Skip to main content
Download PDF
Download ePub

Candida and the Gram-positive trio: testing the vibe in the ICU patient microbiome using structural equation modelling of literature derived data

Emerging Themes in Epidemiology volume 19, Article number: 7 (2022) Cite this article

Abstract

Background

Whether Candida interacts with Gram-positive bacteria, such as Staphylococcus aureus, coagulase negative Staphylococci (CNS) and Enterococci, to enhance their invasive potential from the microbiome of ICU patients remains unclear. Several effective anti-septic, antibiotic, anti-fungal, and non-decontamination based interventions studied for prevention of ventilator associated pneumonia (VAP) and other ICU acquired infections among patients receiving prolonged mechanical ventilation (MV) are known to variably impact Candida colonization. The collective observations within control and intervention groups from numerous ICU infection prevention studies enables tests of these postulated microbial interactions in the clinical context.

Methods

Four candidate generalized structural equation models (GSEM), each with Staphylococcus aureus, CNS and Enterococci colonization, defined as latent variables, were confronted with blood culture and respiratory tract isolate data derived from 460 groups of ICU patients receiving prolonged MV from 283 infection prevention studies.

Results

Introducing interaction terms between Candida colonization and each of S aureus (coefficient + 0.40; 95% confidence interval + 0.24 to + 0.55), CNS (+ 0.68; + 0.34 to + 1.0) and Enterococcal (+ 0.56; + 0.33 to + 0.79) colonization (all as latent variables) improved the fit for each model. The magnitude and significance level of the interaction terms were similar to the positive associations between exposure to topical antibiotic prophylaxis (TAP) on Enterococcal (+ 0.51; + 0.12 to + 0.89) and Candida colonization (+ 0.98; + 0.35 to + 1.61) versus the negative association of TAP with S aureus (− 0.45; − 0.70 to − 0.20) colonization and the negative association of anti-fungal exposure and Candida colonization (− 1.41; − 1.6 to − 0.72).

Conclusions

GSEM modelling of published ICU infection prevention data enables the postulated interactions between Candida and Gram-positive bacteria to be tested using clinically derived data. The optimal model implies interactions occurring in the human microbiome facilitating bacterial invasion and infection. This interaction might also account for the paradoxically high bacteremia incidences among studies of TAP in ICU patients.

Keypoints

  • GSEM modelling of published ICU infection prevention data from > 250 studies enables a test of and provides support to the interaction between Candida and Gram-positive bacteria.

  • The various ICU infection prevention interventions may each broadly impact the patient microbiome.

Introduction

While Candida rarely causes ventilator associated pneumonia (VAP), and blood stream infections (BSI) with Candida (candidemia) are uncommon in the ICU, surprisingly, Candida colonization is associated with higher mortality and poor patient outcomes among ICU patients receiving mechanical ventilation (MV) [1, 2]. The basis for this association remains unclear and interactions between Candida and bacterial colonizations causing invasive infection have been implicated from preclinical studies [3,4,5,6,7,8]. Moreover, Gram-positive bacteria, such as Staphylococcus aureus account for the majority of candidemia associated mixed blood stream infections among ICU patients [9] and bacterial colonization is a key determinant [10].

Evaluating the possible clinical relevance of microbial interactions is unlikely to be achieved within the constraints of a single center study. Moreover, quantifying the impact of the various interventions on not only the presence but also the biological activity of microbial colonization within the microbiome is not simple. Structural equation modelling of literature derived data offers a novel approach [11,12,13,14,15].

Several anti-septic, antibiotic, anti-fungal, or non-decontamination based interventions have been studied for the prevention of ICU acquired infections. These methods target bacterial and Candida colonization with variable specificity [12, 16]. Of note, Topical antibiotic prophylaxis (TAP) based methods appear to be the most effective but these are always used in combination regimens together with an antifungal (termed selective digestive decontamination; SDD) due to their broad microbiome effects [12, 16]. Yet surprisingly, the incidences of candidemia, VAP and bacteremia with Staphylococcus aureus, coagulase negative Staphylococci (CNS) and Enterococci are unusually high among studies of methods using TAP and moreso among the concurrent control groups of these studies [17,18,19,20,21]. These paradoxically high incidences are unexplained.

The objective here is to develop candidate generalized structural equation models (GSEM) of infections arising from colonization with Candida and Gram-positive bacteria with versus without the interaction terms as postulated in the literature (Fig. 1). The optimal model emerges after confronting these models using group level infection data from published studies of ICU patient groups with various group level exposures.

Fig. 1
figure 1

Theoretical model of established and potential factors (structural components; boxes on the left) bearing on the interaction between bacterial and candida colonization towards causing blood stream (BSI) and pneumonia (VAP) infections (measurement components; counts on the right). These elements are required to address the central research question (depicted by the red vertical dotted arrow labelled ‘?’) being whether the candida colonization and bacterial colonization interact to enhance invasive bacterial infections. As the colonizing candida and bacteria might change either in numbers or in activity, these are latent variables determined by the measurement components. Most individual elements are accepted although whether CRF are risk factors for bacterial colonization is unknown (sloping dotted arrow) and require testing. CRF candidemia risk factors, LOS length of ICU stay, RT candida respiratory tract candida (numbers of patients with VAP where Candida is identified), VAP ventilator associated pneumonia

Full size image

Materials and methods

Being an analysis of published work, ethics committee review of this study was not required.

Study selection and decant of groups

The literature search uses systematic reviews of several infection prevention interventions found in The Cochrane database of systematic reviews as a starting point. These systematic reviews were identified by searching the Cochrane systematic review database using the following search terms; mechanical ventilation, ventilator associated pneumonia, blood stream infections and Candida to identify relevant systematic reviews of infection prevention interventions [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]. The basic inclusion criterion for individual studies identified within these systematic reviews was patient groups requiring prolonged (> 24 h) ICU stay within studies of ICU infection prevention interventions applicable to patients receiving mechanical ventilation (MV) with, as an additional inclusion criterion, group level Candida, Staphylococcus aureus, CNS and Enterococcal infection data reported. The intervention studies were classified into four categories based on the principal component of the intervention. Studies meeting these criteria but without ICU infection prevention interventions (observational studies) were sourced to provide benchmark incidence data.

Most of the studies had been cited within either one of the systematic reviews of The Cochrane review database or within additional systematic reviews found by snowball sampling using the ‘Related articles’ function within Google Scholar [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59]. The snowball sampling also identified additional eligible studies. The study decant used here is as described previously [21] and is detailed in Fig. 2.

Fig. 2
figure 2

Search method, screening criteria and resulting classification of eligible studies and subsequent decant of component groups occured in the following steps; (1) An electronic search for systematic reviews in the Cochrane database using search terms; “ventilator associated pneumonia”, “mechanical ventilation”, “intensive care unit”, up to December 2021; (2) The systematic reviews were then searched for studies of patient populations requiring prolonged (> 24 h) ICU admission (3) The studies were triaged from the systematic reviews into one of five categories; studies in which there was no intervention (observational studies), studies of various non-decontamination methods or various methods of decontamination using either anti-septic, antibiotic (TAP) or antifungal prophylaxis. (4) Studies identified outside of these systematic reviews were included by a ‘snowball’ search for potentially eligible studies using the ‘related studies’ function in Google scholar. (5) the studies were reviewed for potentially eligible studies and screened against inclusion and exclusion criteria. Any duplicate or ineligible studies were removed and (6) The component groups were decanted from each study being control (rectangles), intervention (ovals) and observation (diamond) groups. Note; the total numbers do not tally as some systematic reviews provided studies in more than one category and some studies provided groups in more than one category and some studies have unequal numbers of control and interventions groups. TAP  topical antibiotic prophylaxis

Full size image

Structural equation modelling

In these GSEM models, the Candida and Gram-positive bacterial infection data serve as the measurement components, the group level exposure parameters serve as the structural components and colonization with Candida, and individual Gram-positive bacteria, each represented as latent variables, link the structural and measurement components.

Measurement components

The incidences of VAP with Staphylococcus aureus as well as the incidences of bacteremia with each of Staphylococcus aureus, CNS and Enterococci were extracted. As Candida is generally not counted as a cause of VAP, the count of Candida as a respiratory tract (RT Candida) isolate among patients with suspected VAP was recorded along with candidemia counts. Likewise, Enterococci and CNS are rarely recorded among VAP isolates. The use of Center for Disease control (CDC) criteria, being the requirement for at least two positive cultures for diagnosis of CNS bacteremia, was recorded. Counts for all subspecies of Candida, CNS and Enterococci were included. These were each expressed as a proportion using the number of patients with prolonged (> 24 h) ICU stay as the denominator. Note that colonization with Candida, Staphylococcus aureus, CNS and Enterococcal colonization are each derived within the models as latent variables and any colonisation data within any study was not used.

Structural components

The following data were used to form the structural components of the models; year of study publication, origin from trauma ICU’s, whether more than 90% of patients of the group received more than 24 h of MV, and the mean (or median) length of ICU stay (LOS) for the group. In the extraction of MV percentages, if this was not stated for any group, the percentage receiving MV was assumed to be less than 90%. In the extraction of LOS data from the studies, surrogate measures including mean (or median) length of mechanical ventilation were taken if the length of LOS was not available.

Also, the presence of any of the following group wide risk factors for candidemia and invasive Candida infection were noted; liver transplantation or liver failure, use of parenteral nutrition, surgery for intestinal perforation, pancreatitis and being colonized with Candida, however that was defined. An anti-septic exposure included use of agents such as chlorhexidine, povidone-iodine and iseganan. All anti-septic exposures were included regardless of whether the application was to the oropharynx, by tooth-brushing or by body-wash.

Topical antibiotic prophylaxis (TAP) is defined here as the group wide application of topical antibiotic prophylaxis to the oropharynx or stomach without regard to the specific antibiotic constituents. The antibiotic-based regimens often use in addition protocolized parenteral antibiotic prophylaxis (PPAP), being the protocol driven group wide use of any parenteral antibiotic used on a prophylactic basis. Group wide exposure to anti-fungal prophylaxis was identified whether this was as a single agent (SAF) or used in combination with TAP within an SDD regimen, without regard to the specific anti-fungal agent.

Candidate SEM models

Four candidate GSEM models were developed in each of which colonization with Candida and the three individual Gram-positive bacteria constitute four latent variables. The models were constructed with and without the inclusion CRF as a predictor of bacterial colonisations, and with and without interaction terms between the colonization latent variables.

Because the observations are clustered by study, in each model a study identifier was used in order to generate a robust variance covariance matrix of the parameters of each coefficient estimate. The GSEM model with the lowest Akaike's information criterion (AIC) score was selected as having parsimony and optimal fit from among the candidate models using the ‘GSEM’ command in Stata (Stata 17, College Station Texas, USA) [60].

Visual benchmarking

Scatter plots of the Candida, Staphylococcus aureus, CNS and Enterococcal infection incidence proportion data versus group mean LOS were generated to facilitate a visual survey of the entire data used in the analysis. To facilitate this visual survey, benchmarks, being the linear regression of logit transformed incidence proportion versus LOS derived for each isolate type was generated using the groups of the observational studies.

Results

Characteristics of the studies

Of the 283 studies identified by the search, 135 were sourced from 37 systematic reviews (Table 1) [22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58] with 148 found during previous searches or by snowball sampling (Fig. 2). Most studies were published between 1990 and 2010 and most had a group mean LOS exceeding ten days. A minority originated from either North American or trauma ICU’s. Twelve studies had more than one type of intervention groups and ten studies had no control group. The majority of groups from studies of infection prevention interventions had less than 150 patients per group versus more than 150 patients in the observational studies.

Table 1 Characteristics of studies
Full size table

Of the 460 groups from 283 studies, there were 25 groups from 13 studies with mean LOS less than 5 days including the largest of which (> 120,000 patients), being a study of targeted versus universal decontamination versus standard care [61].

There was a broad range of infection prevention exposures. The majority of data for anti-fungal exposures occurred in combination with TAP exposure within Antibiotic studies in the context of an SDD regimen for which the antifungal was topical amphotericin being used in 50 groups. Exposure to anti-fungal prophylaxis as a single agent occurred (within Anti-fungal studies) in only nine groups of studies of which five studies selected patients on the basis of risk factors for invasive candida infection. The TAP exposures included either topical polymyxin or a topical aminoglycoside or both in every case except four intervention groups. PPAP, most commonly a cephalosporin, was used within ten control groups and 48 intervention groups of TAP studies.

Infection data

Across all intervention categories, the incidences for Candidemia (Fig. 3) and RT Candida (Additional file 1: Fig. S1), S aureus VAP (Fig. 4) and bacteremia (Fig. 5) and CNS (Fig. 6) and Enterococcal bacteremia (Fig. 7) varied in each case by > 100 fold and ranging approximately tenfold above and below the respective benchmarks. In each case, the incidence proportions mostly straddle the respective benchmarks with exceptions noted in the figure legends.

Fig. 3
figure 3

Candidemia (candida BSI) incidence proportion (logit scale) versus group mean length of stay (LOS; days) among categories of groups receiving either infection prevention interventions or control exposures. In each panel, the linear regression line derived from the observation studies is shown as a benchmark. Note that the candidemia incidence among the antifungal study intervention groups are asymmetrically distributed below the benchmark. The panels for RT candida incidence proportion versus group mean length of stay (LOS; days) are shown as Additional file 1

Full size image
Fig. 4
figure 4

S. aureus VAP incidence proportion (logit scale) versus group mean length of stay (LOS; days) among categories of groups receiving either infection prevention interventions or control exposures. In each panel, the linear regression line derived from the observation studies is shown as a benchmark. Note that the S. aureus VAP incidences among the Antibiotic study control groups are asymmetrically distributed above the benchmark. The panels for the SAF studies are not shown as there were no observations in each

Full size image
Fig. 5
figure 5

S. aureus BSI (bacteremia) incidence proportion (logit scale) versus group mean length of stay (LOS; days) among categories of groups receiving either infection prevention interventions or control exposures. In each panel, the linear regression line derived from the observation studies is shown as a benchmark. Note that the S. aureus BSI incidences among the antiseptic intervention groups are asymmetrically distributed below the benchmark. The panels for the SAF and anti-septic studies are not shown as there were fewer than three observations in each

Full size image
Fig. 6
figure 6

CNS BSI (bacteremia) incidence proportion (logit scale) versus group mean length of stay (LOS; days) among categories of groups receiving either infection prevention interventions or control exposures. In each panel, the linear regression line derived from the observation studies is shown as a benchmark. Note that the CNS BSI incidences among the control and intervention groups of the Antibiotic studies are asymmetrically distributed above the benchmark. The panels for the SAF and anti-septic studies are not shown as there were fewer than three observations in each

Full size image
Fig. 7
figure 7

Enterococcal BSI (bacteremia) incidence proportion (logit scale) versus group mean length of stay (LOS; days) among categories of groups receiving either infection prevention interventions or control exposures. In each panel, the linear regression line derived from the observation studies is shown as a benchmark. Note that the Enterococcal BSI incidences among the control and intervention groups of the Antibiotic studies are asymmetrically distributed above the benchmark. The panels for the SAF and anti-septic studies are not shown as there were fewer than three observations in each

Full size image

The mean control and intervention group incidences of VAP and bacteremia for each of S aureus (Fig. 2), CNS (Fig. 3) and Enterococci (Fig. 4) were generally similar to the benchmark derived from observational groups with the exception that S aureus VAP incidences among the control groups of Antibiotic studies were generally approximately five percentage points above the respective benchmark and the mean incidences of infection for each of CNS bacteremia (Fig. 3) and Enterococcal bacteremia (Fig. 4) among the control and intervention groups of Antibiotic studies were generally approximately two percentage points above the respective benchmarks.

GSEM modelling

Four candidate GSEM models were evaluated for fit and parsimony (see Table 2; Fig. 8; Additional file 1: Figs. S2–S4). The optimal model, judged by AIC criteria, included an interaction term between the latent terms representing Candida colonization with each of the three Gram-positive bacteria colonization latent variables (Fig. 8). The size and statistical significance of this interaction term was similar in magnitude in each case. CRF predicted Candida colonization, as expected, but not bacterial colonization (Models 1–3; Additional file 1: Figs. S2–S4).

Table 2 Development of GSEM model
Full size table
Fig. 8
figure 8

The optimal GSEM (model 4) representing the interaction term between Candida colonization and colonization with each of three Gram-positive bacteria. Candida_col, S. aureus_col, CNS_col and Ent_col (ovals) are latent variables representing Candida, S. aureus, CNS and Enterococcal colonization, respectively. The variables in rectangles are either continuous variables (los) representing mean or median length of ICU stay or binary predictor variables representing the group level exposure to the following; a trauma ICU setting (trauma50), topical anti-septic based prevention method (a_S), antibiotic based prevention method (tap), antifungal prophlyaxis (af), a non-decontamination based prevention method (non-D), use of mechanical ventialtion more for than 90% of the group (mvp90), or exposure to PPAP (ppap). The circles contain error terms (ɛ). The three-part boxes represent the count data for Candida, and S aureus, CNS and Enterococci as VAP (v_can_n, v_S aureus_n) and bacteremia (b_can_n, b_S aureus_n, b_cns_n, b_Ent_n) isolates. These counts are logit transformed with the total number of patients in each group as the denominator using the logit link function in the generalized model of the GSEM. The precursor models (Models 1–3) generated in the development of the optimal model are shown in Additional file 1: (Figs. S2–S4)

Full size image

In the optimal model (model 4; Table 2; Fig. 8), the following exposures; TAP, trauma ICU admission, LOS and the interaction term with Candida colonization, displayed the strong associations with S aureus colonization. Exposure to TAP displayed positive associations with CNS colonization and Enterococcal colonization but a negative association with S aureus colonization (Table 2). The magnitude of these associations was similar in each case to that with the Candida colonization interaction term.

In all models, exposure to candidemia risk factors and TAP interventions displayed strong and consistent positive associations with Candida colonization and exposure to anti-septic, and antifungal interventions displayed strong and consistent negative associations with Candida colonization (Table 2).

Discussion

There is a range of preclinical study evidence that suggests that interactions between Candida with other bacteria in the patient microbiome has the potential to promote invasive bacterial infections. The basis for the interaction may be molecular [7] or mechanical [8].

However, there are multiple obstacles to defining the clinical relevance of any interactions between Candida and bacteria in the patient microbiome. Candida colonization has several risk factors [1, 2] some of which, such as prolonged antibiotic exposure, are due to broad microbiome changes. Candida and bacterial colonization are problematic to quantify in relation to relevant body site, timing in relation to ICU stay, and whether as defined by viable counts versus biological activity. Various BSI, whether with Gram-positive bacteremias or candidemia are uncommon. Data arising from single center clinical studies, and especially so infection data, will exhibit dependency and also, will unlikely be generalizable. Finally, for S aureus bacteremia, being a relatively rare end point with a benchmark incidence of approximately 1.8%, even large multi-center studies may be underpowered to show any interaction with Candida colonization [62]. Moreover, other Gram-positive bacteria such as Enterococci are even less common than S aureus bacteremia. Finally, CNS bacteremia is variably defined in the studies with or without using CDC defining criteria.

Presumably as a result of these obstacles, attempts to define the clinical relevance of any interaction between Candida with other bacteria are scant and relate mostly to interactions with Gram negative bacteria. Conflicting results emerged mostly from single center studies which generally have fewer than 400 patients under study [63,64,65,66,67,68,69].

There is some evidence that the risk of VAP in association with Pseudomonas aeruginosa is more common in patients with Candida colonization [63] and that antifungal treatments can reduce this likelihood [64]. One study found that Candida colonization of the respiratory tract is associated with Acinetobacter VAP but not Pseudomonas VAP [65].

In contrast, other attempts to define the clinical relevance of any interaction between Candida with other bacteria through either retrospective studies of the association with anti-fungal use or through studies of either pre-emptive or intensified prophylactic anti-fungal treatment [66,67,68] have failed to resolve the question. Several have questioned the specificity of the association and whether any association is simply a reflection of confounding by illness severity [1, 69].

The approach here circumvents these obstacles by using data from > 450 patient groups from > 250 studies of infection prevention interventions among ICU patients as comprising a natural experiment. The various groups of these studies have been exposed to infection prevention interventions which, in conjunction with other exposures, are known to, either specifically or non-specifically, modify the patient microbiome. Of note, any one group here could experience multiple concurrent exposures such as concomitant CRF, TAP, PPAP, anti-fungal and a prolonged ICU-LOS. This is reflected in the wide range in incidences of infections across the > 250 groups here.

For example, using an SEM approach, the interaction between Candida colonization emerges as a key driver of Pseudomonas invasive infections among 279 studies of infection prevention methods among ICU patients. Moreover, the relative impacts of specific anti-fungal agents on Pseudomonas bacteraemia can be estimated and compared [21].

SEM is an established modelling technique. It has emerging applications in epidemiology [70], ecology [71], and critical care [72, 73] research for modelling the relationships between multiple simultaneously observed variables and potential (latent) variables in order to provide quantitative tests of any theoretical model proposed within the literature. The validity and inferred relationship of conceptual variables that cannot be directly quantified are testable by using latent variables within the model. GSEM allows generalized linear response functions in addition to the linear response functions allowed by SEM.

Limitations

There are six key limitations to this analysis. Firstly, this analysis is a group level modelling of four latent variables being colonization with each of Candida, Staphylococcus aureus, CNS and Enterococci. These latent variables and the coefficients derived in the GSEM models are indicative and intended for internal reference only. They have no counterpart at the level of any one patient or study and cannot be directly measured. Specifically, colonization data within the studies was ignored in this analysis as the mere presence of colonizing Candida may not reflect the potential biological activity towards interactions with colonizing bacteria.

The GSEM analysis takes a structural rather than statistical approach to the question of any interactions between Candida and Gram-positive bacteria. The structural approach means that a limited number of conceptually key group level factors were entered mostly as simple binary variables into intentionally simplistic GSEM models. There was no ability nor purpose to adjust for the underlying patient level risk. The true relationships between exposures and outcomes will likely be center specific, complex, graded and with multiple expoure interactions. A conventional approach to estimating exposure effects requires analytic methods such as meta-analysis which, being based on an assumption of exchangability between control and intervention groups that randomized assignement of exposures provides, allows more precise effect size estimates for specific individual interventions under study. However, assumptions that outcomes in the control groups are not influenced by infection prevention interventions within an ICU setting are questionable [74]. Moreover, a randomized assignement of individual exposure to Candida colonization is neither an ethical nor a practical intervention outside of a natural experiement resulting from group level exposures to various ICU infection prevention interventions.

The second limitation is that there was considerable heterogeneity in the interventions, populations, and study designs among the studies here as the inclusion criteria for the various studies have been intentionally broadly specified. This breadth is both a strength, in that the breadth of the group exposures is the basis for the natural experiment here which serves as the basis for the research question central within the model. It is also a limitation, in that the associations for a group wide exposure may not equate to associations at the level of an individual patient exposure.

Thirdly, several assumptions have been made for studies that failed to report key exposure and outcome variables in the analysis. Missing LOS data and percent receiving MV have been imputed and there is missing infection count data which has not been imputed. The data is provided in sufficient detail in Additional file 1 to enable replication of the analysis.

Fourth, there are a large number of studies not included here because the required infection count data was not reported. However, the differences between control and intervention group mean infection incidences noted here in the scatter plots (Figs. 3, 4, 5, 6, 7, Additional file 1: Fig. S1) are similar in magnitude and direction to the summary effect sizes for each of the three broad categories of TAP, anti-septic and non-decontamination methods, against both overall VAP and against overall bacteremia which in turn are similar to prior published effect estimates sizes seen in systematic reviews of these interventions from which most of the studies examined here were derived [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41].

Fifth, the various regimens of TAP, anti-septic and anti-fungal intervention that have been used within the various studies have been considered as similar within each category. This is a deliberate simplification as some, for example the anti-fungal regimens, target different body sites. Also, the duration of application of each regimen varied among the studies. On the other hand, a strength of this analysis is that it attempts to unpack the separate associations between the infection incidences and exposure to the various SDD components (TAP, PPAP, anti-fungal).

Finally, the literature search has been opportunistic rather than systematic. By using existing systematic reviews as a starting point, the key interventions can be readily identified and classified. As a consequence, the included studies have been predominately undertaken within first world country ICU’s. It is uncertain how representative this is of the microbiome for elsewhere in the world. There is some evidence that the bacteria that cause VAP vary in different parts of the world [75,76,77].

Conclusion

GSEM modelling of interactions between colonization with Candida and three Gram-positive bacteria, each as latent variables provide support to the postulate that these interactions within the patient microbiome enhance the potential for invasive infections arising from colonizing bacteria. The magnitude of these interactions towards cause invasive infections may be similar in magnitude but contrary to that achieved with the infection prevention interventions. An interaction leading to enhanced invasive potential of Gram-positive bacteria might also account for the paradoxically high incidences among the groups of TAP studies.

Availability of data and materials

The datasets analysed during the current study are provided in Additional file 1.

References

  1. Terraneo S, Ferrer M, Martin-Loeches I, Esperatti M, Di Pasquale M, Giunta V, et al. Impact of Candida spp. isolation in the respiratory tract in patients with intensive care unit-acquired pneumonia. Clin Microbiol Infect. 2016;22(1):94-e1.

    Article  Google Scholar 

  2. Huang D, Qi M, Hu Y, Yu M, Liang Z. The impact of Candida spp airway colonization on clinical outcomes in patients with ventilator-associated pneumonia: a systematic review and meta-analysis. Am J Infect Control. 2019. https://doi.org/10.1016/j.ajic.2019.11.002.

    Article  PubMed  Google Scholar 

  3. Pendleton KM, Huffnagle GB, Dickson RP. The significance of Candida in the human respiratory tract: our evolving understanding. Pathog Dis. 2017;75:ftx029.

    Article  PubMed Central  CAS  Google Scholar 

  4. Harriott MM, Noverr MC. Candida albicans and Staphylococcus aureus form polymicrobial biofilms: effects on antimicrobial resistance. Antimicrob Agents Chemother. 2009;53:3914–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Carlson E. Effect of strain of Staphylococcus aureus on synergism with Candida albicans resulting in mouse mortality and morbidity. Infect Immun. 1983;42:285–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hajishengallis G, Lamont RJ. Dancing with the stars: how choreographed bacterial interactions dictate nososymbiocity and give rise to keystone pathogens, accessory pathogens, and pathobionts. Trends Microbiol. 2016;24(6):477–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Todd OA, Fidel PL, Harro JM, Hilliard JJ, Tkaczyk C, Sellman BR, et al. Candida albicans augments Staphylococcus aureus virulence by engaging the staphylococcal agr quorum sensing system. MBio. 2019;10(3):e00910-e919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Schlecht LM, Peters BM, Krom BP, Freiberg JA, Hänsch GM, Filler SG, et al. Systemic Staphylococcus aureus infection mediated by Candida albicans hyphal invasion of mucosal tissue. Microbiol. 2015;161(Pt 1):168.

    Article  CAS  Google Scholar 

  9. Kim SH, Yoon YK, Kim MJ, Sohn JW. Risk factors for and clinical implications of mixed Candida/bacterial bloodstream infections. Clin Microbiol Infect. 2013;19(1):62–8.

    Article  PubMed  Google Scholar 

  10. Paling FP, Wolkewitz M, Bode LG, Klouwenberg PK, Ong DS, Depuydt P, de Bus L, Sifakis F, Bonten MJ, Kluytmans JA. Staphylococcus aureus colonization at ICU admission as a risk factor for developing S. aureus ICU pneumonia. Clin Microbiol Infect. 2017;23(1):499.

    Article  Google Scholar 

  11. Carver S, Beatty JA, Troyer RM, Harris RL, Stutzman-Rodriguez K, Barrs VR, et al. Closing the gap on causal processes of infection risk from cross-sectional data: structural equation models to understand infection and co-infection. Parasit Vectors. 2015;8(1):658.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Hurley JC. Structural equation modelling the ‘control of gut overgrowth’ in the prevention of ICU acquired Gram-negative infection. Crit Care. 2020;24:189.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Rushton SP, Shirley MD, Sheridan EA, Lanyon CV, O’Donnell AG. The transmission of nosocomial pathogens in an intensive care unit: a space–time clustering and structural equation modelling approach. Epidemiol Infect. 2010;138(6):915–26.

    Article  CAS  PubMed  Google Scholar 

  14. Hurley JC. How to apply structural equation modelling to infectious diseases concepts. Clin Microbiol Infect. 2022. https://doi.org/10.1016/j.cmi.2022.05.028.

    Article  PubMed  Google Scholar 

  15. Hurley JC. Candida–acinetobacter–pseudomonas interaction modelled within 286 ICU infection prevention studies. J Fungi. 2020;6(4):252.

    Article  Google Scholar 

  16. Silvestri L, Miguel A, van Saene HK. Selective decontamination of the digestive tract: the mechanism of action is control of gut overgrowth. Intensive Care Med. 2012;38(11):1738–50.

    Article  PubMed  Google Scholar 

  17. Hurley JC. Incidence of coagulase-negative staphylococcal bacteremia among ICU patients: decontamination studies as a natural experiment. Eur J Clin Microbiol Infect Dis. 2019. https://doi.org/10.1007/s10096-019-03763-0.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Hurley JC. Studies of selective digestive decontamination as a natural experiment to evaluate topical antibiotic prophylaxis and cephalosporin use as population-level risk factors for enterococcal bacteraemia among ICU patients. J Antimicrob Chemother. 2019;74(10):3087–94.

    Article  CAS  PubMed  Google Scholar 

  19. Hurley JC. Impact of selective digestive decontamination on respiratory tract Candida among patients with suspected ventilator-associated pneumonia. A meta-analysis. Eur J Clin Microbiol Infect Dis. 2016;35(7):1121–35.

    Article  CAS  PubMed  Google Scholar 

  20. Hurley JC. ICU-acquired candidemia within selective digestive decontamination studies: a meta-analysis. Intensive Care Med. 2015;41(11):1877–85.

    Article  PubMed  Google Scholar 

  21. Hurley JC. Structural equation modelling the relationship between anti-fungal prophylaxis and Pseudomonas bacteremia in ICU patients. Intensive Care Med Exp. 2022;10(1):1–7.

    Article  Google Scholar 

  22. Toews I, George AT, Peter JV, Kirubakaran R, Fontes LES, Ezekiel JPB, Meerpohl JJ. Interventions for preventing upper gastrointestinal bleeding in people admitted to intensive care units. Cochrane Database Syst Rev. 2018. https://doi.org/10.1002/14651858.CD008687.pub2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lewis SR, Schofield-Robinson OJ, Alderson P, Smith AF. Enteral versus parenteral nutrition and enteral versus a combination of enteral and parenteral nutrition for adults in the intensive care unit. Cochrane Database Syst Rev. 2018. https://doi.org/10.1002/14651858.CD012276.pub2.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Padilla PF, Martínez G, Vernooij RW, Urrútia G, i Figuls MR, Cosp XB. Early enteral nutrition (within 48 hours) versus delayed enteral nutrition (after 48 hours) with or without supplemental parenteral nutrition in critically ill adults. Cochrane Database Syst Rev. 2019. https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD012340.pub2/pdf/full

  25. Alkhawaja S, Martin C, Butler RJ, Gwadry-Sridhar F. Post-pyloric versus gastric tube feeding for preventing pneumonia and improving nutritional outcomes in critically ill adults. Cochrane Database Syst Rev. 2015. https://doi.org/10.1002/14651858.CD008875.pub2.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Solà I, Benito S. Closed tracheal suction systems versus open tracheal suction systems for mechanically ventilated adult patients. Cochrane Database Syst Rev. 2007. CD004581. https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD004581.pub2/pdf/full

  27. Subirana M, Solà I, Benito S. Closed tracheal suction systems versus open tracheal suction systems for mechanically ventilated adult patients. Cochrane Database Syst Rev. 2007. CD004581.

    Google Scholar 

  28. Gillies D, Todd DA, Foster JP, Batuwitage BT. Heat and moisture exchangers versus heated humidifiers for mechanically ventilated adults and children. Cochrane Database Syst Rev. 2017. CD004711.

  29. Wang L, Li X, Yang Z, Tang X, Yuan Q, Deng L, Sun X. Semi-recumbent position versus supine position for the prevention of ventilator-associated pneumonia in adults requiring mechanical ventilation. Cochrane Database Syst Rev. 2016. CD009946.

  30. Tokmaji G, Vermeulen H, Müller MCA, Kwakman PHS, Schultz MJ, Zaat SAJ. Silver-coated endotracheal tubes for prevention of ventilator-associated pneumonia in critically ill patients. Cochrane Database Syst Rev. 2015. CD009201.

  31. Bo L, Li J, Tao T, Bai Y, Ye X, Hotchkiss RS, Kollef MH, Crooks NH, Deng X. Probiotics for preventing ventilator-associated pneumonia. Cochrane Database Syst Rev. 2014. CD009066.

  32. Hua F, Xie H, Worthington HV, Furness S, Zhang Q, Li C. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane Database Syst Rev. 2016. CD008367.

  33. Zhao T, Wu X, Zhang Q, Li C, Worthington HV, Hua F. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane Database Syst Rev. 2020. CD008367.

  34. Minozzi S, Pieri S, Brazzi L, Pecoraro V, Montrucchio G, D'Amico R. Topical antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving mechanical ventilation. Cochrane Database Syst Rev. 2021. CD000022.

  35. Cortegiani A, Russotto V, Maggiore A, Attanasio M, Naro AR, Raineri SM, Giarratano A. Antifungal agents for preventing fungal infections in non‐neutropenic critically ill patients. Cochrane Database Syst Rev. 2016. CD004920.

  36. Liberati A, D’Amico R, Pifferi S, Torri V, Brazzi L, Parmelli E. Antibiotic prophylaxis to reduce respiratory tract infections and mortality in adults receiving intensive care (Review). Cochrane Database Syst Rev. 2009. CD000022.

    Google Scholar 

  37. Pileggi C, Bianco A, Flotta D, Nobile CG, Pavia M. Prevention of ventilator-associated pneumonia, mortality and all intensive care unit acquired infections by topically applied antimicrobial or antiseptic agents: a meta-analysis of randomized controlled trials in intensive care units. Crit Care. 2011;15:R155.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Silvestri L, Van Saene HK, Casarin A, Berlot G, Gullo A. Impact of selective decontamination of the digestive tract on carriage and infection due to Gram-negative and Gram-positive bacteria: a systematic review of randomised controlled trials. Anaesth Intensive Care. 2008;36(3):324–38.

    Article  CAS  PubMed  Google Scholar 

  39. Hurley JC. Prophylaxis with enteral antibiotics in ventilated patients: selective decontamination or selective cross-infection? Antimicrob Agents Chemother. 1995;39:941–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Silvestri L, Van Saene HK, Milanese M, Gregori D, Gullo A. Selective decontamination of the digestive tract reduces bacterial bloodstream infection and mortality in critically ill patients. Systematic review of randomized, controlled trials. J Hosp Infect. 2007;65(3):187–203.

    Article  CAS  PubMed  Google Scholar 

  41. Silvestri L, Weir WI, Gregori D, Taylor N, Zandstra DF, van Saene JJ, van Saene HK. Impact of oral chlorhexidine on bloodstream infection in critically ill patients: systematic review and meta-analysis of randomized controlled trials. J Cardiothorac Vasc Anesthesia. 2017;31(6):2236–44.

    Article  CAS  Google Scholar 

  42. Labeau SO, Van de Vyver K, Brusselaers N, Vogelaers D, Blot SI. Prevention of ventilator-associated pneumonia with oral antiseptics: a systematic review and meta-analysis. Lancet Infect Dis. 2011;11:845–54.

    Article  CAS  PubMed  Google Scholar 

  43. Klompas M, Speck K, Howell MD, Greene LR, Berenholtz SM. Reappraisal of routine oral care with chlorhexidine gluconate for patients receiving mechanical ventilation: systematic review and meta-analysis. JAMA Intern Med. 2014;174(5):751–61.

    Article  CAS  PubMed  Google Scholar 

  44. Alhazzani W, Smith O, Muscedere J, Medd J, Cook D. Toothbrushing for critically ill mechanically ventilated patients: a systematic review and meta-analysis of randomized trials evaluating ventilator-associated pneumonia. Crit Care Med. 2013;41:646–55.

    Article  PubMed  Google Scholar 

  45. Messori A, Trippoli S, Vaiani M, Gorini M, Corrado A. Bleeding and pneumonia in intensive care patients given ranitidine and sucralfate for prevention of stress ulcer: meta-analysis of randomised controlled trials. BMJ. 2000;321:1103–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Huang J, Cao Y, Liao C, Wu L, Gao F. Effect of histamine-2-receptor antagonists versus sucralfate on stress ulcer prophylaxis in mechanically ventilated patients: a meta-analysis of 10 randomized controlled trials. Crit Care. 2010;14:R194.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Alhazzani W, Almasoud A, Jaeschke R, Lo BW, Sindi A, Altayyar S, Fox-Robichaud A. Small bowel feeding and risk of pneumonia in adult critically ill patients: a systematic review and meta-analysis of randomized trials. Crit Care. 2013;17:R127.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Agrafiotis M, Siempos II, Ntaidou TK, Falagas ME. Attributable mortality of ventilator-associated pneumonia: a meta-analysis. Int J Tuberc Lung Dis. 2011;15(9):1154–63.

    Article  CAS  PubMed  Google Scholar 

  49. Melsen WG, Rovers MM, Bonten MJM. Ventilator-associated pneumonia and mortality: a systematic review of observational studies. Crit Care Med. 2009;37:2709–18.

    PubMed  Google Scholar 

  50. Safdar N, Dezfulian C, Collard HR, Saint S. Clinical and economic consequences of ventilator-associated pneumonia: a systematic review. Crit Care Med. 2005;33:2184–93.

    Article  PubMed  Google Scholar 

  51. Han J, Liu Y. Effect of ventilator circuit changes on ventilator-associated pneumonia: a systematic review and meta-analysis. Respir Care. 2010;55:467–74.

    PubMed  Google Scholar 

  52. Siempos II, Vardakas KZ, Kopterides P, Falagas ME. Impact of passive humidification on clinical outcomes of mechanically ventilated patients: a meta-analysis of randomized controlled trials. Crit Care Med. 2007;35:2843–51.

    PubMed  Google Scholar 

  53. Muscedere J, Rewa O, McKechnie K, Jiang X, Laporta D, Heyland DK. Subglottic secretion drainage for the prevention of ventilator-associated pneumonia: a systematic review and meta-analysis. Crit Care Med. 2011;39:1985–91.

    Article  PubMed  Google Scholar 

  54. Delaney A, Gray H, Laupland KB, Zuege DJ. Kinetic bed therapy to prevent nosocomial pneumonia in mechanically ventilated patients: a systematic review and meta-analysis. Crit Care. 2006;10:R70.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Sud S, Friedrich JO, Taccone P, Polli F, Adhikari NK, Latini R, Gattinoni L. Prone ventilation reduces mortality in patients with acute respiratory failure and severe hypoxemia: systematic review and meta-analysis. Inten Care Med. 2010;36(4):585–99.

    Article  Google Scholar 

  56. Siempos II, Vardakas KZ, Falagas ME. Closed tracheal suction systems for prevention of ventilator-associated pneumonia. Br J Anaesth. 2008;100(3):299–306.

    Article  CAS  PubMed  Google Scholar 

  57. Playford EG, Webster AC, Sorrell TC, Craig JC. Antifungal agents for preventing fungal infections in non-neutropenic critically ill and surgical patients: systematic review and meta-analysis of randomized clinical trials. J Antimicrob Chemother. 2006;57(4):628–38.

    Article  CAS  PubMed  Google Scholar 

  58. van Till JO, van Ruler O, Lamme B, Weber RJ, Reitsma JB, Boermeester MA. Single-drug therapy or selective decontamination of the digestive tract as antifungal prophylaxis in critically ill patients: a systematic review. Crit Care. 2007;11(6):R126.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Goodman L. Snowball sampling. Ann Math Stat. 1961;32:148–70.

    Article  Google Scholar 

  60. Stata Corporation. Stata structural equation modelling reference manual, in Stata 17 documentation. College Station, TX, USA. 2021 https://www.stata.com/bookstore/structural-equation-modeling-reference-manual/. Accessed 16 Jun 2021.

  61. Huang SS, Septimus E, Kleinman K, Moody J, Hickok J, Avery TR, et al. Targeted versus universal decolonization to prevent ICU infection. N Engl J Med. 2013;368(24):2255–65.

    Article  CAS  PubMed  Google Scholar 

  62. Hurley JC. How the cluster randomized trial “works.” Clin Infect Dis. 2020;70:341–6.

    Article  PubMed  Google Scholar 

  63. Azoulay E, Timsit JF, Tafflet M, de Lassence A, Darmon M, Zahar JR, et al. Candida colonization of the respiratory tract and subsequent pseudomonas ventilator-associated pneumonia. Chest. 2006;129(1):110–7.

    Article  PubMed  Google Scholar 

  64. Nseir S, Jozefowicz E, Cavestri B, Sendid B, Di Pompeo C, Dewavrin F, et al. Impact of antifungal treatment on Candida–Pseudomonas interaction: a preliminary retrospective case–control study. Intensive Care Med. 2007;33(1):137–42.

    Article  PubMed  Google Scholar 

  65. Tan X, Zhu S, Yan D, Chen W, Chen R, Zou J, et al. Candida spp. airway colonization: a potential risk factor for Acinetobacter baumannii ventilator-associated pneumonia. Med Mycol. 2016;54(6):557–66.

    Article  PubMed  Google Scholar 

  66. Albert M, Williamson D, Muscedere J, Lauzier F, Rotstein C, Kanji S, et al. Candida in the respiratory tract secretions of critically ill patients and the impact of antifungal treatment: a randomized placebo controlled pilot trial (CANTREAT study). Intensive Care Med. 2014;40:1313–22.

    Article  CAS  PubMed  Google Scholar 

  67. Ong DS, Klouwenberg PM, Spitoni C, Bonten MJ, Cremer OL. Nebulised amphotericin B to eradicate Candida colonisation from the respiratory tract in critically ill patients receiving selective digestive decontamination: a cohort study. Crit Care. 2013;17(5):R233.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Lindau S, Nadermann M, Ackermann H, Bingold TM, Stephan C, Kempf VA, et al. Antifungal therapy in patients with pulmonary Candida spp. colonization may have no beneficial effects. J Intensive Care. 2015;3(1):31.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Timsit JF, Schwebel C, Styfalova L, Cornet M, Poirier P, Forrestier C, et al. Impact of bronchial colonization with Candida spp. on the risk of bacterial ventilator-associated pneumonia in the ICU: the FUNGIBACT prospective cohort study. Intensive Care Med. 2019;45(6):834–43.

    Article  PubMed  Google Scholar 

  70. VanderWeele TJ. Invited commentary: structural equation models and epidemiologic analysis. Am J Epidemiol. 2012;176(7):608–12.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Fan Y, Chen J, Shirkey G, John R, Wu SR, Park H, Shao C. Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecol Process. 2016;5(1):1–2.

    Article  Google Scholar 

  72. McPeake J, Iwashyna TJ, Henderson P, Leyland AH, Mackay D, Quasim T, Walters M, Harhay M, Shaw M. Long term outcomes following critical care hospital admission: a prospective cohort study of UK biobank participants. Lancet Reg Health-Europe. 2021;6: 100121.

    Article  Google Scholar 

  73. Bojan M, Duarte MC, Ermak N, Lopez-Lopez V, Mogenet A, Froissart M. Structural equation modelling exploration of the key pathophysiological processes involved in cardiac surgery-related acute kidney injury in infants. Crit Care. 2016;20(1):171.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Hurley JC. The perfidious effect of topical placebo: calibration of Staphylococcus aureus ventilator-associated pneumonia incidence within selective digestive decontamination studies versus the broader evidence base. Antimicrob Agents Chemother. 2013;57(9):4524–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hurley JC. World-wide variation in incidence of Acinetobacter associated ventilator associated pneumonia: a meta-regression. BMC Infect Dis. 2016;16(1):577.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Hurley JC. Worldwide variation in Pseudomonas associated ventilator associated pneumonia. A meta-regression. J Crit Care. 2019;51:88–93.

    Article  PubMed  Google Scholar 

  77. Hurley JC. World-wide variation in incidence of Staphylococcus aureus associated ventilator-associated pneumonia: a meta-regression. Microorganisms. 2018;6(1):18.

    Article  PubMed Central  Google Scholar 

Download references

Funding

This research has been supported by the Australian Government Department of Health and Ageing through the Rural Clinical Training and Support (RCTS) program. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Author information

Authors and Affiliations

  1. Department of Rural Health, Melbourne Medical School, University of Melbourne, Melbourne, VIC, Australia

    James C. Hurley

  2. Division of Internal Medicine, Ballarat Health Services, Ballarat, VIC, Australia

    James C. Hurley

Authors
  1. James C. Hurley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

As sole author, JH produced the design of the study, performed the statistical analysis and wrote the manuscript. JH is the guarantor of the paper. The author read and approved the final manuscript.

Corresponding author

Correspondence to James C. Hurley.

Ethics declarations

Ethics approval and consent to participate

Being an analysis of published work, ethics committee review of this study was not required.

Consent for publication

Not applicable.

Competing interests

The author declares that he has no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Table S1. Observational studies (Benchmark groups). Table S2. Groups of non-decontamination studies. Table S3. Groups of Anti-septic studies. Table S4. Groups of Antibiotic (= TAP ± PPAP) studies. Table S5. Groups from single anti-fungal (SAF) studies. Reference list for included studies [Ref S1 - S283]. Fig S1. Candida RT count data. Fig S2. GSEM model. Fig S3. GSEM model. Fig S4. GSEM model.

Rights and permissions

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://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hurley, J.C. Candida and the Gram-positive trio: testing the vibe in the ICU patient microbiome using structural equation modelling of literature derived data. Emerg Themes Epidemiol 19, 7 (2022). https://doi.org/10.1186/s12982-022-00116-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12982-022-00116-9

Keywords

  • Bacteremia
  • Staphylococcus aureus
  • Antibiotic prophylaxis
  • Study design
  • Intensive care
  • Mechanical ventilation
  • Selective digestive decontamination
  • Generalized structural equation model

Emerging Themes in Epidemiology

ISSN: 1742-7622