EPIDEMIOLOGY OF HEALTHCARE- ASSOCIATED INFECTIONS IN THE NETHERLANDS Surveillance and research data for action Tjallie van der Kooi
Epidemiology of healthcareassociated infections in the Netherlands: surveillance and research data for action Tjally Ida Irene van der Kooi
ISBN: 978‐94‐6506‐222‐8 Financial support for printing of this thesis was kindly provided by the National Institute for Public Health and the Environment. Cover: Tjallie van der Kooi Printing/layout: Ridderprint, www.ridderprint.nl Copyright ©Tjallie van der Kooi, The Netherlands, 2024 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, without prior permission of the author or the copyright‐ owning journals of published chapters. Over de achterkant: de torenvalk herken je makkelijk door zijn kenmerkende ‘bidden’: klapwiekend tegen de wind in kan deze valk stilstaan in de lucht om, met zijn scherpe ogen, zijn jachtgebied te overzien. Daarom is de torenvalk als symbool gekozen voor de PREZIES surveillance.
Epidemiology of healthcareassociated infections in the Netherlands: surveillance and research data for action PhD thesis to obtain the degree of PhD at the University of Groningen on the authority of the Rector Magnificus Prof. J.M.A. Scherpen and in accordance with the decision by the College of Deans. This thesis will be defended in public on Wednesday 2 October 2024 at 16.15 hours by Tjally Ida Irene van der Kooi born on 2 January 1968
Supervisors Prof. A. Voss Prof. H. Grundmann Co‐supervisor Dr. S.C. de Greeff Assessment Committee Prof. B.N.M Sinha Prof. J.A.J.W. Kluytmans Prof. M.C. Vos
Table of contents Chapter 1 Introduction ..................................................................................................................... 7 Part I HAI in the early 2000s: expansion of surveillance programmes Chapter 2 Incidence and risk factors of device-associated infections and associated mortality at the intensive care in the dutch surveillance system. ................................ 21 Chapter 3 Catheter application, insertion vein and length of ICU stay prior to insertion affect the risk of catheter-related bloodstream infection ............................................ 43 Chapter 4 Using flexible methods to determine risk factors for ventilator-associated pneumonia in the Netherlands. .................................................................................... 59 Chapter 5 Prevalence of nosocomial infections in the Netherlands, 2007-2008: results of the first four national studies. .................................................................................. 89 Chapter 6 Mortality review as a tool to assess the contribution of healthcare-associated infections to death: results of a multicentre validity and reproducibility study, 11 European Union countries, 2017 to 2018 .............................................................. 105 Part II Prevention of HAI: improving compliance to best practices Chapter 7 Prevention of hospital infections by intervention and training (PROHIBIT): results of a pan-European cluster-randomized multicentred study to reduce central venous catheter-related bloodstream infections ....................................................... 145 Chapter 8 Hand hygiene improvement of individual healthcare workers – Results of the multicentre PROHIBIT study........................................................................................ 193 Chapter 9 The effect of an intervention bundle to prevent central venous catheter-related bloodstream infection in a national programme in the Netherlands. ...................... 215 Chapter 10 General discussion.... ................................................................................................ 237 Closing pages................................................................................................................................ 263 Summary ..................................................................................................................................... 265 Samenvatting ................................................................................................................................ 271 Dankwoord .................................................................................................................................... 279 About the author........................................................................................................................... 281 List of publications ........................................................................................................................ 283
Chapter 1
Introduction
Healthcare is meant to cure and care for patients, but unfortunately invasive devices and procedures also form a risk for patients to acquire infections. This type of infection is termed nosocomial or healthcare‐associated infections (HAI) and they increase morbidity and mortality. In this thesis, HAI will usually refer more specifically to hospital‐associated infections. Time spent in a hospital can itself be a risk factor for HAI, as certain pathogens are more concentrated than in the community and may have evolved resistance to antibiotics due to antibiotic selection pressure. The aim of this thesis is to describe achievements in HAI prevention over the last two decades, as measured through surveillance, and the role of the promotion of best practices. In the latter the emphasis is on central venous catheter‐related bloodstream infection (CRBSI) – prevention bundles, and hand hygiene. 1.1 Prevention of hospital infections in historical perspective During millennia of human history, in times of conflict or peace, people have suffered injuries and diseases that needed medical attention. In medieval Europe, treatment in infirmaries and sick‐houses was often a death sentence, with gastroenteritis and louse‐ borne typhus (“hospital fever”) infesting the crowded wards, where beds were usually shared. Neither surgical tools nor hands or gowns were cleaned or changed, and postsurgical mortality rates of 60‐80% were common, mostly due to infectious gangrene [1]. The second half of the 19th century was, however, an era of many scientific achievements. Even before the exact nature of infections was understood or accepted, the importance of hygiene (“cleanliness”) became increasingly acknowledged and led to improvements [2, 3]. The discovery of the role of micro‐organisms laid the foundations of “germ‐theory”, which led to sterilisation of surgical tools and bandages and, consequently, decreasing rates of infection and mortality [1, 3]. Semmelweis, Pasteur, Koch, Lister and Nightingale were the well‐known pioneers in the field of infection prevention. Other advances in health technology that ensured safer healthcare and surgery were the introduction of X‐ray units in hospitals, intravenous fluid therapy and clinical thermometry [1], and anaesthesia in surgery (although initially it increased risk as it allowed surgery to last longer) [6]. While infection prevention improved, infection treatment did not progress until the first half of the last century, when penicillin, discovered in 1928, was introduced in patient care and other antibiotics followed [4]. Despite the hope of some that this would spell the end for infectious diseases, antimicrobial resistance evolved almost instantaneously. Hospital outbreaks and indeed a pandemic of a single lineage of penicillin‐resistant Staphylococcus aureus followed within a single decade after the widespread introduction of penicillin. This set the stage for professional hospital 1 9 Introduction
infection control programmes, which were first initiated in the 1950s in the UK and the USA [1, 5]. In the Netherlands, the Dutch government asked the Health Council for advice on this matter and, starting with the Council’s report in 1966, infection prevention and control in hospitals acquired a legal basis through several laws and guidelines [6]. In the 1960s the Centers for Disease Control (CDC) in the USA recommended hospitals to perform surveillance of HAI, to inform the development of control measures. Since 1970 the CDC has coordinated surveillance of HAI in a group of voluntarily cooperating hospitals known as the National Nosocomial Infections Surveillance (NNIS) system [7]. In the 1970s, the CDC‐initiated study on the efficacy of nosocomial infection control (SENIC) concluded that organised surveillance and control activities in a multimodal infection prevention and control programme could reduce HAI rates by one third [5]; since then, surveillance of healthcare‐associated infections is considered a cornerstone of prevention and control [8]. The monitoring of HAI rates has increased awareness and improved insight into patients at increased risk, enabling targeted interventions. Subsequently, interventions could also be evaluated for their effectiveness. To this day, continuous surveillance initiatives facilitate feedback and have improved institutionalised healthcare (Plan‐Do‐ Check‐Act). Moreover, sufficiently standardised surveillance programmes allow for the benchmarking of institutions as an incentive and a means of quality assessment. 1.2 HAI: current incidence and etiology At present, the most frequently diagnosed HAI in the Netherlands are surgical site infections (SSI), lower respiratory tract infections including pneumonia, bacteraemia, and urinary tract infections (UTI). Clostridioides difficile infection (CDI) is the dominant type of gastrointestinal HAI and can severely affect hospitalised patients [9, 10]. In point prevalence surveys during 2017‐2019, the three years preceding the COVID‐19 pandemic, the prevalence of SSI was 1.7% (95% confidence interval (CI) 1.6‐1.8), of pneumonia 1.1% (1.0‐1.2), of bacteraemia 0.9% (0.8‐1.0) and of symptomatic UTI 0.8% (0.7‐0.9). C. difficile was the responsible micro‐organism in 37% of the hospital‐associated gastrointestinal infections [11]. The acquisition of HAI is influenced by endogenous and exogenous risk factors. Advanced age, acute or chronic illness, impairment of organ functions, or metabolic disturbances, as well as disruption of individual microbiomes or reduced immunity are endogenous factors that additionally increase exposure to exogenous risk factors. The latter includes frequent hospital admissions, diagnostic and/or therapeutic interventions and repeated antibiotic therapy. Institutional factors such as ‐ but not limited to ‐ infection prevention practices and behaviour of healthcare workers (HCW) add 10 Chapter 1
confounding causes for HAI. Direct and conditional causes involve surgery, whereby wounds can become infected, resulting in SSI. Venous catheters, especially central venous catheters (CVC), form a port d’entrée too, allowing bacteria and other pathogens to enter the body and/or adhere to the vascular device and possibly develop into a bloodstream infection (BSI). Approximately half of BSIs are secondary to other infections, either community‐acquired or hospital‐acquired. Impaired mobility is a risk factor for developing hospital‐acquired pneumonia, and the risk is accentuated when invasive ventilation is required. Infections such as (ventilator‐associated) pneumonia and bacteraemia typically develop in seriously ill patients in whom such endogenous and exogenous factors coincide. Healthcare‐associated infections in these patients may be more challenging to prevent. Urinary catheters increase the risk of UTI, which, although less serious than SSI, BSI or pneumonia, is a frequent nosocomial infection and can result in secondary bloodstream infection. Antibiotic use can cause a severe imbalance in the human microbiome, increasing the risk of infections with opportunistic pathogens that thrive under selective pressure. The risk of acquiring a nosocomial infection is further enhanced by the frequent contact with HCWs and with other patients (through HCWs, fomites or the environment). Moreover, nosocomially transmitted pathogens are sometimes antibiotic‐resistant organisms that thrive in hospitals where antibiotics are more frequently prescribed than in the community. An infection with a resistant micro‐ organism may result in a delay of adequate treatment[12]. Almost all HAI cause delayed or inadequate recovery, additional pain and/or anxiety, and sometimes result in secondary bloodstream infection, sepsis and even permanent disability or death [10, 13‐ 20]. Cassini et al. estimated that in 2011‐2012, the burden of the five major HAI (SSI, BSI, pneumonia, UTI and CDI) together with healthcare‐associated neonatal sepsis in the European Union was 501 (95% CI 429‐582) disability‐adjusted life‐years (DALYs) per 100,000 inhabitants of the general population [10]; the burden of antibiotic‐resistant infections, mostly hospital‐acquired, was 131 (113‐149) infections per 100,000, with an attributable mortality of 6.4 (5.5‐7.5) per 100,000 (2015 data). The burden is lowest (<50/100,000) in the Netherlands, Scandinavian countries and a few others [15]. Although the recent focus has been very much on antibiotic resistance, these figures show that HAI with non‐resistant micro‐organisms likewise result in morbidity and mortality. HAI can be expressed as an incidence (e.g. events per 100 or 1,000 patients or hospital admissions) or an incidence density (e.g. events per 1,000 patient‐days or device‐days) when exposure time is taken into account. Incidence‐based surveillance is 1 11 Introduction
suited to monitor specific types of HAI and the associated risk factors in appropriate patient categories, e.g. surgery patients at risk for SSI, ventilated patients at risk for ventilator‐associated pneumonia (VAP), and patients with a CVC at risk for CRBSI. Incidence‐based surveillance of all hospital patients would be very time‐consuming and not very effective, as patients not included in SSI, VAP or CRBSI surveillance are generally at low risk to develop a HAI. An alternative is the point prevalence survey (PPS) in which a cross‐section of the hospital population is observed for one or more types of HAI at one time‐point only. These surveys are more suitable to assess all patients in the hospital than incidence‐based surveillance, as they do not include patient follow‐up. They can identify possible patient populations at increased risk and opportunities for intervention, but are less suitable to assess new risk factors. 1.3 HAI in the early 2000s: expansion of surveillance programmes (Part I) Following the SENIC study many industrialised countries have initiated regional or national HAI monitoring programmes , [5]. To allow interhospital comparisons and trend monitoring surveillance requires standardized criteria to define HAI. The US CDC were the first to establish such criteria [21], which form the basis for most other surveillance programmes [22]. In 1996 the Dutch Institute for Public Health and the Environment (RIVM) and the former Dutch Institute for Healthcare Improvement joined to form one national surveillance programme: Prevention of Hospital infections by Interventions and Surveillance (PREventie van ZIEkenhuisinfecties door Surveillance, PREZIES) (https://www.rivm.nl/prezies). To this day PREZIES, in which hospitals and the RIVM participate, enables hospitals to monitor HAI according to a standardised protocol, including a limited set of relevant literature‐based risk factors; it also provides feedback and benchmarks. The first national surveillance initiative in the Netherlands targeted SSI, complemented in 1998 by a surveillance programme focussed on the intensive care unit (ICU), where patients are more at risk of acquiring HAI than in most other hospital wards. Lasting four years, this program monitored ICU‐acquired infections, including (device‐ associated) pneumonia, BSI and UTI, and patient mortality. Chapter 2 describes the incidence of these device‐associated infections, but the data were also used to quantify the recorded risk factors, including device use, for both infection and mortality. Evaluation of the ICU‐programme led to the development of two more targeted programmes, one aimed at VAP and one at CRBSI, with more detailed patient and device‐ specific data to improve case mix correction, increase insight and provide more specific leads for interventions. Both outcome and time at risk (device‐use days) were recorded to calculate incidence densities. The first results of these two programmes are presented 12 Chapter 1
in Chapter 3 (CRBSI) and 4 (VAP). In chapter 3, the incidence of and independent risk factors for CRBSI are described. These results were also used to evaluate the data requirements of the protocol. In chapter 4, incidence of and independent risk factors for VAP are described. Meanwhile, awareness increased that national prevalence surveys of a wider range of HAI could be worthwhile for hospitals, while providing RIVM and the national government with better understanding of the disease burden imposed by the full range of HAI. Point prevalence surveys (PPS) were therefore introduced in 2007. The results of the first two years are presented in chapter 5 and describe the range of HAI prevalence, use of medical devices, and use of antibiotics among hospitals. Apart from HAI themselves, related outcomes can be monitored in surveillance programmes. HAI can lead to longer hospital admissions, repeated surgeries, readmissions, and increased mortality. These measures illustrate the impact of HAI on patients and the healthcare system. Attributing them to nosocomial infection is, however, often not straightforward and requires adjusting for the patient’s condition by using statistical approaches or relying on clinicians’ opinion. The inter‐rater reliability of a clinician‐based measure for the contribution of HAI to mortality was evaluated in a multicentre study commissioned by the European Centre for Disease Prevention and Control (ECDC). The results are presented in chapter 6. 1.4 Prevention of HAI: improving compliance to best practices (Part II) Although surveillance is known to increase awareness and can lead to improvement, in this case lower HAI rates [5, 23‐26], certain requirements must be met. HCWs must have knowledge of best practices and perceive them to be important and feasible; materials must be available and care processes optimally organised. The prevention of HAI is one aspect of ‘patient safety’. This concept within healthcare quality and the notion that it should be embedded and fostered throughout the entire healthcare system was developed in the USA [27]. Following the seminal studies from Berenholtz and Pronovost et al. [28, 29], the Institute for Healthcare Improvement (IHI) in the USA, in its 100,000 Lives Campaign to improve patient safety and outcomes in 2004, recommended as one of six interventions the “central line bundle” [30]. Central line bundles to prevent CRBSI or central‐line associated BSI (CLABSI) have been implemented in many hospitals and national surveillance programmes since [30‐32]. The bundle approach emphasises compliance to a coherent set of best practices instead of an uncoordinated introduction and monitoring of individual best practices. Included in these bundles is hand hygiene during the CVC insertion, as hand hygiene is a corner stone of infection prevention in general. Improving hand hygiene has proven to be challenging [33]. Whether a CRBSI 1 13 Introduction
prevention bundle, a World Health Organisation (WHO)‐based intervention addressing hand hygiene, or both in combination would be effective in CRBSI prevention was evaluated in a wide range of European hospitals by the PROHIBIT (Prevention of hospital infections by intervention and training) study. As discussed in chapter 7, both process parameters ‐ the CVC insertion score and hand hygiene compliance ‐ were measured, and facilitating factors and barriers were evaluated in‐depth in a related study of six hospitals [34]. The individual HCW’s response to a hand hygiene intervention remains terra incognita in most studies. In chapter 8 additional analyses of individual hand hygiene in seven PROHIBIT hospitals are presented. More insight into personal uptake of a hand hygiene intervention enables the design of more effective interventions in the future. To ensure that a large number of hospitals adopt a multifaceted intervention without too much delay, the key is a national or otherwise large‐scale movement, such as the IHI or, based on this initiative, the Dutch Hospital Patient Safety Programme (DHPSP), starting in 2009. The DHPSP encouraged Dutch hospitals to introduce a CRBSI prevention bundle and 62% of them acted upon this, with a concurrent reduction in CRBSI rates. In chapter 9 the association between bundle compliance and CRBSI risk is evaluated. Finally, chapter 10 presents a discussion and reviews the merits and limitations of the surveillance and study methods. It will briefly consider the consequences of the current HAI incidence and achieved reductions for the relevance of and future set‐up of HAI surveillance, particularly with regard to CRBSI in the Netherlands. The relevance of hand hygiene and the current compliance in Dutch hospitals are additionally discussed. 14 Chapter 1
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Part I HAI in the early 2000s: expansion of surveillance programmes Part I HAI in the early 2000s: expansion of surveillance programmes
Chapter 2
Incidence and risk factors of deviceassociated infections and associated mortality at the intensive care in the Dutch surveillance system Tjallie I.I. van der Kooi, Annette S. de Boer, Judith Manniën, Jan Wille, Mariëlle T. Beaumont, Ben W. Mooi, Susan van den Hof Intensive Care Medicine 2007(33): 271-278
ABSTRACT Objective: To examine the incidence of and risk factors for device‐associated infections and associated mortality. Design and setting: Prospective surveillance‐based study in ICUs of 19 hospitals in The Netherlands. Patients: The study included 2,644 patients without infection at admission during 1997‐ 2000, staying at the ICU for at least 48 h. Measurements and results: The occurrence of ventilator‐associated pneumonia (VAP), central venous catheter (CVC)‐related bloodstream infection (CR‐BSI), urinary catheter‐ associated urinary tract infection (CA‐UTI) and risk factors was monitored. Of the ventilated patients 19% developed pneumonia (25/1,000 ventilator days); of the patients with a central line 3% developed CR‐BSI (4/1,000 CVC days) and of the catheterized patients 8% developed CA‐UTI (9/1,000 catheter days). Longer device use increased the risk for all infections, especially for CR‐BSI. Independent risk factors were sex, immunity, acute/elective admission, selective decontamination of the digestive tract, and systemic antibiotics at admission, dependent upon the infection type. Crude mortality significantly differed in patients with and without CR‐BSI (31% vs. 20%) and CA‐UTI (27% vs. 17%) but not for VAP (26% vs. 23%). Acquiring a device‐associated infection was not an independent risk factor for mortality. Being in need of ventilation or a central line, and the duration of this, contributed significantly to mortality, after adjusting for other risk factors. Conclusions: Device use was the major risk factor for acquiring VAP, CR‐BSI and CA‐UTI. Acquiring a device‐associated infection was not an independent risk factor for mortality, but device‐use in itself was. 22 Chapter 2
INTRODUCTION Information on the incidence of different intensive care unit (ICU) acquired infections and their risk factors can help clinicians, other healthcare workers and hospital policy makers to try to reduce the burden of ICU‐acquired infections in patients. This will not only lead to less suffering, but may also be cost saving. In a European prevalence survey in which 78 ICUs in the Netherlands participated 16% of the Dutch patients had an ICU‐ acquired infection [1]. In the Netherlands PREZIES, a national network, started a surveillance of nosocomial infections at the ICU in 1997 which continued until the end of 2000. As in most European surveillance systems, the definitions used were based on those of the Centers for Disease Control (CDC)/National Nosocomial Infections Surveillance (NNIS) system. However, unlike the surveillance in the United States [2] and Germany [3] this surveillance is patient based instead of unit based. The infection rates have been previously reported to the participating hospitals, in a Dutch journal [4] and in abstract form [5]. All ICU‐acquired infections were recorded, but because most infections at the ICU are device associated, we have chosen to present results of device‐associated infections only. Here we report the rates of VAP, CR‐BSI, urinary catheter a demeure (CAD)‐ associated urinary tract infection (CA‐UTI), mortality and the effects of various risk factors. We also investigate the effect that the duration of the use of invasive devices has. MATERIAL AND METHODS PREZIES, established in 1996, is a cooperation of participating hospitals, the Dutch Institute for Healthcare Improvement (CBO) and the National Institute for Public Health and the Environment (RIVM). During the period July 1997‐ December 2000 19 Dutch hospitals (c. 20 % of all hospitals in The Netherlands) with 23 ICUs prospectively collected data of intensive care patients on a daily basis according to the PREZIES protocol. Both university and other hospitals participated, but university hospitals were relatively better represented (three out of seven). The study period varied between 2 and 39 months, with a median of 14. The average capacity of the participating ICUs was 8 beds (range 5 to 12). Experts in the field of intensive care medicine and nosocomial infections developed the protocol in consultation with the participating hospitals. In each hospital a multidisciplinary team of the infection control professional, ICU nurses, the medical microbiologist, and the ICU physician performed the surveillance. The procedure of data collection and the tasks of the involved persons were established within each hospital. The definitions of pneumonia, sepsis, UTI and risk factors were standardized and based 2 23 Device-associated infections and associated mortality in the ICU
on those of the CDC/NNIS system. An infection was deemed device‐associated when the day of or the day before the infection occurred was a device day. All patients who stayed at the ICU for 48 hours or more were included in the surveillance and followed from admission until discharge, death, or the day of withholding treatment because of their moribund condition. The study period per patient was restricted up to 56 days. After discharge from the ICU patients were followed‐up for infection for another 24 h. The surveillance included 4,105 patients, for 3,921 of whom sufficient data were available. Of these patients 1,277 (33%) had an infection when entering the ICU and were analyzed separately (data not shown). The remaining 2,644 patients remained at the ICU for a total of 25,432 days. Median ICU stay was 6 days, interquartile range (IQR) 6 days. Patient characteristics of patients with and without a device‐associated infection are presented in the Electronic Supplementary Material (ESM; Appendix A). The following patient characteristics were recorded: demographic data, medical discipline treating the patient (specialty), Acute Physiology and Chronic Health Evaluation (APACHE) II score, immunity status (normal immunity, leukopenia (leukocytes polymorphonuclear cells < 0.5 x 109/l), and otherwise impaired immunity (defined as a chronic low or recent high dose of corticosteroids, chemotherapy, dialysis or systemic diseases such as leukemia or AIDS in patients with leukocytes polymorphonucelair cells > 0.5 x 109/l), origin (e.g. community, ward) and whether admission was acute or elective. The use of medical devices (mechanical ventilation (including intubation without ventilation and/or having a tracheostoma); CVC and indwelling transurethral or suprapubic catheter), systemic antibiotics and selective decontamination of the digestive tract (SDD) were recorded daily. Two or more central venous catheters on 1 day were counted as one CVC day. For each nosocomial infection the infection date, type of infection, and microbiological test result were recorded. Pneumonias registered within 4 days from an earlier pneumonia in the same patient, and sepsis and UTI occurring within 7 days after the same kind of infection were not regarded as new infections, according to the European protocol for nosocomial infection surveillance [6]. This led to the exclusion of about 2.5% of infections but did not affect the calculation of risk factors, as only the first VAP, CR‐BSI, or CA‐UTI was included in the regression analysis. Any new pathogens with these excluded infections were presented with the former infection. In patients who developed a device‐related infection the time at risk was defined as the number of days from the first day the device was used until the day on which the device‐related infection was diagnosed or, if no infection occurred, until the last day of device use. Observations were censored if the device was no longer used or if the patients with the device were transferred to other hospitals, deceased or when active (life‐supporting) treatment was 24 Chapter 2
withheld. Before aggregation individual data were checked for completeness and consistency. Patient and treatment characteristics were determined in patients with and without infection. The incidence of infections per 1,000 device days was calculated. To calculate the incidence density of subsequent periods the numbers of days at risk of a patient were divided over and thus contributed to the subsequent categories, as described by McLaws and Berry [7]. Kaplan Meier survival analysis and Cox regression in SAS 9.1 [8] were used to calculate the relative risk of acquiring infection for patient and treatment characteristics with regard to the time at risk. Logistic regression was used to determine the effect of duration of device use on infection and the effect of risk factors on mortality. For uniformity we used the same categories of risk factors for all infections. Risk factors with a p‐value of 0.20 or less in the univariate regression were initially included in the multiple regression models. The model was reduced by means of manual backward elimination. Risk factors, contributing significantly to the goodness of fit of the model but not statistically significant independent risk factors in themselves are also shown. Statistical significance was defined at p ≤ 0.05. RESULTS Device‐related infection rates, and ICU stay Overall 58% of patients were mechanically ventilated (568 days per 1,000 ICU days), 61% had a CVC (506 days per 1,000 ICU days) and 86% had an indwelling catheter (818 days per 1,000 ICU days). As many as 71% of the patients had two or more different devices during (part of) their ICU stay and 43% had all three. Of all pneumonia cases 86% were associated with mechanical ventilation. VAP occurred in 19% of ventilated patients, with an incidence of 25 per 1,000 ventilator days. Of all sepsis cases 34% were related to a central vascular catheter. Of the patients with a CVC 3% developed CR‐BSI, with an incidence of 4 per 1,000 CVC days. Of all UTI cases 95% were associated with the use of an indwelling catheter. CA‐UTI occurred in 8% of the patients with an indwelling urinary catheter, with an incidence of 9 per 1,000 CAD days. Median ICU stay was 7 days (IQR 7) in ventilated patients without VAP and 17 days (IQR 17) in those with infection; 6 days (IQR 8) in patients with a central vascular catheter without CR‐BSI and 24 days in those with infection; and 6 days (IQR 6) in patients with a urinary catheter without CA‐UTI and 18.5 days (IQR 16.5) in those who developed infection. Table 1 shows the median duration of device use and the IQR. Patients who developed a device‐associated infection had significantly longer ICU stays. 2 25 Device-associated infections and associated mortality in the ICU
Table 1: Median duration of device use for all patients, those that develop infection and up to infection. Median duration of device use (interquartile range) for all patients on device for all patients that developed the device‐ associated infection up to the first device‐associated infection ventilation 6 (9) 14 (15) 6 (5) central venous catheterization 5 (5) 21 (16) 9 (13) urinary catheterization 6 (7) 17 (17.5) 8 (9) Duration of device use as a risk factor for infection Figure 1 shows the incidence densities of patients at risk that developed an infection, according to the duration of mechanical ventilation, central vascular catheterization or urinary catheterization. The incidence density of CR‐BSI and CA‐UTI varied relatively little according to duration of CVC and CAD use, but that of VAP decreased when the ventilation lasted longer than 9 days. We also calculated the VAP risk per day. Figure 2 presents the risk of patients expressed as a proportion of those at risk for at least the number of indicated days. The risk increased until day 5, remained more or less constant unto day 10 and decreased thereafter. There were too few patients at risk for more than 3 weeks to draw conclusions. Therefore we summarize these. Cox regression takes into account the effect of time at risk, but its relative risks do not give insight into its effect. Logistic regression does not integrate the time at risk in the calculation of its odds ratios. However, this makes it possible to express the effects of discerned periods at risk. Therefore Table 2 shows the odds ratios of increased device use (until infection), determined by univariate logistic regression. Prolonged device use significantly increased the risk of acquiring a device‐associated infection. The risk of CR‐BSI was affected most: the odds ratio for a CVC in situ for 5‐9 days was 4.3 and for a period of 10 days or longer 8.4. Device use affected the risk of VAP the least. Being on a ventilator for at least 10 days was not associated with a higher risk than being ventilated for 5‐9 days. This is reflected in the decreasing incidence density in Figure 1. Other risk factors for infection Incidence densities for different categories of patients are given in the ESM (Appendix B). Table 3 presents the relative risks determined by multivariate Cox regression (univariate results in the ESM, Appendix C). Female sex and SDD use were associated with lower VAP risk. An APACHE II score of 20 or greater was associated with a higher risk. Only SDD use affected the VAP risk significantly. The only independent risk factor for CR‐BSI was acute admission. Acutely admitted patients had a lower risk for CR‐BSI. Independent risk factors for CA‐UTI were female sex, impaired immunity, acute admission, and systemic 26 Chapter 2
0 10 20 30 40 50 1-4 5-9 10-14 15+ device use days (until infection) incidence density (per 1000 device days) VAP CVC sepsis CAD UTI Figure 1: Incidence densities of device‐associated infections per 1,000 device days, according to duration of device use. 0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 1 2 3 4 5 6 7 8 9 10111213141516171819202122+ xth day at risk % acquiring VAP of patients at risk, per day Figure 2: The proportion of all patients ventilated for x days or longer that develop VAP at day x and 95% confidence intervals. 2 27 Device-associated infections and associated mortality in the ICU
Table 2: Odds ratios for duration of device use, determined by univariate logistic regression and 95% confidence intervals. Device use Duration of device use 1‐4 days 5‐9 days (95% CI) ≥ 10 days (95% CI) ventilation 1 1.9* (1.4‐2.6) 1.6* (1.1‐2.2) central venous catheterization 1 4.3* (1.7‐10.7) 8.4* (3.4‐20.4) urinary catheterization 1 1.6* (1.0‐2.4) 3.3* (2.2‐4.9) * p< 0.05 antibiotics. Acute admission had no proportional hazard over time, indicating that the effect of this risk factor changed over time. To account for this an interaction term with time at risk was included in the analysis. The effect of acute admission was highest at the start of the urinary catheterization and decreased with continuing ICU stay/catheterization at a factor of 10% per day. Mortality Developing VAP was not associated with a higher crude mortality (26.0% and 23.2% in patients with and without infection, respectively). Developing a CR‐BSI or a CA‐UTI was associated with a (nearly) significantly higher crude mortality: 30.9% versus 20.2% (p=0.06) in patients with a CVC and 26.7% versus 16.7% (p=0.002) in patients with a CAD. In multivariate regression developing a device‐associated infection was not associated with mortality (Table 4). Micro‐organisms Only the culture of the first infection of its kind is given here. During the first 4 days of ventilation 37% of the isolates for VAP were flora associated with early‐onset VAP: Staphylococcus aureus, Streptococcus pneumoniae, and Haemophilus influenzae. In pneumonia patients ventilated for 5 days or more less H. influenzae was isolated and more Pseudomonas aeruginosa and Enterobacteriaceae. In CA‐UTI patients intestinal flora contributed 69% in the first 4 days. This decreased to 44%, whereas P. aeruginosa and Klebsiella pneumoniae increased in frequency. Staphylococci were found in 60% of the isolates of CR‐BSI patients in the first 2 weeks. After 2 weeks they were only found in 41% of the isolates whereas Enterobacteriaceae were more frequently found with increasing duration of CVC. 28 Chapter 2
Table 3: Relative risks (RR) for infection, with 95% confidence intervals (CI), based on multivariate Cox regression. VAP CR‐BSI CA‐UTI RR 95% CI RR 95% CI RR 95% CI Sex Male Female 1 0.8* (0.6‐1.0) 1 1.4* (1.0‐1.8) APACHE II score 0‐19 ≥20 1 1.2 (1.0‐1.5) Immunity Not impaired Leucopenia Otherwise impaired immunity 1 ‡ 2.5** (1.5‐4.0) Admission Planned Acute Interaction with time 1 0.5** (0.3‐1.0) 1 1.8* 0.9** (1.0‐3.3) (0.9‐1.0) SDD use no SDD‐use SDD use 1 0.6** (0.4‐0.9) Systemic antibiotics at admission no SAB use SAB use 1 0.5** (0.3‐1.0) * 0.05 < p < 0.1 ** p < 0.05 ‡ No cases in category Analysis of risk factors for which interaction with time was significant was executed with interaction terms included for all categories. However only significant interactions are shown. DISCUSSION This is one of the few prospective studies to investigate both the incidence of and the risk factors for different types of device‐related ICU‐acquired infections as well as their effect on mortality in the same patient population. Nearly every fifth ventilated patient without a preexisting infection, admitted for 48 hours or more at Dutch ICUs developed VAP. Infection rates in patients with a CVC or CAD were 3% and 8% respectively. Longer device use increased the risk of acquiring an infection, especially CR‐BSI, and CA‐UTI. Device‐ associated infections did not significantly increase the mortality of device‐assisted patients after adjustment for case‐mix. Device utilization rates and infection rates Device use was high in our population. The overall mean ventilator use rate reported by the NNIS was approximately 40% [9] whereas this was 58% in our study. The same applies for central line use (approx. 50% and 61%, respectively) and urinary catheter use 2 29 Device-associated infections and associated mortality in the ICU
Table 4: Multivariate odds ratios (OR) for mortality, with 95% confidence intervals (CI) Ventilated patients (n=1516) Patients with a CVC (n=1604) Patients with a urinary catheter (n=2259) OR 95% CI OR 95% CI OR 95% CI Age ≤39 years 40‐70 years ≥ 70 years 1 1.7** 3.0** (1.1‐2.8) (1.9‐4.8) 1 1.3 2.7** (0.8‐2.2) (1.6‐4.5) 1 1.6** 2.8** (1.0‐2.5) (1.8‐4.4) APACHE II score 0‐19 ≥20 1 1.9** (1.5‐2.4) 1 1.7** (1.3‐2.3) 1 1.9** (1.5‐2.4) Specialty Surgery/traumatology Internal medicine Cardiology/‐surgery Neurology/‐surgery Other 1 1.7** 2.4** 1.8** 1.3 (1.5‐2.7) (1.6‐3.6) (1.2‐2.8) (0.8‐2.1) 1 2.1** 2.4** 1.9** 1.8** (1.5‐2.9) (1.6‐3.6) (1.2‐3.2) (1.1‐2.8) 1 1.9** 2.6** 1.8** 1.4 (1.4‐2.7) (1.8‐3.8) (1.2‐2.7) (0.9‐2.2) Admission Planned Acute 1 1.4** (1.0‐1.8) Systemic antibiotics at admiss. No Yes 1 1.6** (1.1‐2.4) 1 1.4 (0.9‐2.1) 1 1.5** (1.1‐2.3) Ventilation No Yes 1 3.9** (2.5‐6.0) 1 4.8** (3.3‐7.0) CVC No Yes 1 1.7** (1.2‐2.3) 1 1.8** (1.3‐2.5) Duration of device use < 4 days 5‐14 days >15 days 1 1.5** 1.6** (1.1‐2.0) (1.1‐2.2) 1 1.6** 2.8** (2.0‐4.0) (2.5‐6.0) * 0.05 < p < 0.1; ** p < 0.05 (70% and 86%, respectively). These differences could be the result of different selections of patient populations (all ICU patients in NNIS vs. patients staying at least 48 h in this study) which is likely to be reflected in their need of device assistance, but also of differences in patient management. The inclusion criterion of ICU stay 48 h or longer in our study probably resulted in higher infection incidence rates. Also, some publications report incidence density rates calculated with all ICU or device days instead of the number of days up to infection, resulting in lower rates, as pointed out for ventilated patients by Eggimann et al [10]. This is the case with figures derived from NNIS data [9]. Our pneumonia rate of 19% in ventilated patients and 25 per 1,000 ventilator days falls within the reported rates in more recent studies with comparable methods of diagnosing VAP (cultures usually from endotracheal aspirates or sputum): 9.8/1,000 30 Chapter 2
ventilator days [9], 15% [11], 15% [12] and 44.0 per 1,000 ventilator days at risk [10], although it seems relatively high. In our study a pneumonia was considered ventilator‐ associated when the infection day or the day before was a ventilator day. Many studies consider VAP when a patient is ventilated longer than 48 h [13]. This difference may account in part for a relatively high VAP rate. The CR‐BSI rate among patients with a central line was 3%. This figure is comparable to rates in other studies [14‐16]. Our CA‐ UTI rate of 8% was also in accordance with earlier reported CA‐UTI rates [17‐19]. Risk factors for infection The increased VAP, CR‐BSI and CA‐UTI risk as a consequence of device use (in general) and the effects of some of the other risk factors, for example, sex, were comparable to those reported previously [13;20;21]. After much debate [22‐24] a recent Cochrane review concluded that SDD, aimed at eradicating colonization of aerobic, potentially pathogenic micro‐organisms from the oropharynx, stomach, and gut, does benefit the ventilated patient [25]. In accordance with this, we found a decreased relative risk of acquiring VAP when receiving SDD. Although reported in several other studies the use of systemic antibiotics was not associated with VAP in this group. Ibrahim et al [12] found that multiple central venous line insertions increased the VAP risk, but in our data a central vascular catheter was not associated with a higher VAP risk. Also, in CVC patients, ventilation did not affect the risk of CR‐BSI, unlike the findings in another study [20]. An unexpected and unaccountable finding was acute admission lowering the risk of CR‐BSI. Impaired immunity increased the CA‐UTI risk whereas the use of systemic antibiotics at admission was associated with a lower risk. Ventilation or a central vascular catheter did not affect the CA‐UTI risk in our study. Duration of device use Our data showed that a longer time at risk increased the chance of infection. However, this association was less for VAP, when ventilation lasted longer than approx. 10 days, indicating that ventilation provokes pneumonia relatively early, rendering patients remaining ventilated without infection as ‘survivors’ with lower intrinsic risk for VAP [26]. The incidence density was highest in patients ventilated for 5‐9 days (Figure 1). Figure 2 shows that the proportion of patients developing VAP increased until day 5. Thereafter the percentage remained more or less constant until day 10 and declined slightly thereafter, although this was not statistically significant. An increase in VAP risk during the first 5 days or so, as we observed, has been reported by almost all studies [26‐28]. The results in patients ventilated for a longer period are less consistent. Unfortunately, the different ways of expressing the daily risk complicates comparisons between studies. 2 31 Device-associated infections and associated mortality in the ICU
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