5 years of experience implementing a methicillin-resistant Staphylococcus aureus search and destroy policy at the largest university medical center in the Netherlands

OBJECTIVE: To evaluate the effectiveness of a rigorous search and destroy policy for controlling methicillin-resistant Staphylococcus aureus (MRSA) infection or colonization. DESIGN: Hospital-based observational follow-up study. SETTING: Erasmus University Medical Center Rotterdam, a 1,200-bed tertiary care center in Rotterdam, the Netherlands. METHODS: Outbreak control was accomplished by the use of active surveillance cultures for persons at risk, by the preemptive isolation of patients at risk, and by the strict isolation of known MRSA carriers and the eradication of MRSA carriage. For unexpected cases of MRSA colonization or infection, patients placed in strict isolation or contact isolation and healthcare workers (HCWs) were screened. We collected data from 2000-2004. RESULTS: During the 5-year study period, 51,907 MRSA screening culture

External validation of a prediction model for timely implementation of innovations in radiotherapy

Background and purpose: The aim of this study was to externally validate a model that predicts timely innovation implementation, which can support radiotherapy professionals to be more successful in innovation implementation. Materials and methods: A multivariate prediction model was built based on the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) criteria for a type 4 study (1). The previously built internally validated model had an AUC of 0.82, and was now validated using a completely new multicentre dataset. Innovation projects that took place between 2017–2019 were included in this study. Semi-structured interviews were performed to retrieve the prognostic variables of the previously built model. Projects were categorized according to the size of the project; the success of the project and the presence of pre-defined success factors were analysed. Results: Of the 80 included innovation projects (32.5% technological, 35% organisational and 32.5% treatment innovations), 55% were successfully implemented within the planned timeframe. Comparing the outcome predictions with the observed outcomes of all innovations resulted in an AUC of the external validation of the prediction model of 0.72 (0.60–0.84, 95% CI). Factors related to successful implementation included in the model are sufficient and competent employees, desirability and feasibility, clear goals and processes and the complexity of a project. Conclusion: For the first time, a prediction model focusing on the timely implementation of innovations has been successfully built and externally validated. This model can now be widely used to enable more successful innovation in radiotherapy

External validation of a prediction model for timely implementation of innovations in radiotherapy

Background and purpose: The aim of this study was to externally validate a model that predicts timely innovation implementation, which can support radiotherapy professionals to be more successful in innovation implementation. Materials and methods: A multivariate prediction model was built based on the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis) criteria for a type 4 study (1). The previously built internally validated model had an AUC of 0.82, and was now validated using a completely new multicentre dataset. Innovation projects that took place between 2017–2019 were included in this study. Semi-structured interviews were performed to retrieve the prognostic variables of the previously built model. Projects were categorized according to the size of the project; the success of the project and the presence of pre-defined success factors were analysed. Results: Of the 80 included innovation projects (32.5% technological, 35% organisational and 32.5% treatment innovations), 55% were successfully implemented within the planned timeframe. Comparing the outcome predictions with the observed outcomes of all innovations resulted in an AUC of the external validation of the prediction model of 0.72 (0.60–0.84, 95% CI). Factors related to successful implementation included in the model are sufficient and competent employees, desirability and feasibility, clear goals and processes and the complexity of a project. Conclusion: For the first time, a prediction model focusing on the timely implementation of innovations has been successfully built and externally validated. This model can now be widely used to enable more successful innovation in radiotherapy