Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study.

Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In this study, we aimed to develop a model that can reliably identify infants who do not have positive blood cultures (and, by extension, BSI) based on the full blood count (FBC) and C-reactive protein (CRP) values. Several models (i.e. multivariable logistic regression, linear discriminant analysis, K nearest neighbors, support vector machine, random forest model and decision tree) were trained using FBC and CRP values of 2693 infants aged 7 to 60 days with suspected BSI between 2005 and 2022 in a tertiary paediatric hospital in Dublin, Ireland. All models tested showed similar sensitivities (range 47% - 62%) and specificities (range 85%-95%). A trained decision tree and random forest model were applied to the full dataset and to a dataset containing infants with suspected BSI in 2023 and showed good segregation of a low-risk and high-risk group. Negative predictive values for these two models were high for the full dataset (> 99%) and for the 2023 dataset (> 97%), while positive predictive values were low in both dataset (4%-20%).   Conclusion: We identified several models that can predict positive blood cultures in infants with suspected BSI aged 7 to 60 days. Application of these models could prevent administration of antimicrobial treatment and burdensome diagnostics in infants who do not need them. What is Known: • Bloodstream infection (BSI) in infants cause non-specific symptoms and may be difficult to diagnose. • Results of blood cultures can take up to 48 hours. What is New: • Machine learning models can contribute to clinical decision making on BSI in infants while blood culture results are not yet known.

Outbreak of invasive Group A streptococcus disease in a nursing home in Ireland in February 2023 caused by emm type 18.

An out-of-season increase in cases of invasive Group A streptococcus (iGAS) was observed in Ireland between October 2022 and August 2023. We describe the management of an iGAS outbreak involving three nursing home residents in Ireland in early 2023. A regional Department of Public Health was notified of an iGAS case in a nursing home resident in January 2023. When two further cases among residents were notified 7 days later, an outbreak was declared. Surveillance for GAS/iGAS infection in residents and staff was undertaken. The site was visited to provide infection prevention and control (IPC) support. Isolates were emm typed. A total of 38 residents and 29 staff in contact with resident cases were provided with antibiotic chemoprophylaxis. Seven additional staff with no direct resident contact also received chemoprophylaxis after finding one probable localised GAS infection among them. No more iGAS cases subsequently occurred.Site visit recommendations included advice on terminal cleaning and cleaning of shared equipment, as well as strengthening staff education on hand hygiene and masking. All isolates were of emm subtype 18.12, a subtype not previously detected in Ireland. Key outbreak control measures were rapid delivery of IPC support and chemoprophylaxis. Emm18 is infrequently associated with GAS infections.

Where to start? The Irish Emergency Department Antimicrobial Discharge (EDAD) study: a multicentre, prospective cohort analysis.

Objectives: To determine the percentage of patients across Ireland who are discharged from the Emergency Department (ED) with an antimicrobial prescription, the indication, classification of infections, and guideline compliance. To identify potential areas for antimicrobial stewardship (AMS) interventions in the ED. Patients and methods: A multicentre, prospective cohort analysis study in EDs across eight hospitals in Ireland. At each site, patients aged 1 month and older who presented to the ED and were discharged directly from the ED were included. A random selection of records of patients discharged from the ED were reviewed until a minimum of 30 records with an infection diagnosis resulting in an antibiotic prescription were obtained per hospital. The number of patient discharges with no antibiotic prescriptions were included to calculate the denominator. The indication, infection classification and guideline compliance data were collected on the 30 prescriptions in the participating hospitals. Results: A total of 2619 patient records were reviewed. Of these, 249 (9.5%) patients were discharged with antimicrobial prescriptions from the ED. Most (158; 63%) were classified as probable bacterial infection, 21 (8%) as probable viral, and 18 (7%) had no documented evidence of infection. Three indications accounted for 73% of antimicrobial prescriptions: skin/soft tissue infection; ear, nose and throat infection; and urinary tract infection. Overall guideline compliance was 64%. Conclusions: Several areas for AMS interventions to optimize antimicrobial prescribing in the ED were identified, including targeted local and national guideline reviews, delayed prescribing, improved point-of-care testing and prescriber and patient education.