Andrew James Stewardson

Hospital-acquired complications (HACs) have recently been introduced in Australia as a quality-of-care metric. These use International Classification of Diseases (ICD) codes to identify potentially avoidable complications, including healthcare-associated infections. We sought to determine the positive predictive value (PPV) of the HAC algorithms in detecting hospital-acquired pneumonia (HAP) and healthcare-associated urinary tract infections (HA-UTI) using traditional surveillance as the standardised comparator. We conducted a retrospective analysis at Alfred Health. For both HAP and UTI, we selected 50 admitted episodes with ICD codes that satisfied the HAC definition. We applied standardised surveillance definitions to these episodes, based on European Centre for Disease Prevention and Control (ECDC) guidance. This involved a manual review of documentation and results by an Infection Prevention and Control Nurse Consultant. PPVs were calculated by comparing ICD-code diagnoses with surveillance-confirmed infections. The PPV for the HAP algorithm was 20% (95% CI: 11.0-33.2%). Among non-confirmed cases, 23% (9/40) had no radiological evidence of pneumonia, and 23% (9/40) had symptoms or consolidation on admission. Of the ten true HAP cases, 50% (5/10) were ventilator-associated. The PPV for the HA-UTI algorithm was 58% (95% CI: 44.2-70.6%), with 71% (15/21) of false positives due to asymptomatic bacteriuria. We found that current HAC algorithms based on ICD coding data did not reliably predict HAP and HA-UTI as defined by standardised surveillance. Given their use in healthcare funding, further validation and improvement of current algorithms should be explored.

The Hospital-acquired Pneumonia Prevention (HAPPEN) study is a multi-site trial evaluating the clinical effectiveness of improving oral care among adult patients to reduce the incidence of non-ventilator, hospital-acquired pneumonia (HAP). The HAPPEN study design is a stepped-wedge cluster randomised trial across three public hospitals in Australia. This document is the Statistical Analysis Plan for evaluating primary and secondary effectiveness outcomes. The trial was preregistered on the Australian and New Zealand Clinical Trials registry (ACTRN12624000187549) and is funded by the Medical Research Future Fund (MRF2022645). A copy of the study protocol and a signed version of the Statistical Analysis Plan are available on request from the corresponding author (BM).

Introduction: There are limited existing data regarding the financial costs associated with hospitalisation for injection‐related infection (IRI) in Australia. We aimed to estimate the cost, from the healthcare funder perspective, of admissions to treat IRI.Methods: We performed a retrospective audit of 226 admissions with an IRI at the Alfred Hospital (Melbourne, Australia), from January 2017 to April 2019. Eligibility criteria included admission for management of an IRI and current injecting drug use, defined as self‐reported injecting drug use within the preceding 6 months, documented in the medical record. We estimated the cost of each hospital admission, using national hospital price weights that accounted for the Australian Refined Diagnosis Related Groups and length of stay.Results: Among admissions with IRI, the median (interquartile range; IQR) cost of admissions with uncomplicated skin and soft tissue infection and complicated infection were AU$8416 (IQR $4132–$8611) and AU$31,938 (IQR $14,301–$54,397), respectively. Admissions requiring intensive care or surgery were associated with higher costs. The most expensive infection based on hospital cost was spinal infections (median AU$58,366 [IQR 31,938–$69,001]), despite endocarditis having higher median length of stay (38 days [IQR 18–46] vs. 24 [15–32]).Discussion and Conclusions: We demonstrate the costs associated with hospital admissions for IRI, including the substantial increased costs associated with complicated IRI. This highlights the opportunity for cost savings associated with harm minimisation interventions to avoid infections and their sequelae.

To reduce antimicrobial resistance (AMR), pathogen-specific AMR burden data are crucial to guide target selection for research and development of vaccines and monoclonal antibodies (mAbs). We identified knowledge gaps through previously conducted systematic reviews, which informed a Delphi expert consultation on future AMR research priorities and harmonisation strategies to support data-driven decision-making. Consensus (≥80% agreement) on importance and feasibility of research topics was achieved in two rounds, involving 24 of 39 and 19 of 24 invited experts, respectively. Priority pathogens and resistance profiles for future research were identified: third generation cephalosporin-resistant Klebsiella pneumoniae and Escherichia coli, for bloodstream and urinary tract infections, respectively, and meticillin-resistant Staphylococcus aureus for surgical-site infections. Prioritised high-risk populations included surgical, haemato-oncological and transplant patients. Mortality and resource use were prioritised as health-economic outcomes. The importance of age-stratified data and inclusion of a non-infected comparator group were highlighted. This agenda provides guidance for future research to fill knowledge gaps and support data-driven selection of target pathogens and populations for new preventive and treatment strategies, specifically vaccines and mAbs, to effectively address the AMR burden in Europe. These research priorities are also relevant to improve the evidence base for future AMR burden estimates.

We investigated whether injecting drug use was a risk factor for methicillin resistance among inpatients with Staphylococcus aureus bloodstream infections (SABSIs) at an Australian health service. In 273 inpatients, 46 (16.9%) of SABSIs were methicillin‐resistant S. aureus (MRSA). MRSA was more frequent in those who had injected drugs in the past 6 months (20.6%) compared with other inpatients (15.7%). Injecting drug use was associated with a 4.82‐fold (95% confidence interval = 1.54–16.29) increased odds of MRSA after accounting for confounders.

Objectives: Enterococcus faecium is a common cause of hospital-acquired infections and is associated with vancomycin resistance. The hospital environment is often a reservoir for E. faecium due to its ability to survive on surfaces. In 2019 we noted increasing prevalence of vanA E. faecium causing bloodstream infections. We aimed to assess the hospital environment as a reservoir of vanA E. faecium . Methods: We conducted a point prevalence survey of the environment at the Alfred Hospital, Melbourne, Australia. We sampled all wards (n=12), 10 surface types and 357 surfaces. Resulting E. faecium isolates underwent short-read whole genome sequencing and were compared to invasive infection (bloodstream) isolates. The van operon, multi-locus sequence type (MLST) and core genome MLST (cgMLST) were determined. Results: Twenty percent (73/357) of surfaces isolated E. faecium with a median of 20% positivity per ward (IQR 11.6-30.6%). vanA and vanB were detected in 12/73 (16%) and 58/73 (79%) environmental genomes, respectively. In contrast, a higher proportion of bloodstream isolates carried vanA (32/72 [44%], P vanA genomes, with the majority being clinical (22/26 genomes, 85%). Environmental-clinical links were observed in 292/895 (33%) putative genomic transmission links. In completed assemblies, the vanA and vanB operons were located on pRE25-like plasmids and the chromosome, respectively. Conclusion: We noted a 20% prevalence of E. faecium environmental colonisation but the vanA operon was detected in only 12/73 genomes. We identified distinct environmental and patient reservoirs, with most environmental genomes carrying the vanB operon and having distinct STs/cgMLST clusters. Environment-clinical reservoir spillover was detected but accounted for a minority of putative transmission links.