Sigh maneuver to enhance assessment of fluid responsiveness during pressure support ventilation.

BACKGROUND: Assessment of fluid responsiveness is problematic in intensive care unit (ICU) patients, in particular for those undergoing modes of partial support, such as pressure support ventilation (PSV). We propose a new test, based on application of a ventilator-generated sigh, to predict fluid responsiveness in ICU patients undergoing PSV. METHODS: This was a prospective bi-centric interventional study conducted in two general ICUs. In 40 critically ill patients with a stable ventilatory PSV pattern and requiring volume expansion (VE), we assessed the variations in arterial systolic pressure (SAP), pulse pressure (PP) and stroke volume index (SVI) consequent to random application of 4-s sighs at three different inspiratory pressures. A radial arterial signal was directed to the MOSTCARE™ pulse contour hemodynamic monitoring system for hemodynamic measurements. Data obtained during sigh tests were recorded beat by beat, while all the hemodynamic parameters were averaged over 30 s for the remaining period of the study protocol. VE consisted of 500 mL of crystalloids over 10 min. A patient was considered a responder if a VE-induced increase in cardiac index (CI) ≥ 15% was observed. RESULTS: The slopes for SAP, SVI and PP of were all significantly different between responders and non-responders (p < 0.0001, p = 0.0004 and p < 0.0001, respectively). The AUC of the slope of SAP (0.99; sensitivity 100.0% (79.4-100.0%) and specificity 95.8% (78.8-99.9%) was significantly greater than the AUC for PP (0.91) and SVI (0.83) (p = 0.04 and 0.009, respectively). The SAP slope best threshold value of the ROC curve was - 4.4° from baseline. The only parameter found to be independently associated with fluid responsiveness among those included in the logistic regression was the slope for SAP (p = 0.009; odds ratio 0.27 (95% confidence interval (CI95) 0.10-0.70)). The effects produced by the sigh at 35 cmH20 (Sigh35) are significantly different between responders and non-responders. For a 35% reduction in PP from baseline, the AUC was 0.91 (CI95 0.82-0.99), with sensitivity 75.0% and specificity 91.6%. CONCLUSIONS: In a selected ICU population undergoing PSV, analysis of the slope for SAP after the application of three successive sighs and the nadir of PP after Sigh35 reliably predict fluid responsiveness. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12615001232527 . Registered on 10 November 2015.

Efficacy of ventilator waveforms observation in detecting patient-ventilator asynchrony.

OBJECTIVES: The value of visual inspection of ventilator waveforms in detecting patient-ventilator asynchronies in the intensive care unit has never been systematically evaluated. This study aims to assess intensive care unit physicians' ability to identify patient-ventilator asynchronies through ventilator waveforms. DESIGN: Prospective observational study. SETTING: Intensive care unit of a University Hospital. PATIENTS: Twenty-four patients receiving mechanical ventilation for acute respiratory failure. INTERVENTION: Forty-three 5-min reports displaying flow-time and airway pressure-time tracings were evaluated by 10 expert and 10 nonexpert, i.e., residents, intensive care unit physicians. The asynchronies identified by experts and nonexperts were compared with those ascertained by three independent examiners who evaluated the same reports displaying, additionally, tracings of diaphragm electrical activity. MEASUREMENTS AND MAIN RESULTS: Data were examined according to both breath-by-breath analysis and overall report analysis. Sensitivity, specificity, and positive and negative predictive values were determined. Sensitivity and positive predictive value were very low with breath-by-breath analysis (22% and 32%, respectively) and fairly increased with report analysis (55% and 44%, respectively). Conversely, specificity and negative predictive value were high with breath-by-breath analysis (91% and 86%, respectively) and slightly lower with report analysis (76% and 82%, respectively). Sensitivity was significantly higher for experts than for nonexperts for breath-by-breath analysis (28% vs. 16%, p < .05), but not for report analysis (63% vs. 46%, p = .15). The prevalence of asynchronies increased at higher ventilator assistance and tidal volumes (p < .001 for both), whereas it decreased at higher respiratory rates and diaphragm electrical activity (p < .001 for both). At higher prevalence, sensitivity decreased significantly (p < .001). CONCLUSIONS: The ability of intensive care unit physicians to recognize patient-ventilator asynchronies was overall quite low and decreased at higher prevalence; expertise significantly increased sensitivity for breath-by-breath analysis, whereas it only produced a trend toward improvement for report analysis.

Virtual Laboratory and Imaging: an online simulation tool to enhance hospital disaster preparedness training experience.

OBJECTIVE: Hospitals play a pivotal role as basic healthcare providers during mass casualty incidents (MCIs). Radiological studies and emergency laboratory test are of high importance for the management of hospital patients. However, it is known that during these events, they can generate significant bottlenecks. Appropriate request of such tests is of utmost importance to not generate delays in the patient flow. The aim of this paper is to describe a software designed to increase the realism of hospital-based MCI training through a realistic reproduction of radiology and laboratory departments. METHODS: In this paper, we present a Virtual Laboratory and Imaging system that we designed with the goal of increasing the realism of full-scale mass casualty simulations. The system is able to dynamically manage the speed and load of virtual departments while collecting data on usage and load, and provide data useful for the after-event debriefing. We tested this system in two pilot simulations involving, respectively, 105 and 89 simulated casualties. RESULTS: The system, by measuring the number of requests and exams' turnaround time, enabled an objective measurement of the laboratory and radiology workload during simulated MCIs. It was possible to identify bottlenecks and consequently use these data for after-action debriefing. CONCLUSION: The tool not only increased the simulation realism by adding the radiology and laboratory departments but also provided valuable data that could be used for educational and organizational purposes.

Disaster medicine through Google Glass.

Nontechnical skills can make a difference in the management of disasters and mass casualty incidents and any tool helping providers in action might improve their ability to respond to such events. Google Glass, released by Google as a new personal communication device, could play a role in this field. We recently tested Google Glass during a full-scale exercise to perform visually guided augmented-reality Simple Triage and Rapid Treatment triage using a custom-made application and to identify casualties and collect georeferenced notes, photos, and videos to be incorporated into the debriefing. Despite some limitations (battery life and privacy concerns), Glass is a promising technology both for telemedicine applications and augmented-reality disaster response support to increase operators' performance, helping them to make better choices on the field; to optimize timings; and finally represents an excellent option to take professional education to a higher level.

Data collection in a live mass casualty incident simulation: automated RFID technology versus manually recorded system.

OBJECTIVES: To demonstrate the applicability and the reliability of a radio frequency identification (RFID) system to collect data during a live exercise. METHODS: A rooftop collapse of a crowded building was simulated. Fifty-three volunteers were trained to perform as smart victims, simulating clinical conditions, using dynamic data cards, and capturing delay times and triage codes. Every victim was also equipped with a RFID tag. RFID antenna was placed at the entrance of the advanced medical post (AMP) and emergency department (ED) and recorded casualties entering the hospital. RESULTS: A total of 12 victims entered AMP and 31 victims were directly transferred to the ED. 100% (12 of 12 and 31 of 31) of the time cards reported a manually written hospital admission time. No failures occurred in tag reading or data transfers. A correlation analysis was performed between the two methods plotting the paired RFID and manual times and resulted in a r=0.977 for the AMP and r=0.986 for the ED with a P value of less than 0.001. CONCLUSION: We confirmed the applicability of RFID system to the collection of time delays. Its use should be investigated in every aspect of data collection (triage, treatments) during a disaster exercise.