ABSTRACT
AIM: A medical emergency team (MET) stand-down decision is the decision to end a MET response and hand responsibility for the patient back to ward staff for ongoing management. Little research has explored this decision. This study aimed to obtain expert consensus on the essential elements required to make optimal MET call stand-down decisions and the communication required before MET departure. DESIGN: A Delphi design was utilised. METHODS: An expert panel of 10 members were recruited based on their expert knowledge and recent clinical MET responder experience in acute hospital settings. Participants were emailed a consent form and an electronic interactive PDF for each survey. Two rounds were conducted with no attrition between rounds. The CREDES guidance on conducting and reporting Delphi studies was used to report this study. RESULTS: Consensus by an expert panel of 10 MET responders generated essential elements of MET stand-down decisions. Essential elements comprised of two steps: (1) the stand-down decision that was influenced by both the patient situation and the ward/organisational context; and (2) the communication required before actioning stand-down. Communication after the decision required both verbal discussions and written documentation to hand over patient responsibility. Specific patient information, a management plan and an escalation plan were considered essential. CONCLUSION: The Delphi surveys reached consensus on the actions and communication required to stand down a MET call. Passing responsibility back to ward staff after a MET call requires both patient and ward safety assessments, and a clearly articulated patient plan for ward staff. Observation of MET call stand-down decision-making is required to validate the essential elements. IMPLICATION FOR THE PROFESSION AND PATIENT/OR PATIENT CARE: In specifying the essential elements, this study offers clinical and MET staff a process to support the handing over of clinical responsibility from the MET to the ward staff, and clarification of management plans in order to reduce repeat MET calls and improve patient outcomes. IMPACT: Minimal research has been focussed on the decision to hand responsibility back to ward staff so the MET may leave the ward with safety plan in place. This study provided expert consensus to optimise MET stand-down decision-making and the ultimate decision to end a MET call. Communication of agreed patient treatment and escalation plans is recommended before leaving the ward. This study can be used as a checklist for MET responder staff making these decisions and ward staff responsible for post-MET call care. The aim being to reduce the likelihood of potentially preventable repeat deterioration in the MET patient population. REPORTING METHOD: The CREDES guidance on conducting and reporting Delphi studies. PATIENT OR PUBLIC CONTRIBUTION: None.
ABSTRACT
Introduction: Silver (Ag) nanoparticles (NPs) are well documented for their broad-spectrum bactericidal effects. This study aimed to test the effect of bioactive Ag-hydrosol NPs on drug-resistant E. faecium 1449 strain and explore the use of artificial intelligence (AI) for automated detection of the bacteria. Methods: The formation of E. faecium 1449 biofilms in the absence and presence of Ag-hydrosol NPs at different concentrations ranging from 12.4 mg/L to 123 mg/L was evaluated using a 3-dimentional culture system. The biofilm reduction was evaluated using the confocal microscopy in addition to the Transmission Electronic Microscopy (TEM) visualization and spectrofluorimetric quantification using a Biotek Synergy Neo2 microplate reader. The cytotoxicity of the NPs was evaluated in human nasal epithelial cells using the MTT assay. The AI technique based on Fast Regional Convolutional Neural Network architecture was used for the automated detection of the bacteria. Results: Treatment with Ag-hydrosol NPs at concentrations ranging from 12.4 mg/L to 123 mg/L resulted in 78.09% to 95.20% of biofilm reduction. No statistically significant difference in biofilm reduction was found among different batches of Ag-hydrosol NPs. Quantitative concentration-response relationship analysis indicated that Ag-hydrosol NPs exhibited a relative high anti-biofilm activity and low cytotoxicity with an average EC50 and TC50 values of 0.0333 and 6.55 mg/L, respectively, yielding an average therapeutic index value of 197. The AI-assisted TEM image analysis allowed automated detection of E. faecium 1449 with 97% ~ 99% accuracy. Discussion: Conclusively, the bioactive Ag-hydrosol NP is a promising nanotherapeutic agent against drug-resistant pathogens. The AI-assisted TEM image analysis was developed with the potential to assess its treatment effect.
