A do it yourself (DIY) point-of-care wrist ultrasound phantom for joint access training.

BACKGROUND: Joint access is essential for arthrocentesis, or joint aspiration of fluids. Joint treatments that are not performed properly can result in avoidable patient issues such as damage to the muscles, tendons, and blood vessels surrounding the joint. The use of ultrasound has become the gold standard for this procedure and proven to be a support in the skill learning process. However, success with this equipment, particularly in small joints like the wrist, depends on a clinician's capacity to recognize the crucial landmarks that guide these procedures. Prior to executing on a real patient, task trainers have proven to be an effective way for doctors to practice and prepare for procedures. However, shortcomings of current solutions include high purchase costs, incompatibility with ultrasound imaging, and low reusability. In addition, since this is a procedure that is not performed frequently, there may not be space or resources available in healthcare facilities to accommodate one at the point of care. This study aimed to close the existing gap by developing a DIY ultrasound compatible task trainer for wrist joint access training. RESULTS: We developed a novel ultrasound compatible wrist joint model that can be made from sustainable materials and reusable parts, thus reducing the costs for acquisition and environmental impact. Our model, which was produced utilizing small-batch production methods, is made up of 3D-printed bones enclosed in an ultrasound-compatible gelatin mixture. It can be easily remade after each practice session, removing needle tracks that are visible under ultrasound for conventional phantoms. The ultrasonic properties of this model were tested through pixel brightness analysis and visual inspection of simulated anatomical structures. CONCLUSION: Our results report the advantages and limitations of the proposed model regarding production, practice, and ultrasound compatibility. While future work entails the transfer to patients of the same skill, this reusable and replicable model has proven, when presented to experts, to be successful in representing the physical characteristics and ultrasound profile of significant anatomical structures. This novel DIY product could be an effective alternative to teach procedures in the context of resource-restrained clinical simulation centers.

Critical Care Delivery in the Emergency Department: Bringing the Intensive Care Unit to the Patient.

Boarding of critically ill patients in the Emergency Department (ED) has increased over the past 20 years, leading hospital systems to explore ED-focused models of critical care delivery. ED-critical care delivery models vary between health systems due to differences in hospital resources and the needs of the critically ill patients boarding in the ED. Three published systems include an ED critical care intensivist consultation model, a hybrid model, and an ED-intensive care unit model. Paraphrasing the Greek philosopher, Plato, "necessity is the mother of invention." This proverb rings true as EDs are facing an increasing challenge of caring for boarding patients, especially those who are critically ill.

Bedside Diagnosis of Pulmonary Embolism Using Electrical Impedance Tomography: A Case Report.

Electrical impedance tomography (EIT) is an emerging imaging modality that can be used to diagnose ventilatory and intrathoracic perfusion mismatches in unstable patients at the bedside. We present a case of a postoperative hypoxic patient in the intensive care unit (ICU) who was too unstable for transport for computed tomography (CT) imaging but was diagnosed and treated for a pulmonary embolism using EIT at the bedside. After the patient clinically improved, CT imaging confirmed the pulmonary embolism diagnosis. EIT is a promising diagnostic tool that may have great utility in ICUs, where it can be safely applied at the bedside.

Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.

The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined a cohort of 575 aSAH patients from Emory Healthcare, identified via digital subtraction angiog- raphy. The outcome measure was the modified Ranking Scale (mRS) after 90 days. Predictions were performed with longitudinal clinical and imaging risk factors as inputs into a regularized Logistic Regression, a feedforward Neural Network and a multivariate time-series prediction model known as the long short-term memory (LSTM) architecture. Through extraction of higher-order risk factors, the LSTM model achieved an AUC of 0.89 eight days into hospitaliza tion, outperforming other techniques. Our preliminary findings indicate the proposed model has the potential to aid treatment decisions and effective imaging resource utilization in high-risk patients by providing actionable predictions prior to the development of neurological deterioration.