3) 4.4 (3.9–5.0)* Orthopaedic surgery 1.1 (1.0–1.1) 1.2 (1.0–1.3) 1.0 (0.9–1.1) Obstetrics 1.4 (1.2–1.6) 1.4 (1.0–1.9) 1.3 (1.0–1.8) Neurosurgery 1.0 (0.7–1.4) 0.7 (0.4–1.3) 0.8 (0.4–1.7) CRBSI incidence 1.6 (1.3–2.0) 1.4 (1.0–2.1) 4.0 (3.2–5.0)* 95%-CI 95% confidence interval, HAI healthcare-associated infection, PPS point prevalence survey, SSI surgical site infection, CRBSI catheter-related bloodstream infection, RTIs respiratory tract infections, BSIs bloodstream infections, UTIs urinary tract infections, NA not applicable *Statistically significant different from predicted rates Catheter‑related bloodstream infections Table 4 Distribution of healthcare-associated infections in pre- During the pandemic period, patients with a CVC were pandemic and pandemic PPS cohort slightly younger, more often of the male gender and more Pre‑pandemic Pandemic often admitted to the ICU compared to the pre-pan- n = 16,106 [n (%)] n = 3124 [n (%)] demic period. During the pandemic period, the number Total HAI (by PPS) n = 1028 (6.4%)* n = 230 (7.4%)* of inserted CVCs per patient was slightly higher and the SSIs 347 (33.8) 73 (31.7) CVC duration was longer. CVCs were more frequently RTIs 202 (19.7) 45 (19.6) used for antibiotics and hemodynamic monitoring and Of which lower RTIs 177 (87.6) 40 (88.9) less often for TPN (Table 2). The observed CRBSI inci - Associated with mechanical venti- 24 (13.5)* 9 (22.5)* lation ( VAP) dence of 4.0/1000 CVC days (95%-CI 3.2–4.9/1000) BSIs 205 (20.0) 45 (19.6) in the pandemic period was significantly higher than Of which catheter-related 44 (4.3) 6 (2.6) the predicted rate of 1.4/1000 CVC days (95%-CI 1.0– UTIs 134 (13.0) 25 (10.9) 2.1/1000; p < 0.01) (Fig. 1). Of which catheter-related 81 (7.9) 19 (8.2) GTIs 37 (3.6)* 16 (7.0)* Healthcare‑associated infections within COVID‑19 patients Skin infections 35 (3.4) 7 (3.0) Within the PPS module, COVID-19 status was only reg- Mouth infections 16 (1.6) 5 (2.2) istered during the survey in October 2020: 50 (6.6%) Central nervous system infections 13 (1.3)* 7 (3.0)* patients were SARS-CoV-2 positive during admission Cardiovascular infections 12 (1.2) 3 (1.3) and were compared with 713 (93.4%) non-COVID-19 Bone infections 11 (1.1) 0 (0.0) patients. COVID-19 patients were more often admitted Other systemic infections 8 (0.8) 0 (0.0) to the ICU, had more often medical devices and anti- Reproductive tract infections 5 (0.5) 2 (0.9) biotic use (Table 5). A significantly higher HAI preva - Eye infections 2 (0.2) 2 (0.9) lence was observed in this patient group as compared to Ear infections 1 (0.1) 0 (0.0) non-COVID-19 patients (12% versus 0.4% respectively, PPS point prevalence survey, HAIs healthcare-associated infections, SSIs surgical p < 0.01), with bloodstream infections (BSI) as the most site infections, RTIs respiratory tract infections, VAP ventilator-associated pneumonia, BSIs bloodstream infections, UTIs urinary tract infections, predominant manifestation (Additional file 1: Table S3). GTIs Gastro-intestinal infections. Percentages are presented as % out of total A total of 9 out of 11 hospitals participating in the HAIs CRBSI module reported whether the patient was *Statistically significant V erberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 7 of 11 Fig. 1 Infection rates pre-pandemic, and predicted and observed infection rates during the pandemic period. HAIs healthcare-associated infections, PPS point prevalence survey, SSIs surgical site infections, RTIs respiratory tract infections, BSIs bloodstream infections, UTIs urinary tract infections, CRBSIs catheter-related bloodstream infections admitted to the hospital due to COVID-19. These Discussion COVID-19 patients were more often male, were slightly During the first pandemic year CRBSIs, VAPs, gas - younger in age, and had significant longer ICU length of tro-intestinal- and central nervous system infections stay compared to non-COVID-19 patients with a CVC occurred more frequently among hospitalised patients, during the pandemic period. In addition, COVID-19 while SSIs and catheter-associated urinary tract infec- patients had more CVCs inserted and with a longer dura- tion (CAUTI) rates remained stable. HAIs occurred more tion (Table 5). The CVC was more often used for anti - often in COVID-19 patients, however, in non-COVID-19 biotics and less for TPN compared to non-COVID-19 patients admitted to the ICU an sevenfold increase of patients. The CRBSI incidence was 8.1/1000 CVC days CRBSI was observed during the pandemic compared to (95%-CI 5.9–10.8) in COVID-19 patients compared to pre-pandemic as well. 3.4/1000 (95%-CI 2.2–5.0) in patients without COVID- Regarding SSI, less surgeries were performed in 2020 19 (p < 0.01). When stratifying the COVID-19 patients to and the patients that have been operated had slightly ICU and non-ICU, CRBSI rates were 7.8/1000 CVC days higher ASA and NNIS scores compared to previous (95%-CI 5.6–10.7) and 11.1 (95%-CI 5.0–24.7) respec- years, possibly explained by prioritising urgent pro- tively. When stratifying the non-COVID-19 patients to cedures during the pandemic period. Although this ICU and non-ICU, CRBSI rates were 4.8/1000 CVC days patient population may be more likely to develop SSIs, (95%-CI 3.0–7.6) and 1.7 (95%-CI 0.7–4.0) respectively. no increase in incidence was observed. Remarkable is The CRBSI incidence for non-COVID-19 patients in the the relative high number of laparoscopic colon surgeries ICU (4.8/1000) was significantly higher compared to pre- during the pandemic, which may be induced by policies pandemic years (0.7/1000; 95%-CI 0.5–1.1) as well. to relieve ICU capacity and the shift to minimally inva- sive surgery to protect operating room personnel from SARS-CoV-2 aerosol transmission [21]. Future data will show whether open surgery had been replaced during the Verberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 8 of 11 Table 5 Differences in COVID-19 patients versus non-COVID patients admitted to the hospital, March 2020–December 2020 COVID‑19 patient Non‑ COVID‑19 patient p value PPS module [n (%)] n = 50 n = 713 Age in years [median, (IQR)] 71.7 (20.7) 66.8 (30.0) < 0.01 Sex [male (n (%))] 32 (64.0) 355 (49.8) 0.05 Specialty [n (%)] < 0.01 Cardiology 1 (2.0) 81 (11.4) Surgery 1 (2.0) 125 (17.5) Internal medicine 8 (16.0) 110 (15.4) Paediatrics 0 (0.0) 52 (7.3) Respiratory medicine 25 (50.0) 53 (7.4) Other 15 (30.0) 292 (41.0) McCabe [n (%)] 0.93 Non-fatal (> 5 year) 44 (88.0) 620 (87.0) Ultimately fatal (1–5 year) 5 (10.0) 74 (10.4) Rapidly fatal (< 1 year) 1 (2.0) 13 (1.8) Unknown 0 (0.0) 6 (0.8) ICU [n (%)] < 0.01 Yes 13 (26.0) 31 (4.4) No 37 (74.0) 682 (95.6) Medical devices [n (%)] Urethral catheter 10 (20.0) 147 (20.6) 0.36 Peripheral catheter 39 (78.0) 456 (64.0) 0.04 Mechanical ventilation 5 (10.0) 8 (1.2) < 0.01 Central venous catheter 5 (10.0) 47 (6.6) 0.35 Antibiotics [n (%)] < 0.01 Yes 32 (64.0) 266 (37.3) No 18 (36.0) 447 (62.7) HAIs [% (95%-CI)] 12 (5.6–23.8) 0.4 (0.1–1.2) < 0.01 CRBSI module [n (%)] n = 367 n = 708 Age in years [median (IQR)] 66.2 (57.0–71.8) 69.3 (58.3–75.1) < 0.01 Sex [male (n (%))] 288 (78.5) 435 (61.4) < 0.01 Number of CVCs per patient [median (IQR)] 1.8 (1–2) 1.3 (1–1) < 0.01 CVC days [median (IQR)] 7 (5–10) 6 (4–9) < 0.01 ICU [n (%)] < 0.01 Yes 350 (95.4) 518 (73.2) No 17 (4.6) 190 (26.8) Length of ICU stay in days [median (IQR)] 18 (8–33) 4 (2–11) < 0.01 CVC use [n (%)] Total parenteral nutrition 37 (5.6) 200 (21.9) < 0.01 Antibiotics 454 (69.3) 523 (57.2) < 0.01 Dialysis 92 (14.0) 130 (14.2) 0.94 Hemodynamic monitoring 319 (48.7) 441 (48.2) 0.88 Other 98 (15.0) 144 (15.8) 0.72 CRBSI per 1000 CVC days (95%-CI) 8.1 (5.9–10.8) 3.4 (2.2–5.0) < 0.01 For PPS, COVID-19 status was only measured in the survey of October 2020. For CRBSI, COVID-19 status was reported by 9 out of 11 hospitals for the majority (56.2%) of the patients: 19.2% were COVID-19 patients, 37.0% non-COVID-19 and for the remaining 43.8% within the CRBSI module, COVID-19 status was unknown Patients can have multiple devices at the same time. Percentages are calculated as the proportion of patients with a specific device out of the total number of patients Patients can have a CVC for multiple applications. Percentages are calculated as the proportion of CVCs for a specific use out of all CVCs V erberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 9 of 11 pandemic by closed surgery, or whether the open surger- to numerous elective procedures that were cancelled and ies were postponed. postponed. Unfortunately, within the surveillance mod- The findings of this study are in line with previous ules we only have limited patient- and clinical informa- research: several studies reported increases during the tion, restricting the adjustment for casemix. Although we pandemic in among others CRBSIs, BSIs, and VAPs used data of a fixed set of hospitals and used the NNIS [12, 13, 22–25]. The PPS data showed that the preva - score and CVC applications to calculate the expected lence of LRTIs did not change, however the proportion infection rates, we may not have completely addressed of LRTIs associated with ventilation increased, likely the shift in characteristics of the patient population dur- due to the increased use of mechanical ventilation [26]. ing the pandemic. The increased CRBSI incidence in Importantly, the work pressure, burden and influx of non-COVID-19 ICU patients may indicate that both COVID-19 patients was not constant throughout 2020: a change in patient mix or the reorganization of care, COVID-19 surges varied during the year, by region and such as changed IPC practices, modified use of personal by hospital [27]. Especially for the PPS, the timing of the protective equipment, and additional (unskilled ICU) surveys (March and October) may not have paralleled the temporary staff, may have contributed to the increased COVID-19 surges and circumstances and therefore may infection risk [5, 16, 34, 35]. To fully explain HAI dynam- have underestimated potential effects: we did not find ics in pandemic circumstances indicators describing the any increase in CRBSIs or CAUTIs in the PPS data while local healthcare context at institutional level are needed, this was reported by others [23, 24]. Within the CRBSI such as patient characteristics, disruption of IPC prac- module, the number of CRBSI events was too low to per- tices, prescribing- and (microbiological) order practices, form sub-analyses to evaluate stronger effects on inci - and antimicrobial resistance patterns [36]. dence rates during COVID-19 surges. Most studies published so far are of variable quality as Conclusions they are limited to retrospective cohort studies. Moreo- Summarized, we observed an increase in rates of CRBSI, ver, they focus solely on COVID-19 patients, and lack VAP, gastro-intestinal- and central nervous system infec- standardized case definitions without differentiating tions among hospitalised patients during the first pan - between settings or specialties [28]. The current surveil - demic year. Furthermore, CRBSI incidence was also lance-based study has a retrospective design as well and increased in the non-COVID-19 ICU population during hospitals performed the surveillance themselves, how- the pandemic. The full scope and driving factors of this ever by using standardized case definitions and large change in HAIs need to be studied in more detail to be sample sizes from a fixed number of hospitals for several able to anticipate—from an infection prevention perspec- years, the results of our study may be more robust. Still, tive—more adequately on future epidemics of COVID-19 with our study design, we cannot fully explain (causal) or other severe acute respiratory infections. reasons for the change in HAIs observed during the pan- demic. Several hypotheses are possible, probably all con- Abbreviations tributing to some degree. In part, the increase in HAIs ASA American Society of Anaesthesiologists can be explained by the fact that hospitalisations were BSI Bloodstream infection dominated by COVID-19 patients who may have been CAUTI C atheter-associated urinary tract infection CRBSI Catheter-related bloodstream infection more vulnerable for HAIs and other co-infections due COVID-19 C oronavirus disease 2019 to immune dysregulation [29–32]. This is also reflected CVC Central venous catheter by the high antibiotic use observed in these patients, ICU Intensive care unit IPC Infection prevention and control which will increase risk of antibiotic resistance. In Ger- IQR Interquartile range many, there was no ICU overcrowding due to COVID-19 GI Gastro-intestinal infection patients because of their high ICU bed capacity as com- HAI Healthcare-associated infection LRTI Lower respiratory tract infections pared with the Netherlands, and no increase in device- NA Not applicable associated infections was observed in this country [33]. NNIS National nosocomial infections surveillance system In addition, COVID-19 patients in general are more PPS Point prevalence survey RTI Respiratory tract infection exposed to known risk factors for HAIs such as longer SSI Surgical site infection durations of mechanical ventilation, higher number of TPV Total parenteral nutrition CVCs inserted, corticosteroid treatment, prone posi- UTI Urinary tract infection VAP Ventilator-associated pneumonia tioning, and longer lengths of stay [24]. Although not observed within this study, the composition of char- acteristics of remaining non-COVID-19 hospitalised patients is likely to be different than pre-pandemic, due Verberk et al. Antimicrobial Resistance & Infection Control (2023) 12:2 Page 10 of 11 10. Palmore TN, Henderson DK. Healthcare-associated infections during the Supplementary Information coronavirus disease 2019 (COVID-19) pandemic. Infect Control Hosp The online version contains supplementary material available at https:// doi. Epidemiol. 2021;42(11):1372–3. org/ 10. 1186/ s13756- 022- 01201-z. 11. Bicudo D, Batista R, Furtado GH, Sola A, Medeiros EA. Risk factors for catheter-related bloodstream infection: a prospective multicenter study Additional file 1: Supplementary Figures and Tables. in Brazilian intensive care units. Braz J Infect Dis. 2011;15(4):328–31. 12. Amarsy R, Trystram D, Cambau E, Monteil C, Fournier S, Oliary J, et al. Surging bloodstream infections and antimicrobial resistance during the Author contributions first wave of COVID-19: a study in a large multihospital institution in the JV conceptualized and designed the study, wrote the first draft of the manu- Paris region. Int J Infect Dis. 2022;114:90–6. script, and coordinated the analysis and interpretation of data. TK, NK and NR 13. Fakih MG, Bufalino A, Sturm L, Huang RH, Ottenbacher A, Saake K, et al. were responsible for the data management, analyses and interpretation. SvR, Coronavirus disease 2019 (COVID-19) pandemic, central-line-associated TH, SG, and SdG reviewed the manuscript for intellectual content and scien- bloodstream infection (CLABSI), and catheter-associated urinary tract tific integrity. All authors read and approved the final manuscript. infection (CAUTI): the urgent need to refocus on hardwiring prevention efforts. Infect Control Hosp Epidemiol. 2022;43(1):26–31. Funding 14. 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A surveil- lance framework for healthcare associated infections and antimicrobial resistance in acute care in the context of COVID-19: a rapid literature review and expert consensus. 2021. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub- lished maps and institutional affiliations. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : fast, convenient online submission thorough peer review by experienced researchers in your field rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions
Journal
Antimicrobial Resistance & Infection Control – Springer Journals
Published: Jan 5, 2023
Keywords: Healthcare-associated infections; Covid-19; Pandemic; Surveillance