There were three objectives here. Firstly, the study level evidence for various ICU infection prevention interventions toward preventing VAP and BSI derived here broadly recapitulates prior summary estimates among systematic reviews in the literature (Table S6) [9–17]. The effect sizes are indicative as each category includes broadly selected studies, with some having compound interventions, such as TAP combined with an anti-fungal, together with without PPAP given to either or both of control and intervention groups in antibiotic-based studies. The selection of studies in the summaries here is congruous with that in the literature summaries. Of note, the concurrent control groups within antibiotic-based intervention studies have unusually high incidences for several end-points that are unexplained in previous meta-regression models, taking into account several other group level variables not included here, such as year of publication, and likely represents a contextual effect [29–30].
The second objective is the development of GSEM models. The optimal model includes both the postulated interaction between Candida and Pseudomonas colonization and the contextual influences of TAP on concurrent control groups. In confronting the optimal GSEM model with published group level infection data, the anti-fungal agents, such as azoles and amphotericin, and anti-septic agents, each showed strong prevention effects versus Candida colonization. By contrast, TAP as a singular exposure versus Pseudomonas colonization and versus Candida colonization demonstrated effects that were weaker and variable in direction and significance.
Thirdly, in post GSEM model predictions, the estimated effects of singular exposure to topical amphotericin and anti-septic agents would each more than halve the incidence of candidemia and Pseudomonas bacteremia, although for absolute differences being approximately one percentage point or less. These differences would be challenging to detect. For example, a cluster-randomized trial demonstrating halving in Pseudomonas bacteremia incidence from 1% in the control group to 0.5% in the intervention group would need to enrol over 2,000 ICU’s each providing 500 patients per arm to provide 80% power.
By contrast, singular TAP exposure, with or without PPAP in combination, was either neutral or promoted bacteremia and candidemia incidence. Of note, the most common PPAP among SDD studies is cefotaxime, which is not generally active against Pseudomonas. That antibiotic exposure in the ICU environment paradoxically promotes Pseudomonas acquisition is a finding with precedent [31, 32]. Moreover, bacterial colonization rebounds following cessation of TAP with effects on the whole-of-ICU population [33]. Whether rebound contributes to colonization pressure, and the contextual effects of TAP exposure, needs to be further examined [34].
The observations here are paradoxical. On the one hand, antibiotic-based interventions show strong prevention effects against VAP that is most apparent for Pseudomonas VAP together with a halving in candidemia among studies of antibiotic-based interventions.
On the other hand, despite these two strong prevention effects of antibiotic-based interventions, there is insignificant prevention of Pseudomonas bacteremia. Moreover, the incidences of candidemia and bacteremia are generally higher among the concurrent control groups of antibiotic-based studies.
These observations, while paradoxical, potentially explain how ICU-acquired gram-negative bacteremia might occur seemingly without preceding colonization [35, 36]. They also could account for the bacteremia prevention effects observed in large studies of antibiotic-based interventions of SDD regimens containing topical polymyxin and tobramycin combined with amphotericin as the anti-fungal [20, 21], whereas the largest SDD study, where the same TAP regimen was combined with nystatin [22], failed to demonstrate any prevention effects against any bacteremia, or candidemia, end-points.
The SEM technique is an emerging method among critical care and infection pathogenesis research that enables group level modelling of multiple simultaneously observed variables [37, 38]. This technique enables the testing of concepts that infer relationships between observed variables mediated through latent variables. GSEM allows generalized linear response functions in addition to the linear response functions allowed by non-generalized SEM. A strength of GSEM modelling is the ability to incorporate observations from clusters with missing observations under the assumption of missing at random. This enables the inclusion of groups from studies either lacking control groups or providing data for only some end-points.