Generally accepted risk factors towards the acquisition of AGNB in the ICU include LOS > 7 days, exposure to invasive devices such as MV, and exposure to antibiotics together with acquisition by cross infection within the ICU environment [10]. A GSEM model founded on COGO concepts is used to evaluate these risk factors versus other group level exposures. This GSEM model enables the component groups of studies of the various infection prevention methods to be considered as a natural experiment with various group wide exposures among over two hundred ICU populations in the literature. This enables a novel perspective on the COGO concept that would not be possible within any one study examined in isolation nor within several studies examined collectively as within a systematic review [21].
The data used here to confront the COGO model is drawn mostly from studies located in systematic reviews. The extracted data is provided in sufficient detail in the ESM to enable replication of the analysis. In this regard, the summary effect sizes here for each of the three broad categories of TAP, anti-septic and non-decontamination methods, against both overall VAP and against overall bacteremia are similar to prior published estimates [1-10]. As has previously been noted, TAP (moreso when in combination with PPAP [22]) appears to be have the strongest prevention effect against both overall VAP and against overall bacteremia.
In confronting the COGO model with the Pseudomonas and Acinetobacter infection data, the COGO model is robust with several factors remaining consistent over the evolution through seven candidate versions of the GSEM. There are several expected observations. Length of stay and admission to a trauma ICU are strong positive factors and non-decontamination interventions appear not to mediate significant effects on either Pseudomonas GO or Acinetobacter GO. TAP exposure is associated with a negative coefficient towards Pseudomonas GO, albeit weaker than that associated with anti-septic interventions. These negative coefficients in association with TAP and anti-septic exposures towards Pseudomonas GO reflect the generally lower Pseudomonas VAP among the intervention groups of these studies.
On the other hand, the various components of the SOD/SDD regimens, TAP, EAP and PPAP have mixed effects within the GSEM models. Neither TAP nor EAP have negative coefficients towards Acinetobacter GO. This is surprising as in nearly all instances these contain polymyxin and or an aminoglycoside. Moreover, PPAP is associated with a strong positive correlation with Pseudomonas bacteremia.
Finally, patient groups exposed to the full SDD regimen (i.e. all of TAP, EAP and PPAP) have Pseudomonas and Acinetobacter bacteremia incidences that are either higher than or else not lower than patient groups receiving TAP alone. This is possibly not paradoxical as antibiotics used for PPAP typically lack activity against Pseudomonas and Acinetobacter. In this regard, the cumulative days of exposure to antibiotics without activity against Pseudomonas has been reported as being a risk factor for acquiring P aeruginosa and Acinetobacter in the ICU [23-25]. Moreover, concomitant systemic antibiotic therapy (CSAT) fails to prevent the acquisition of respiratory tract colonization with Gram negative bacteria [26] and more than triples the risk of subsequent infection among ICU patients receiving an enteral decolonization regimen with gentamicin against KPC-producing Klebsiella pneumonia [27] and CRE producing Acinetobacter [28].
The exact relationship between gut colonization, PPAP use and subsequent bacteremia remains controversial amid conflicting reports that PPAP use may or may not be important for some Gram negative bacteremias versus others [29-32]. In studying the relative prevention effects of SDD versus SOD each versus standard care in the prevention of gram-negative bacteremias (i.e. not limited to Pseudomonas bacteremia), the majority of bacteremias occur after 4 days in the ICU (the typical duration of PPAP) and indeed the daily risk peaks after day 30 [11, 30]. Moreover, among patients receiving SDD or SOD, Pseudomonas accounts for one third of GN bacteremia episodes with most episodes not preceded by enteral colonization.
Defining the separate effects of EAP, TAP and PPAP on the Acinetobacter and Pseudomonas bacteremia incidences is difficult as these exposures are confounded with each other among the multiple SDD/SOD regimens under investigation in the different studies. Also, the duration of application of the regimens varied among the studies. In this regard, a non-significant increase in hospital acquired infections post discharge from the ICU as great as 50% was noted in a small SDD sub-study [33].
In critical care research, SEM is emerging as a method to model the relationships among multiple simultaneously observed variables in order to provide a quantitative test of any theoretical model proposed within the literature [34]. The use of latent variables within the model enables the ability to test the validity of concepts that can only be indirectly quantified through their inferred relationship to observed variables [35]. GSEM allows generalized linear response functions in addition to the linear response functions allowed by SEM.
Limitations.
There are five key limitations to this analysis, the first being that this analysis is a group level modelling of two latent variables, Pseudomonas GO and Acinetobacter GO, within a GSEM founded on the COGO construct. These latent variables and the coefficients derived in the GSEM 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. There was no ability nor purpose to adjust for the underlying patient level risk. 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. In this regard, a strength of the analysis is that the heterogeneity among the studies here generally resembles that expected among ICU populations to which these interventions might be targeted.
The second limitation is that the analysis is inherently observational. Only a limited number of key group level factors were entered into the GSEM models. Moreover, the GSEM modelling is deliberately simplistic with exposures entered as only binary variables and without use of interaction terms. In reality, the relationships between expoures and outcomes will likely be complex and expoure interactions could have great importance.
Thirdly, the analysis is likely underpowered to examine the Acinetobacter infection data, being a relatively rare end point. Likewise, the incidence of resistant infections with Acinetobacter and Pseudomonas are of great interest. However, examination of the incidence of these resistant infections is difficult as these end points are generally uncommon or rare and have beed inconsistently reported among thes studies here.
Fourthly, only those studies for which Pseudomonas and Acinetobacter infection data were available were able to be included in this analysis. However, the effect of the interventions on overall VAP and bacteremia incidences (Figure S2-S7) resembles that in the broader literature.
Finally, it should be noted that the various interventions among the studies here targeted a range of sites which may or may not have included the oropharynx and gastrointestinal tract. In this regard, it is surprising that the TAP and EAP interventions, which most directly target the oropharynx and gastrointestinal tract had weaker effects than did anti-septic interventions several of which, such as chlorhexidine body washes, target other sites.
Can the paradoxical findings of the GSEM model be reconciled with the apparent superior summary prevention effects of TAP against VAP and bacteremia? TAP exposure and control group concurrency have associations with Pseudomonas GO that are each similar in size but contrary in direction to each other. In this regard, the incidences of both overall VAP and overall bacteremia among the concurrent control groups within studies of SOD/SDD are as much as ten percentage points higher than control groups within studies of equivalent ICU populations. This higher overall VAP incidence can partly be accounted for by incidences of VAP with specific bacteria such as Acinetobacter [36], Pseudomonas [37] and Staphlococcus aureus [38] being each 3 to 5 percentage points higher among CC (but not NCC) control groups and each up to 2 percentage points higher for intervention groups.
Likewise, the higher overall bacteremia incidence can partly be accounted for incidences of bacteremia with specific bacteria being on average each 1 to 4 percentage points higher among CC (but not NCC) control groups. Even among intervention groups, these bacteremia incidences may on average be up to 3 percentage points higher for Acinetobacter (Fig 2), Pseudomonas (Fig 2) [39],Staphlococcus aureus [40], Enterococci [41] and coagulase negative Staphlococci [42].
In each case, the increased incidence within control groups of CC design studies of topical antibiotics remains apparent in meta-regression models adjusting for other recognized associations. The influence of topical placebo use, concurrent colonization with Candida and other influences may also have influences in this process [43-45].
Hence, reconciling the findings of the GSEM model founded on COGO concepts on the one hand, with the apparent superior summary prevention effects of TAP against VAP and bacteremia, on the other, is possible by noting that the incidences of VAP and bacteremia are generally higher among CC (but not NCC) control groups of studies of TAP. These higher incidences within CC (but not NCC) control groups of studies of TAP remains to be explained.