Lutz T. Zabel

The COVID-19 pandemic has starkly exposed queryPlease check author names and affiliation if presented correctly.vulnerabilities in the management of surveillance and testing. Significant challenges associated with physical tests, i.e., PCR and antigen tests, include their high cost, resource-intensive nature, turnaround time, and sensitivity. Although the literature has underscored the potential of Machine Learning-based methods for the digital diagnosis of COVID-19, developing high-performing models crucially depends on extensive datasets exceeding the amount available in one healthcare institution. Federated Machine Learning offers a solution to that dilemma. The aim of this research is to evaluate the potential impact of Federated Learning-based digital COVID-19 diagnosis on the trajectory of a pandemic. Therefore, we design a multidimensional evaluation framework, consisting of a simulation study utilizing real-world lab parameters from multiple hospitals and a newly developed performance indicator, named Testing Evaluation for Pandemics. We find that Federated Learning can significantly support the decision-making process of diagnosing COVID-19 at the beginning of a pandemic while saving scarce resources. However, a warm-up phase is needed until constant performance similar to physical tests is reached. In addition, lab parameters have a high prediction power for the diagnosis and are well suited because of patient welfare reasons.

For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approximately 4,000 patients and an extraordinary high ratio of SARS-CoV-2-positive patients. We analyze the influence of data preparation, flexibility in optimization targets, as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities, and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times, and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model.

Hospital outbreaks with vancomycin-resistant enterococci (VRE) pose a serious health threat and a challenge to infection prevention and control (IPC). We herein report on a VRE outbreak of unprecedented extent in Southern Germany (October 2015–November 2019). We used descriptive epidemiology and whole-genome sequencing (WGS) for a detailed outbreak investigation. Of the 2905 cases, 2776 (95.3%) were colonized, whereas from 127 (3.7%), VRE could be isolated from otherwise sterile body fluids or sites unlikely for enterococci colonization. Cases had a median age of 78 years (IQR 68–84) and 1339/2905 (46%) were female. The majority of isolates sequenced belonged to the clonal lineage ST80/CT1013 (212/397, 53%). Nosocomial transmission was observed as well as the constant import of VRE into the hospital. Extensive IPC measures were implemented and terminated the outbreak in late 2019, eventually. Our study shows that the combination of epidemiological and genomic analyses is indispensable for comprehensive outbreak investigations. The adaptation of IPC measures to these findings, their timely implementation, and strict execution also allow containment of large VRE outbreaks in hospital settings.

Accelerating rates of health care-associated infections caused by Clostridium difficile , with increasing recurrence and rising antibiotic resistance rates, have become a serious problem in recent years. This study was conducted to explore whether a combination of antibiotics with human antimicrobial peptides may lead to an increase in antibacterial activity. The in vitro activities of the antimicrobial peptides HBD1 to HBD3, HNP1, HD5, and LL-37 and the antibiotics tigecycline, moxifloxacin, piperacillin-tazobactam, and meropenem alone or in combination against 10 toxinogenic and 10 nontoxinogenic C. difficile strains were investigated. Bacterial viability was determined by flow cytometry and toxin production by enzyme-linked immunosorbent assay (ELISA). When combined at subinhibitory concentrations, antimicrobial peptides and antibiotics generally led to an additive killing effect against toxinogenic and nontoxinogenic C. difficile strains. However, LL-37 and HBD3 acted in synergism with all the antibiotics that were tested. Electron microscopy revealed membrane perturbation in bacterial cell walls by HBD3. In 3 out of 10 toxinogenic strains, HBD3, LL-37, piperacillin-tazobactam, and meropenem administration led to an increased toxin release which was not neutralized by the addition of HNP1. Antimicrobial peptides increase the bacterial killing of antibiotics against C. difficile regardless of the antibiotics' mode of action. Membrane perturbation in or pore formation on the bacterial cell wall may enhance the uptake of antibiotics and increase their antibacterial effect. Therefore, a combination of antibiotics with antimicrobial peptides may represent a promising novel approach to the treatment of C. difficile infections.