Dataset Information

Structural equation modelling the relationship between anti-fungal prophylaxis and Pseudomonas bacteremia in ICU patients.

ABSTRACT:

Purpose

Animal models implicate candida colonization facilitating invasive bacterial infections. The clinical relevance of this microbial interaction remains undefined and difficult to study directly. Observations from studies of anti-septic, antibiotic, anti-fungal, and non-decontamination-based interventions to prevent ICU acquired infection collectively serve as a natural experiment.

Methods

Three candidate generalized structural equation models (GSEM), with Candida and Pseudomonas colonization as latent variables, were confronted with blood culture and respiratory tract isolate data derived from 464 groups from 279 studies including studies of combined antibiotic and antifungal exposures within selective digestive decontamination (SDD) interventions.

Results

Introducing an interaction term between Candida colonization and Pseudomonas colonization substantially improved GSEM model fit. Model derived coefficients for singular exposure to anti-septic agents (- 1.23; - 2.1 to - 0.32), amphotericin (- 1.78; - 2.79 to - 0.78) and topical antibiotic prophylaxis (TAP; + 1.02; + 0.11 to + 1.93) versus Candida colonization were similar in magnitude but contrary in direction. By contrast, the model-derived coefficients for singular exposure to TAP, as with anti-septic agents, versus Pseudomonas colonization were weaker or non-significant. Singular exposure to amphotericin would be predicted to more than halve candidemia and Pseudomonas bacteremia incidences versus literature benchmarks for absolute differences of approximately one percentage point or less.

Conclusion

GSEM modelling of published data supports the postulated interaction between Candida and Pseudomonas colonization towards promoting bacteremia among ICU patients. This would be difficult to detect without GSEM modelling. The model indicates that anti-fungal agents have greater impact in preventing Pseudomonas bacteremia than TAP, which has no impact.

SUBMITTER: Hurley JC

PROVIDER: S-EPMC8776977 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Body condition is an important concept in behaviour, evolution and conservation, commonly used as a proxy of an individual's performance, for example in the assessment of environmental impacts. Although body condition potentially encompasses a wide range of health state dimensions (nutritional, immune or hormonal status), in practice most studies operationalize body condition using a single (univariate) measure, such as fat storage. One reason for excluding additional axes of variation may be that multivariate descriptors of body condition impose statistical and analytical challenges. Structural equation modelling (SEM) is used in many fields to study questions relating multidimensional concepts, and we here explain how SEM is a useful analytical tool to describe the multivariate nature of body condition. In this 'Research Methods Guide' paper, we show how SEM can be used to resolve different challenges in analysing the multivariate nature of body condition, such as (a) variable reduction and conceptualization, (b) specifying the relationship of condition to performance metrics, (c) comparing competing causal hypothesis and (d) including many pathways in a single model to avoid stepwise modelling approaches. We illustrated the use of SEM on a real-world case study and provided R-code of worked examples as a learning tool. We compared the predictive power of SEM with conventional statistical approaches that integrate multiple variables into one condition variable: multiple regression and principal component analyses. We show that model performance on our dataset is higher when using SEM and led to more accurate and precise estimates compared to conventional approaches. We encourage researchers to consider SEM as a flexible framework to describe the multivariate nature of body condition and thus understand how it affects biological processes, thereby improving the value of body condition proxies for predicting organismal performance. Finally, we highlight that it can be useful for other multidimensional ecological concepts as well, such as immunocompetence, oxidative stress and environmental conditions.

Project description:BACKGROUND:Conceptually, the "control of gut overgrowth" (COGO) is key in mediating prevention against infection with Gram-negative bacilli by topical antibiotic prophylaxis, a common constituent of selective digestive decontamination (SDD) regimens. However, the relative importance of the other SDD components, enteral and protocolized parenteral antibiotic prophylaxis, versus other methods of infection prevention and versus other contextual exposures cannot be resolved within individual studies. METHODS:Seven candidate generalized structural equation models founded on COGO concepts were confronted with Pseudomonas and Acinetobacter bacteremia as well as ventilator-associated pneumonia data derived from >?200 infection prevention studies. The following group-level exposures were included in the models: use and mode of antibiotic prophylaxis, anti-septic and non-decontamination methods of infection prevention; proportion receiving mechanical ventilation; trauma ICU; mean length of ICU stay; and concurrency versus non-concurrency of topical antibiotic prophylaxis study control groups. RESULTS:In modeling Pseudomonas and Acinetobacter gut overgrowth as latent variables, anti-septic interventions had the strongest negative effect against Pseudomonas gut overgrowth but no intervention was significantly negative against Acinetobacter gut overgrowth. Strikingly, protocolized parenteral antibiotic prophylaxis and concurrency each have positive effects in the model, enteral antibiotic prophylaxis is neutral, and Acinetobacter bacteremia incidences are high within topical antibiotic prophylaxis studies, moreso with protocolized parenteral antibiotic prophylaxis exposure. Paradoxically, topical antibiotic prophylaxis (moreso with protocolized parenteral antibiotic prophylaxis) appears to provide the strongest summary prevention effects against overall bacteremia and overall VAP. CONCLUSIONS:Structural equation modeling of published Gram-negative bacillus infection data enables a test of the COGO concept. Paradoxically, Acinetobacter and Pseudomonas bacteremia incidences are unusually high among studies of topical antibiotic prophylaxis.