Long-term adherence to a 5 day antibiotic course guideline for treatment of intensive care unit (ICU)-associated Gram-negative infections

Evaluating the potential for respiratory metagenomics to improve treatment of secondary infection and detection of nosocomial transmission on expanded COVID-19 intensive care units

BACKGROUND

Clinical metagenomics (CMg) has the potential to be translated from a research tool into routine service to improve antimicrobial treatment and infection control decisions. The SARS-CoV-2 pandemic provides added impetus to realise these benefits, given the increased risk of secondary infection and nosocomial transmission of multi-drug-resistant (MDR) pathogens linked with the expansion of critical care capacity.

METHODS

CMg using nanopore sequencing was evaluated in a proof-of-concept study on 43 respiratory samples from 34 intubated patients across seven intensive care units (ICUs) over a 9-week period during the first COVID-19 pandemic wave.

RESULTS

An 8-h CMg workflow was 92% sensitive (95% CI, 75-99%) and 82% specific (95% CI, 57-96%) for bacterial identification based on culture-positive and culture-negative samples, respectively. CMg sequencing reported the presence or absence of β-lactam-resistant genes carried by Enterobacterales that would modify the initial guideline-recommended antibiotics in every case. CMg was also 100% concordant with quantitative PCR for detecting Aspergillus fumigatus from 4 positive and 39 negative samples. Molecular typing using 24-h sequencing data identified an MDR-K. pneumoniae ST307 outbreak involving 4 patients and an MDR-C. striatum outbreak involving 14 patients across three ICUs.

CONCLUSION

CMg testing provides accurate pathogen detection and antibiotic resistance prediction in a same-day laboratory workflow, with assembled genomes available the next day for genomic surveillance. The provision of this technology in a service setting could fundamentally change the multi-disciplinary team approach to managing ICU infections. The potential to improve the initial targeted treatment and rapidly detect unsuspected outbreaks of MDR-pathogens justifies further expedited clinical assessment of CMg.

Intensive care unit (ICU)-acquired bacteraemia and ICU mortality and discharge: addressing time-varying confounding using appropriate methodology

BACKGROUND

Studies often ignore time-varying confounding or may use inappropriate methodology to adjust for time-varying confounding.

AIM

To estimate the effect of intensive care unit (ICU)-acquired bacteraemia on ICU mortality and discharge using appropriate methodology.

METHODS

Marginal structural models with inverse probability weighting were used to estimate the ICU mortality and discharge associated with ICU-acquired bacteraemia among patients who stayed more than two days at the general ICU of a London teaching hospital and remained bacteraemia-free during those first two days. For comparison, the same associations were evaluated with (i) a conventional Cox model, adjusting only for baseline confounders and (ii) a Cox model adjusting for baseline and time-varying confounders.

FINDINGS

Using the marginal structural model with inverse probability weighting, bacteraemia was associated with an increase in ICU mortality (cause-specific hazard ratio (CSHR): 1.29; 95% confidence interval (CI): 1.02-1.63) and a decrease in discharge (CSHR: 0.52; 95% CI: 0.45-0.60). By 60 days, among patients still in the ICU after two days and without prior bacteraemia, 8.0% of ICU deaths could be prevented by preventing all ICU-acquired bacteraemia cases. The conventional Cox model adjusting for time-varying confounders gave substantially different results [for ICU mortality, CSHR: 1.08 (95% CI: 0.88-1.32); for discharge, CSHR: 0.68 (95% CI: 0.60-0.77)].

CONCLUSION

In this study, even after adjusting for the timing of acquiring bacteraemia and time-varying confounding using inverse probability weighting for marginal structural models, ICU-acquired bacteraemia was associated with a decreased daily ICU discharge risk and an increased risk of ICU mortality.

Does appropriate empiric antibiotic therapy modify intensive care unit-acquired Enterobacteriaceae bacteraemia mortality and discharge?

BACKGROUND

Conflicting results have been found regarding outcomes of intensive care unit (ICU)-acquired Enterobacteriaceae bacteraemia and the potentially modifying effect of appropriate empiric antibiotic therapy.

AIM

To evaluate these associations while adjusting for potential time-varying confounding using methods from the causal inference literature.

METHODS

Patients who stayed more than two days in two general ICUs in England between 2002 and 2006 were included in this cohort study. Marginal structural models with inverse probability weighting were used to estimate the mortality and discharge associated with Enterobacteriaceae bacteraemia and the impact of appropriate empiric antibiotic therapy on these outcomes.

FINDINGS

Among 3411 ICU admissions, 195 (5.7%) ICU-acquired Enterobacteriaceae bacteraemia cases occurred. Enterobacteriaceae bacteraemia was associated with an increased daily risk of ICU death [cause-specific hazard ratio (HR): 1.48; 95% confidence interval (CI): 1.10-1.99] and a reduced daily risk of ICU discharge (HR: 0.66; 95% CI: 0.54-0.80). Appropriate empiric antibiotic therapy did not significantly modify ICU mortality (HR: 1.08; 95% CI: 0.59-1.97) or discharge (HR: 0.91; 95% CI: 0.63-1.32).

CONCLUSION

ICU-acquired Enterobacteriaceae bacteraemia was associated with an increased daily risk of ICU mortality. Furthermore, the daily discharge rate was also lower after acquiring infection, even when adjusting for time-varying confounding using appropriate methodology. No evidence was found for a beneficial modifying effect of appropriate empiric antibiotic therapy on ICU mortality and discharge.

Evolving classification of intensive care patients from event data

OBJECTIVE

This work aims at predicting the patient discharge outcome on each hospitalization day by introducing a new paradigm-evolving classification of event data streams. Most classification algorithms implicitly assume the values of all predictive features to be available at the time of making the prediction. This assumption does not necessarily hold in the evolving classification setting (such as intensive care patient monitoring), where we may be interested in classifying the monitored entities as early as possible, based on the attributes initially available to the classifier, and then keep refining our classification model at each time step (e.g., on daily basis) with the arrival of additional attributes.

MATERIALS AND METHODS

An oblivious read-once decision-tree algorithm, called information network (IN), is extended to deal with evolving classification. The new algorithm, named incremental information network (IIN), restricts the order of selected features by the temporal order of feature arrival. The IIN algorithm is compared to six other evolving classification approaches on an 8-year dataset of adult patients admitted to two Intensive Care Units (ICUs) in the United Kingdom.

RESULTS

Retrospective study of 3452 episodes of adult patients (≥16years of age) admitted to the ICUs of Guy's and St. Thomas' hospitals in London between 2002 and 2009. Random partition (66:34) into a development (training) set n=2287 and validation set n=1165. Episode-related time steps: Day 0-time of ICU admission, Day x-end of the x-th day at ICU. The most accurate decision-tree models, based on the area under curve (AUC): Day 0: IN (AUC=0.652), Day 1: IIN (AUC=0.660), Day 2: J48 decision-tree algorithm (AUC=0.678), Days 3-7: regenerative IN (AUC=0.717-0.772). Logistic regression AUC: 0.582 (Day 0)-0.827 (Day 7).

CONCLUSIONS

Our experimental results have not identified a single optimal approach for evolving classification of ICU episodes. On Days 0 and 1, the IIN algorithm has produced the simplest and the most accurate models, which incorporate the temporal order of feature arrival. However, starting with Day 2, regenerative approaches have reached better performance in terms of predictive accuracy.

Shortening the course of antibiotic treatment in the intensive care unit

Effective antimicrobial stewardship is an increasingly important concern for healthcare providers globally. Antibiotics are frequently prescribed for patients who develop sepsis in the intensive care unit and traditionally courses are prolonged, with uncertain benefit and probable harm. There is little evidence to support many guidelines recommending between 10 and 14 days, and a number of studies suggest substantially shorter courses of less than 7 days may suffice. Safely reducing course length is likely to depend on a number of preconditions, including thorough eradication of any septic foci; optimization of serum antibiotic concentrations, particularly when there is physiological derangement; and use of novel biomarkers such as procalcitonin. The critical care environment is well suited to this aim as patients are closely monitored. With these measures in place, it is reasonable to believe short antibiotic courses can safely be used for the majority of intensive care infections.

Healthcare-associated bloodstream infections in critically ill patients: descriptive cross-sectional database study evaluating concordance with clinical site isolates

Background

Healthcare-associated bloodstream infections are related to both increased antibiotic use and risk of adverse outcomes. An in-depth understanding of their epidemiology is essential to reduce occurrence and to improve outcomes by targeted prevention strategies. The objectives of the study were to determine the epidemiology, source and concordance of healthcare-associated bloodstream infections with clinical site isolates.

Methods

We conducted a descriptive cross-sectional study in critically ill adults admitted to a tertiary semi-closed intensive care unit in England to determine the epidemiology, source and concordance of healthcare-associated bloodstream infections with clinical site isolates. All nosocomial positive blood cultures over a 4-year study period were identified. Pathogens detected and concordances with clinical site are reported as proportions.

Results

Contaminant pathogens accounted for half of the isolates. The most common non-contaminant pathogens cultured were Pseudomonas spp. (8.0%), Enterococcus spp. (7.3%) and Escherichia coli (5.6%). Central venous catheter-linked bloodstream infections represent only 6.0% of the positive blood cultures. Excluding contaminants and central venous line infections, in only 39.5% of the bloodstream infections could a concordant clinical site source be identified, the respiratory and urinary tracts being the most common.

Conclusions

Clinical practice should focus on a) improving blood culture techniques to reduce detection of contaminant pathogens and b) ensuring paired clinical site cultures are performed alongside all blood cultures to better understand the epidemiology and potential implications of primary and secondary discordant health-care associated bloodstream infections.