Visual Blood, Visualisation of Blood Gas Analysis in Virtual Reality, Leads to More Correct Diagnoses: A Computer-Based, Multicentre, Simulation Study

Interpreting blood gas analysis results can be challenging for the clinician, especially in stressful situations under time pressure. To foster fast and correct interpretation of blood gas results, we developed Visual Blood. This computer-based, multicentre, noninferiority study compared Visual Blood and conventional arterial blood gas (ABG) printouts. We presented six scenarios to anaesthesiologists, once with Visual Blood and once with the conventional ABG printout. The primary outcome was ABG parameter perception. The secondary outcomes included correct clinical diagnoses, perceived diagnostic confidence, and perceived workload. To analyse the results, we used mixed models and matched odds ratios. Analysing 300 within-subject cases, we showed noninferiority of Visual Blood compared to ABG printouts concerning the rate of correctly perceived ABG parameters (rate ratio, 0.96; 95% CI, 0.92–1.00; p = 0.06). Additionally, the study revealed two times higher odds of making the correct clinical diagnosis using Visual Blood (OR, 2.16; 95% CI, 1.42–3.29; p < 0.001) than using ABG printouts. There was no or, respectively, weak evidence for a difference in diagnostic confidence (OR, 0.84; 95% CI, 0.58–1.21; p = 0.34) and perceived workload (Coefficient, 2.44; 95% CI, −0.09–4.98; p = 0.06). This study showed that participants did not perceive the ABG parameters better, but using Visual Blood resulted in more correct clinical diagnoses than using conventional ABG printouts. This suggests that Visual Blood allows for a higher level of situation awareness beyond individual parameters’ perception. However, the study also highlighted the limitations of today’s virtual reality headsets and Visual Blood

Model‐based control of mechanical ventilation: design and clinical validation

Background. We developed a model‐based control system using end‐tidal carbon dioxide fraction (FE′CO2) to adjust a ventilator during clinical anaesthesia. Methods. We studied 16 ASA I-II patients (mean age 38 (range 20-59) yr; weight 67 (54-87) kg) during i.v. anaesthesia for elective surgery. After periods of normal ventilation the patients were either hyper‐ or hypoventilated to assess precision and dynamic behaviour of the control system. These data were compared with a previous group where a fuzzy‐logic controller had been used. Responses to different clinical events (invalid carbon dioxide measurement, limb tourniquet release, tube cuff leak, exhaustion of carbon dioxide absorbent, simulation of pulmonary embolism) were also noted. Results. The model‐based controller correctly maintained the setpoint. No significant difference was found for the static performance between the two controllers. The dynamic response of the model‐based controller was more rapid (P<0.05). The mean rise time after a setpoint increase of 1 vol% was 313 (sd 90) s and 142 (17) s for fuzzy‐logic and model‐based control, respectively, and after a 1 vol% decrease was 355 (127) s and 177 (36) s, respectively. The new model‐based controller had a consistent response to clinical artefacts. Conclusion. A model‐based FE′CO2 controller can be used in a clinical setting. It reacts appropriately to artefacts, and has a better dynamic response to setpoint changes than a previously described fuzzy‐logic controller. Br J Anaesth 2004; 92: 800-

Interference by new-generation mobile phones on critical care medical equipment

GPRS, UMTS en medische apparatuur in het ziekenhui

Nomenclature for renal replacement therapy and blood purification techniques in critically ill patients: practical applications

This article reports the conclusions of the second part of a consensus expert conference on the nomenclature of renal replacement therapy (RRT) techniques currently utilized to manage acute kidney injury and other organ dysfunction syndromes in critically ill patients. A multidisciplinary approach was taken to achieve harmonization of definitions, components, techniques, and operations of the extracorporeal therapies. The article describes the RRT techniques in detail with the relevant technology, procedures, and phases of treatment and key aspects of volume management/fluid balance in critically ill patients. In addition, the article describes recent developments in other extracorporeal therapies, including therapeutic plasma exchange, multiple organ support therapy, liver support, lung support, and blood purification in sepsis. This is a consensus report on nomenclature harmonization in extracorporeal blood purification therapies, such as hemofiltration, plasma exchange, multiple organ support therapies, and blood purification in sepsis

Recertification and Reentry to Practice for Nurse Anesthetists: Determining Core Competencies and Evaluating Performance via High-Fidelity Simulation Technology

Introduction The National Board of Certification and Recertification for Nurse Anesthetistsaddressed a barrier to return to practice of uncertified practitioners by replacing required direct patient care experiences with high-fidelity simulation. Objectives The aims of this study were to: (a) validate a set of clinical activities for their relevance to reentry and determine if they could be replicated using simulation, (b) evaluate the content validity of an existing simulation scenario containing the proposed clinical activities and determine its substitutability for a clinical practicum, and (c) evaluate the validity of two methods to assess simulation performance. Methods A modified Delphi method incorporating an autonomous, anonymous, three-round online survey process using three unique expert certified registered nurse anesthetists groups was used to address each study aim. Results Twenty-seven clinical activities gained consensus as necessary to be assessed in the simulation. All 14 survey questions used to determine simulation content validity exceeded the minimum content validity index (CVI) value of 0.78, with a mean CVI of 0.99. The global rating scale CVI and the competency checklist CVI were 0.83 and 1.0, respectively. Conclusion The findings add to the existing literature supporting the utility of simulation for high-stakes provider assessment and certification