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

Epidemiological patterns of candidaemia: A comprehensive analysis over a decade

BACKGROUND

The prevalence of fungal bloodstream infections (BSI), especially candidaemia, has been increasing globally during the last decades. Fungal diagnosis is still challenging due to the slow growth of fungal microorganisms and need for special expertise. Fungal polymicrobial infections further complicate the diagnosis and extend the time required. Epidemiological data are vital to generate effective empirical treatment strategies.

OBJECTIVES

The overall aim of this project is to describe the epidemiology of monomicrobial candidaemia and polymicrobial BSI, both with mixed fungaemia and with mixed Candida/bacterial BSIs.

METHODS

We conducted a single-centre retrospective epidemiological study that encompasses 950,161 blood cultures during the years 2010 to 2020. The epidemiology of monomicrobial and polymicrobial candidaemia episodes were investigated from the electronic records.

RESULTS

We found that 1334 candidaemia episodes were identified belonging to 1144 individual patients during 2010 to 2020. Candida albicans was the most prevalent species detected in candidaemia patients, representing 57.7% of these episodes. Nakaseomyces (Candida) glabrata and Candida parapsilosis complex showed an increasing trend compared to previous studies, whereas Candida albicans demonstrated a decrease. 19.8% of these episodes were polymicrobial and 17% presented with mixed Candida/bacterial BSIs while 2.8% were mixed fungaemia. C. albicans and N. glabrata were the most common combination (51.4%) in mixed fungaemia episodes. Enterococcus and Lactobacillus spp. were the most common bacteria isolated in mixed Candida/bacterial BSIs.

CONCLUSIONS

Polymicrobial growth with candidaemia is common, mostly being mixed Candida/bacterial BSIs. C. albicans was detected in more than half of all the candidaemia patients however showed a decreasing trend in time, whereas an increase is noteworthy in C. parapsilosis complex and N. glabrata.

Rebound Inverts the Staphylococcus aureus Bacteremia Prevention Effect of Antibiotic Based Decontamination Interventions in ICU Cohorts with Prolonged Length of Stay

Could rebound explain the paradoxical lack of prevention effect against Staphylococcus aureus blood stream infections (BSIs) with antibiotic-based decontamination intervention (BDI) methods among studies of ICU patients within the literature? Two meta-regression models were applied, each versus the group mean length of stay (LOS). Firstly, the prevention effects against S. aureus BSI [and S. aureus VAP] among 136 studies of antibiotic-BDI versus other interventions were analyzed. Secondly, the S. aureus BSI [and S. aureus VAP] incidence in 268 control and intervention cohorts from studies of antibiotic-BDI versus that among 165 observational cohorts as a benchmark was modelled. In model one, the meta-regression line versus group mean LOS crossed the null, with the antibiotic-BDI prevention effect against S. aureus BSI at mean LOS day 7 (OR 0.45; 0.30 to 0.68) inverted at mean LOS day 20 (OR 1.7; 1.1 to 2.6). In model two, the meta-regression line versus group mean LOS crossed the benchmark line, and the predicted S. aureus BSI incidence for antibiotic-BDI groups was 0.47; 0.09-0.84 percentage points below versus 3.0; 0.12-5.9 above the benchmark in studies with 7 versus 20 days mean LOS, respectively. Rebound within the intervention groups attenuated and inverted the prevention effect of antibiotic-BDI against S. aureus VAP and BSI, respectively. This explains the paradoxical findings.

Candida-bacterial cross-kingdom interactions

While the fungus Candida albicans is a common colonizer of healthy humans, it is also responsible for mucosal infections and severe invasive disease. Understanding the mechanisms that allow C. albicans to exist as both a benign commensal and as an invasive pathogen have been the focus of numerous studies, and recent findings indicate an important role for cross-kingdom interactions on C. albicans biology. This review highlights how C. albicans-bacteria interactions influence healthy polymicrobial community structure, host immune responses, microbial pathogenesis, and how dysbiosis may lead to C. albicans infection. Finally, we discuss how cross-kingdom interactions represent an opportunity to identify new antivirulence compounds that target fungal infections.

Transcriptomics reveals shared immunosuppressive landscapes in ventilator-associated lower respiratory tract infections (VA-LRTI) patients

BACKGROUND

Ventilator-associated pneumonia (VAP) represents a transitory status of immunoparalysis, and we hypothesized that ventilator-associated tracheobronchitis (VAT) could share also some degree of immune response to a respiratory infection.

RESEARCH DESIGN AND METHODS

A prospective observational study in five medical ICUs to evaluate immunological alterations of patients with VA-LRTI. Immunological gene expression profiles in the blood using whole transcriptome microarrays in the first 24 hours following diagnosis. The area under the receiver operating characteristic curve (AUROC) was used to assess the accuracy of mRNA levels to differentiate VA-LRTI and lack of infection. A principal component analysis (PCA) was employed for analyzing the impact of each genetic expression footprint variable in explaining the variance of the cohort.

RESULTS

There was overlapping between the three classes of patients encompassing gene expression levels of 8 genes (i.e. HLA, IL2RA, CD40LG, ICOS, CCR7, CD1C, CD3E). HLA-DRA was equally low among VAT and VAP patients characterizing immune depression, and significantly lower than the control group.

CONCLUSIONS

Our findings suggest that VAP and VAT are not so different regarding gene expression levels suggesting a degree of immunosuppression. Our results indicate a state of immunoparalysis in respiratory infections in critically ill patients.

Establishing the safety of selective digestive decontamination within the ICU population: a bridge too far?

BACKGROUND

Infection prevention interventions within the intensive care unit (ICU) setting, whether studied within quality improvement projects or cluster randomized trials (CRT), are seen as low risk and grounded in an ethical imperative. Selective digestive decontamination (SDD) appears highly effective at preventing ICU infections within randomized concurrent control trials (RCCTs) prompting mega-CRTs with mortality as the primary endpoint.

FINDINGS

Surprisingly, the summary results of RCCTs versus CRTs differ strikingly, being respectively, a 15-percentage-point versus a zero-percentage-point ICU mortality difference between control versus SDD intervention groups. Multiple other discrepancies are equally puzzling and contrary to both prior expectations and the experience within population-based studies of infection prevention interventions using vaccines. Could spillover effects from SDD conflate the RCCT control group event rate differences and represent population harm? Evidence that SDD is fundamentally safe to concurrent non-recipients in ICU populations is absent. A postulated CRT to realize this, the SDD Herd Effects Estimation Trial (SHEET), would require > 100 ICUs to achieve sufficient statistical power to find a two-percentage-point mortality spillover effect. Moreover, as a potentially harmful population-based intervention, SHEET would pose novel and insurmountable ethical issues including who is the research subject; whether informed consent is required and from whom; whether there is equipoise; the benefit versus the risk; considerations of vulnerable groups; and who should be the gatekeeper?

CONCLUSION

The basis for the mortality difference between control and intervention groups of SDD studies remains unclear. Several paradoxical results are consistent with a spillover effect that would conflate the inference of benefit originating from RCCTs. Moreover, this spillover effect would constitute to herd peril.

Staphylococcus aureus hitchhiking from colonization to bacteremia via Candida within ICU infection prevention studies: a proof of concept modelling

Whether Candida within the patient microbiome drives the pathogenesis of Staphylococcus aureus bacteremia, described as microbial hitchhiking, cannot be directly studied. Group-level observations from studies of various decontamination and non-decontamination-based ICU infection prevention interventions and studies without study interventions (observational groups) collectively enable tests of this interaction within causal models. Candidate models of the propensity for Staphylococcus aureus bacteremia to arise with versus without various antibiotic, anti-septic, and antifungal exposures, each identified as singleton exposures, were tested using generalized structural equation modelling (GSEM) techniques with Candida and Staphylococcus aureus colonization appearing as latent variables within the models. Each model was tested by confrontation against blood and respiratory isolate data, obtained from 467 groups within 284 infection prevention studies. Introducing an interaction term between Candida colonization and Staphylococcus aureus colonization substantially improved GSEM model fit. Model-derived coefficients for singular exposure to anti-septic agents (- 1.28; 95% confidence interval; - 2.05 to - 0.5), amphotericin (- 1.49; - 2.3 to - 0.67), and topical antibiotic prophylaxis (TAP; + 0.93; + 0.15 to + 1.71) as direct effects versus Candida colonization were similar in magnitude but contrary in direction. By contrast, the coefficients for singleton exposure to TAP, as with anti-septic agents, versus Staphylococcus colonization were weaker or non-significant. Topical amphotericin would be predicted to halve both candidemia and Staphylococcus aureus bacteremia incidences versus literature derived benchmarks for absolute differences of < 1 percentage point. Using ICU infection prevention data, GSEM modelling validates the postulated interaction between Candida and Staphylococcus colonization facilitating bacteremia.

Structural equation modelling the impact of antimicrobials on the human microbiome. Colonization resistance versus colonization susceptibility as case studies

The impact of antimicrobials on the human microbiome and its relationship to human health are of great interest. How antimicrobial exposure might drive change within specific constituents of the microbiome to effect clinically relevant endpoints is difficult to study. Clinical investigation of each step within a network of causation would be challenging if done 'step-by-step'. An analytic tool of great potential to clinical microbiome research is structural equation modelling (SEM), which has a long history of applications to research questions arising within subject areas as diverse as psychology and econometrics. SEM enables postulated models based on a network of causation to be tested en bloc by confrontation with data derived from the literature. Case studies for the potential application of SEM techniques are colonization resistance (CR) and its counterpart, colonization susceptibility (CS), wherein specific microbes within the microbiome are postulated to either impede (CR) or facilitate (CS) invasive infection with pathogenic bacteria. These postulated networks have three causation steps: exposure to specific antimicrobials are key drivers, clinically relevant infection endpoints are the measurable observables and the activity of key microbiome constituents mediating CR or CS, which may be unobservable, appear as latent variables in the model. SEM methods have potential application towards evaluating the activity of specific antimicrobial agents within postulated networks of causation using clinically derived data.

How to apply structural equation modelling to infectious diseases concepts

BACKGROUND

Structural equation modelling (SEM) can address causation questions of great interest to infectious disease physicians and infection control practitioners that would elude techniques based on tests of association. Questions such as the size of intervention effects mediated on entities that cannot be easily measured, questions that cannot be studied in randomized controlled trials and question arising from 'big data'.

OBJECTIVES

To outline the computational and, moreover conceptual, differences between SEM methods versus the traditional tests of association.

SOURCES

Google scholar search for "structural equation modelling" and " Infection" CONTENT: Several examples of SEM applications to infectious diseases topics are used to illustrate. The SEM technique enables postulated causation models to be confronted with data. With this, the candidate models emerge as either 'importantly wrong', or potentially useful for enabling empiric predictions from the one identified as optimal.

IMPLICATIONS

Applications of SEM techniques and related modelling techniques to infectious diseases research will likely continue to emerge, especially so with the availability of 'big data'. ''Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad.'' [George Box; 1].