Focus topics for the ECFA study on Higgs / Top / EW factories

In order to stimulate new engagement and trigger some concrete studies in areas where further work would be beneficial towards fully understanding the physics potential of an $e^+e^-$ Higgs / Top / Electroweak factory, we propose to define a set of focus topics. The general reasoning and the proposed topics are described in this document.Comment: v3: fixed spelling of two author

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

Objectives: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. Methods: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. Results: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter “reduction in ICU length of stay.” Conclusions: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter “reduction in ICU length of stay” and potential spill-over effects

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective. METHODS: A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses. RESULTS: PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter "reduction in ICU length of stay." CONCLUSIONS: We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter "reduction in ICU length of stay" and potential spill-over effects

Vermamoeba vermiformis resides in water-based heater-cooler units and can enhance Mycobacterium chimaera survival after chlorine exposure.

Background Mycobacterium chimaera colonizes water-based heater–cooler units (HCUs), from which it can spread to patients during surgery. Vermamoeba vermiformis is a free-living waterborne amoeba, which was consistently present within HCUs. Aim To determine whether these amoebae can be involved in the persistent presence of M. chimaera. Methods An in-vitro disinfection model. Findings Increased survival of M. chimaera was observed after chlorine exposure in the presence of V. vermiformis. Confocal microscopy demonstrated the intracellular presence of M. chimaera in V. vermiformis. Conclusion In this way, V. vermiformis can contribute to the persistent presence of M. chimaera in HCUs. Cleaning and disinfection protocols should take this phenomenon into account

Withdrawal of a novel-design duodenoscope ends outbreak of a VIM-2-producing Pseudomonas aeruginosa

Background and study aims: Infections are a recognized risk of endoscopic retrograde cholangio-pancreatography (ERCP). This paper reports on a large outbreak of VIM-2-producing Pseudomonas aeruginosa that was linked to the use of a recently introduced duodenoscope with a specific modified design (Olympus TJF-Q180V). Methods: Epidemiological investigations and molecular typing were executed in order to identify the source of the outbreak. Audits on implementation of infection control measures were performed. Additional infection control strategies were implemented to prevent further transmission. The design and the ability to clean and disinfect the duodenoscope were evaluated, and the distal tip was dismantled. Results: From January to April 2012, 30 patients with a VIM-2-positive P. aeruginosa were identified, of whom 22 had undergone an ERCP using a specific duodenoscope, the TJF-Q180V. This was a significant increase compared with the hospital-wide baseline level of 2-3 cases per month. Clonal relatedness of the VIM-2 P. aeruginosa was confirmed for all 22 cases and for the VIM-2 strain isolated from the recess under the forceps elevator of the duodenoscope. An investigational study of the new modified design, including the dismantling of the duodenoscope tip, revealed that the fixed distal cap hampered cleaning and disinfection, and that the O-ring might not seal the forceps elevator axis sufficiently. The high monthly number of cases decreased below the pre-existing baseline level following withdrawal of the TJF-Q180V device from clinical use. Conclusions: Duodenoscope design modifications may compromise microbiological safety as illustrated by this outbreak. Extensive pre-marketing validation of the reprocessability of any new endoscope design and stringent post-marketing surveillance are therefore mandatory