World-Wide Variation in Incidence of Staphylococcus aureus Associated Ventilator-Associated Pneumonia: A Meta-Regression

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.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12982-022-00116-9.

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.

Variation in Bacterial Respiratory Culture Results in Children With Neurologic Impairment

OBJECTIVES

To examine bacterial respiratory cultures in children with neurologic impairment (NI) (eg, cerebral palsy), both with and without tracheostomies, who were hospitalized with acute respiratory infections (ARIs) (eg, pneumonia) and to compare culture results across hospitals and age groups.

METHODS

This multicenter retrospective cohort study included ARI hospitalizations for children aged 1 to 18 years with NI between 2007 and 2012 who had a bacterial respiratory culture obtained within 2 days of admission. Data from 5 children's hospitals in the Pediatric Health Information System Plus database were used. Organisms consistent with oral flora and nonspeciated organisms were omitted from analysis. The prevalence of positive respiratory culture results and the prevalence of organisms identified were compared across hospitals and age groups and in subanalyses of children with and without tracheostomies by using generalized estimating equations to account for within-patient clustering.

RESULTS

Of 4900 hospitalizations, 693 from 485 children had bacterial respiratory cultures obtained. Of these, 54.5% had positive results, although this varied across hospitals (range 18.6%-83.2%; P < .001). Pseudomonas aeruginosa and Staphylococcus aureus were the most commonly identified organisms across hospitals and age groups and in patients with and without tracheostomies. Large variation in growth prevalence was identified across hospitals but not age groups.

CONCLUSIONS

The bacteriology of ARI in hospitalized children with NI differs from that of otherwise healthy children. Significant variation in prevalence of positive bacterial respiratory culture results and organism growth were observed across hospitals, which may be secondary to local environmental factors and microbiology reporting practices.

Effects of a national quality improvement program on ICUs in China: a controlled pre-post cohort study in 586 hospitals

INTRODUCTION

Patient safety and critical care quality remain a challenging issue in the ICU. However, the effects of the national quality improvement (QI) program remain unknown in China.

METHODS

A national ICU QI program was implemented in a controlled cohort of 586 hospitals from 2016 to 2018. The effects of the QI program on critical care quality were comprehensively investigated.

MAIN RESULTS

A total of 81,461,554 patients were enrolled in 586 hospitals, and 1,587,724 patients were admitted to the ICU over 3 years. In 2018, there was a significantly higher number of ICU beds (2016 vs. 2018: 10668 vs. 13,661, P = 0.0132) but a lower doctor-to-bed ratio (2016 vs. 2018: 0.64 (0.50, 0.83) vs. 0.60 (0.45, 0.75), P = 0.0016) and nurse-to-bed ratio (2016 vs. 2018: 2.00 (1.64, 2.50) vs. 2.00 (1.50, 2.40), P = 0.031) than in 2016. Continuous and significant improvements in the ventilator-associated pneumonia (VAP) incidence rate, microbiology detection rate before antibiotic use and deep vein thrombosis (DVT) prophylaxis rate were associated with the implementation of the QI program (VAP incidence rate (per 1000 ventilator-days), 2016 vs. 2017 vs. 2018: 11.06 (4.23, 22.70) vs. 10.20 (4.25, 23.94) vs. 8.05 (3.13, 17.37), P = 0.0002; microbiology detection rate before antibiotic use (%), 2016 vs. 2017 vs. 2018: 83.91 (49.75, 97.87) vs. 84.14 (60.46, 97.24) vs. 90.00 (69.62, 100), P < 0.0001; DVT prophylaxis rate, 2016 vs. 2017 vs. 2018: 74.19 (33.47, 96.16) vs. 71.70 (38.05, 96.28) vs. 83.27 (47.36, 97.77), P = 0.0093). Moreover, the 6-h SSC bundle compliance rates in 2018 were significantly higher than those in 2016 (6-h SSC bundle compliance rate, 2016 vs. 2018: 64.93 (33.55, 93.06) vs. 76.19 (46.88, 96.67)). A significant change trend was not found in the ICU mortality rate from 2016 to 2018 (ICU mortality rate (%), 2016 vs. 2017 vs. 2018: 8.49 (4.42, 14.82) vs. 8.95 (4.89, 15.70) vs. 9.05 (5.12, 15.80), P = 0.1075).

CONCLUSIONS

The relationship between medical human resources and ICU overexpansion was mismatched during the past 3 years. The implementation of a national QI program improved ICU performance but did not reduce ICU mortality.

Transitional Percentage of Minute Volume as a Novel Predictor of Weaning from Mechanical Ventilation in Patients with Chronic Respiratory Failure

PURPOSE

Some patients with respiratory failure fail initial weaning attempts and need prolonged mechanical ventilation (MV). Prolonged MV is associated with many complications and consumption of heathcare resources. Objective weaning indices help staffs to identify high-potential patients for weaning from the MV. Traditional weaning indices are not reliable in clinical practice. Transitional percentage of minute volume (TMV%) is a new index of the work of breathing. This study aimed to investigate the utility of TMV% in the prediction of weaning potential.

METHODS

This study was prospectively performed including all patients with prolonged MV. Researchers recorded their demographics, TMV%, respiratory parameters, Acute Physiology and Chronic Health Evaluation II score, and laboratory data upon arrival at the respiratory care center. The factors associated with successful weaning were analyzed.

RESULTS

Out of the 120 patients included, 84 (70.0%) were successfully weaned from MV. Traditional weaning indices such as rapid shallow breathing index could not predict the weaning outcome. TMV% was a valuable parameter as patients with a lower TMV%, higher tidal volume, higher hemoglobin, lower blood urea nitrogen, and lower Acute Physiology and Chronic Health Evaluation II scores had a higher rate of successful weaning. TMV%, tidal volume, and HCO3^- levels were independent predictors of successful weaning, and the area under the curve was .79 in the logistic regression model.

CONCLUSION

TMV% is a novel and effective predictor of successful weaning. Patients with lower TMV% had a higher MV weaning outcome. Once patients with a high potential for successful weaning are identified, they should be aggressively weaned from MV as soon as possible.

CLINICAL TRIALS GOVERNMENT IDENTIFIER

NCT033480.