Clinical Utility of a Biomarker to Detect Contrast-Induced Acute Kidney Injury during Percutaneous Cardiovascular Procedures

<b><i>Introduction:</i></b> Contrast-induced acute kidney injury (CI-AKI) is a major clinical complication of percutaneous cardiovascular procedures requiring iodinated contrast. Despite its relative frequency, practicing physicians are unlikely to identify or treat this condition. <b><i>Methods:</i></b> In a 2-round clinical trial of simulated patients, we examined the clinical utility of a urine-based assay that measures liver-type fatty acid-binding protein (L-FABP), a novel marker of CI-AKI. We sought to determine if interventional cardiologists’ ability to diagnose and treat potential CI-AKI improved using the biomarker assay for 3 different patient types: pre-procedure, peri-procedure, and post-procedure patients. <b><i>Results:</i></b> 154 participating cardiologists were randomly divided into either control or intervention. At baseline, we found no difference in the demographics or how they identified and treated potential complications of AKI, with both groups providing less than half the necessary care to their patients (46.4% for control vs. 47.6% for intervention, <i>p</i> = 0.250). The introduction of L-FABP into patient care resulted in a statistically significant improvement of 4.6% (<i>p</i> = 0.001). Compared to controls, physicians receiving L-FABP results were 2.9 times more likely to correctly identify their patients’ risk for AKI (95% CI 2.1–4.0) and were more than twice as likely to treat for AKI by providing volume expansion and withholding nephrotoxic medications. We found the greatest clinical utility in the pre-procedure and peri-procedure settings but limited value in the post-procedure setting. <b><i>Conclusion:</i></b> This study suggests L-FABP as a clinical marker for assessing the risk of potential CI-AKI, has clinical utility, and can lead to more accurate diagnosis and treatment.

The Multi Split Ventilator System: Performance Testing of Respiratory Support Shared by Multiple Patients

Ventilator sharing has been proposed as a method of increasing ventilator capacity during instances of critical shortage. We sought to assess the ability of a regulated, shared ventilator system (Multi Split Ventilator System, MSVS) to individualize support to multiple simulated patients using one ventilator. We employed simulated patients of varying size, compliance, minute ventilation requirement, and PEEP requirement. Performance tests were performed to assess the ability of the QSVS, versus control, to achieve individualized respiratory goals to clinically disparate patients sharing a single ventilator following ARDSNet guidelines. Resilience tests measured the effects of simulated adverse events occurring to one patient on another patient sharing a single ventilator. The QSVS met individual oxygenation and ventilation requirements for multiple simulated patients with a tolerance similar to a single ventilator. Abrupt endotracheal tube occlusion or extubation occurring to one patient resulted in modest, clinically tolerable changes in ventilation parameters for the remaining patients. The QSVS is a regulated, shared ventilator system capable of individualizing ventilatory support to clinically dissimilar simulated patients. It is also resilient to common adverse events. The QSVS represents a feasible option to ventilate multiple patients during a severe ventilator shortage.

Target attainment of extended-interval dosing of tobramycin in patients less than five years of age with cystic fibrosis: A pharmacokinetic analysis.

Background: Extended interval dosing (EID) of tobramycin is recommended for treatment of pulmonary exacerbations in adults and older children with cystic fibrosis (CF), but data are limited in patients less than 5 years of age.Methods:We performed a retrospective population pharmacokinetic (PK) analysis of hospitalized children with CF <5 years of age prescribed intravenous tobramycin for a pulmonary exacerbation from March 2011 to September 2018 at our hospital. Children with normal renal function who had ≥1 tobramycin concentration available were included. Nonlinear mixed effects population PK modeling was performed using NONMEM® using data from the first 48 hours of tobramycin treatment. Monte Carlo simulations were implemented to determine the fraction of simulated patients that met published therapeutic targets with regimens of 10-15 mg/kg/day once daily dosing. Results:Fifty-eight patients received 111 tobramycin courses (range 1-9/patient). A 2-compartment model best described the data. Age, glomerular filtration rate, and vancomycin coadministration were significant covariates on tobramycin clearance. The typical values of clearance and central volume of distribution were 0.252 L/hr/kg^0.75 and 0.308 L/kg, respectively. No once daily regimens achieved all pre-specified targets simultaneously in >75% of simulated subjects. A dosage of 13 mg/kg/dose best met the predefined targets of Cmax >25 mg/L and AUC24 of 80-120 mg*h/L.Conclusions:Based on our population PK analysis and simulations, once daily dosing of tobramycin would not achieve all therapeutic goals in young patients with CF. However, extended-interval dosing regimens may attain therapeutic targets in the majority of young patients.

Petrha+

Role play and simulated patients are tools frequently used in undergraduate physiotherapy courses to help students gain familiarity with what they will find in future real-life encounters. However, these approaches have limitations when it comes to delivering diversity and repetition to a large number of students and are mostly bounded to the school's premises. Web-based virtual patient software can help to overcome these shortcomings as they equally require students to go through most of the steps of the physiotherapy process, and simultaneously offer unlimited diversity of cases and repetition opportunities and can be delocalized from physical schools. PETRHA + is an Erasmus+ strategic partnership of European high education institutions aiming at the improvement of a web-based serious game prototype designed to enhance physiotherapy students' clinical reasoning using virtual patients. The objective of this chapter is the presentation of the background context that led to the development of the serious game, its design features, functions, and ongoing and future developments.

Virtual OSCE: Experience and challenges with a large cohort of pharmacy students

In response to the inability to conduct conventional face-to-face objective structured clinical examination (OSCE) due to the COVID-19 lockdown, this study explored options to design virtual OSCE (vOSCE) that meets the objectives and standards of effective competency-based assessment for a large cohort of pharmacy students. The vOSCE required advanced planning of the actual assessment and technical conduct. The development of a master plan consisting of the types of competencies to test, topics and number of cases, and assessment rubrics, guided the team members to develop an adequate OSCE assessment module. Technical aspects included recruitment of examiners, simulated patients (SP), technical support, and a platform for vOSCE. The main challenges were to ensure well-ordered vOSCE and a stable internet connection for examiners, SP, and students. Google Meet was utilised due to its functionality, familiarity, and low internet consumption to all parties involved. Feedback was obtained from stakeholders to improve future OSCE conduct.

Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient’s consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims’ mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies’ longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.

Case Study: Rapid Training of Community Health Workers Using Enskill®

The COVID-19 pandemic caused many workers to lose their jobs, and also resulted in rapid surges in demand for workers with particular skills. In public health there was suddenly a huge demand for community health workers to conduct contact tracing, vaccination, and community outreach. To address this need, our team undertook the challenge of creating an online course that trains workers for community health work in half the time of typical training programs. It utilizes the Enskill® learning platform, which uses AI technology to create simulated scenarios in which trainees practice skills with avatars acting as simulated patients. Fifty-seven training participants without college degrees were recruited for the program from the Hampton Roads region, in collaboration with the Hampton Roads Workforce Council. The first cohorts of trainees were able to complete the training successfully in just eight weeks, and are now being placed in public health and healthcare positions. The approach also shows promise for upskilling existing employees to address skill gaps. The Enskill training program is a competitor in the XPRIZE Foundation’s Rapid Reskilling competition, to quickly reskill under-resourced workers for the digital revolution.