Identifying the need for infection-related consultations in intensive care patients using machine learning models.

Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records.

Vancomycin-resistant enterococci (VRE) in hospital settings across European borders: a scoping review comparing the epidemiology in the Netherlands and Germany.

The rising prevalence of vancomycin-resistant enterococci (VRE) is a matter of concern in hospital settings across Europe without a distinct geographical pattern. In this scoping review, we compared the epidemiology of vancomycin-resistant Enterococcus spp. in hospitals in the Netherlands and Germany, between 1991 and 2022.

Better antimicrobial resistance data analysis and reporting in less time.

Objectives: Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting.

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer.

Although it has long been known that the immune cell composition has a strong prognostic and predictive value in colorectal cancer (CRC), scoring systems such as the immunoscore (IS) or quantification of intraepithelial lymphocytes are only slowly being adopted into clinical routine use and have their limitations. To address this we established and evaluated a multistain deep learning model (MSDLM) utilizing artificial intelligence (AI) to determine the AImmunoscore (AIS) in more than 1,000 patients with CRC. Our model had high prognostic capabilities and outperformed other clinical, molecular and immune cell-based parameters.

Exploration of oxygen-mediated disinfection of medical devices reveals a high sensitivity of Pseudomonas aeruginosa to elevated oxygen levels.

The microbiological safety of medical devices is of paramount importance for patients and manufacturers alike. However, during usage medical devices will inevitably become contaminated with microorganisms, including opportunistic pathogens. This is a particular problem if these devices come in contact with body sites that carry high bacterial loads, such as the oral cavity.